PaPaGei: Open Foundation Models for Optical Physiological Signals
Authors: Arvind Pillai
Dimitris Spathis
Fahim Kawsar
Mohammad Malekzadeh
Venue: ICLR 2025 Poster (2025)
Keywords: self-supervised learning
foundation models
time series
Photoplethysmography (PPG)
health
physiology
Primary area: foundation or frontier models, including LLMs
TL;DR: PaPaGei is an open foundation model for PPG signals pre-trained on more than 57,000 hours of PPG signals, which outperforms other competitive time-series foundation models in at least 14 tasks.

Abstract: Photoplethysmography (PPG) is the leading non-invasive technique for monitoring biosignals and cardiovascular health, with widespread adoption in both clinical settings and consumer wearable devices. While machine learning models trained on PPG signals have shown promise, they tend to be task-specific and struggle with generalization. Current research is limited by the use of single-device datasets, insufficient exploration of out-of-domain generalization, and a lack of publicly available models, which hampers reproducibility. To address these limitations, we present PaPaGei, the first open foundation model for PPG signals. The model is pre-trained on over 57,000 hours of data, comprising 20 million unlabeled PPG segments from publicly available datasets. We introduce a novel representation learning approach that leverages domain knowledge of PPG signal morphology across individuals, enabling the capture of richer representations compared to traditional contrastive learning methods. We evaluate PaPaGei against state-of-the-art time-series foundation models and self-supervised learning benchmarks across 20 tasks from 10 diverse datasets, spanning cardiovascular health, sleep disorders, pregnancy monitoring, and wellbeing assessment. Our model demonstrates superior performance, improving classification and regression metrics by 6.3% and 2.9% respectively in at least 14 tasks. Notably, PaPaGei achieves these results while being more data- and parameter-efficient, outperforming models that are 70x larger. Beyond accuracy, we examine model robustness across different skin tones, establishing a benchmark for bias evaluation in future models. PaPaGei can serve as both a feature extractor and an encoder for multimodal models, opening up new opportunities for multimodal health monitoring. Models, data, and code are available at: https://github.com/nokia-bell-labs/papagei-foundation-model

Published as a conference paper at ICLR 2025
PAPAGEI: OPEN FOUNDATION MODELS FOR OPTICAL PHYSIOLOGICAL SIGNALS
Arvind Dimitris Spathis Fahim Kawsar Mohammad Malekzadeh*
1 Nokia Bell Labs, Cambridge, UK, Dartmouth College, NH, USA, 3 Universiiyy of Cambridge, UK *University of Glasgow, Scotland, UK
ABSTRACT
Photoplethysmography (PPG) is the leading non-invasive technique for monitor- ing biosignals and cardiovascular health, with widespread adoption in both clin- ical settings and consumer wearable devices. While machine learning models trained on PPG signals have shown promise, they tend to be task-specific and struggle with generalization. Current research is limited by the use of single- device datasets, insufficient exploration of out-of-domain generalization, and a lack of publicly available models, which hampers reproducibility. To address these limitations, we present PAPAGEI, the first open foundation model for PPG signals. The model is pre-trained on over 57,000 hours of data, comprising 20 million unlabeled PPG segments from publicly available datasets. We introduce a novel representation learning approach that leverages domain knowledge of PPG signal morphology across individuals, enabling the capture of richer representa- tions compared to traditional contrastive learning methods. We evaluate PAPAGEI against state-of-the-ar -frtte-art time-series foundation models and self-supervised learn- ing benchmarks across 20 tasks from 10 diverse datasets, spanning cardiovascular health, sleep disorders, pregnancy monitoring, and wellbeing assessment. Our model demonstrates superior performance, improving classification and regres- sion metrics by 6.3% and 2.9% respectively in at least tasks. Notably, PAPAGEI achieves these results while being more data- and -fffiieer-effcieen,, outperform- ing models that are 70x larger. Beyond accuracy, we examine model robustness across different skin tones, establishing benchmark for bias evaluation in future models. PAPAGEI can serve as both a feature extractor and an encoder for multi- modal models, opening up new opportunities for multimodal health monitoring'.
INTRODUCTION
Photoplethysmography (PPG), non-invasive optical sensing technique, is widely used to monitor cardiovascular health and physiological signals in both clinical and consumer health applications (Charlton et al., 2023). From hospital pulse oximeters health monitoring in various settings, bridging acute medical care and long-term health manage- ment. PPG signals help in tracking a diverse range of health indicators, including cardiovascular health, blood pressure, mood, and sleep disorders (Sadad et al., 2022; Ave et al., 2015; Reiss et al., 2019; Liang et al., 2018a; Haddad et al., 2021; Schrumpf al., 2021). Despite its widespread adop- tion, PPG poses substantial challenges for machine learning applications. A primary obstacle is the high cost of data annotation, which requires specialized domain expertise. This challenge is partic- ularly pronounced in consumer health applications, where varying sensing conditions and diverse user populations create additional complexity. PPG signals are susceptible to noise and motion arti- facts (Afandizadeh Zargari et al., 2023), well as inherent variability due to factors like skin tone and body composition (Bent et al., 2020). These complicate the development of generalizable ML models for PPG. Consequently, existing PPG datasets their generalizability, posing a major obstacle to the development robust and widely applicable models that could fully leverage the potential of PPG technology.
smartwatches, PPG enables continuous
often small, task-specific, and limited in
Work has been done during the author's internship at Nokia Bell Labs. Models, data, and code are available at: httpa///cmmmmmiiilallaa///annnna

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13.5K Users
Emotion Mood
Hcart
Foundation Modell
Pragnancy
Seep Disorders
PPG
20M Segments
57K Hours
Pre-training
Figure 1: PAPAGEI Overview. We curate public datasets of diverse PPG signals, and train founda- tion model leveraging a novel morphology -aware contrastive learning approach. To evaluate its ef- fectiveness, we apply the embeddings generated by PAPAGEI 20 tasks from 10 different datasets.
The PPG domain, unlike language or vision domains, lacks general-purpost foundation models (FMs), with most current works focused on single-dataset task-specific models. Although PPG can detect vital signs like heart rate variability and blood oxygen saturation, the absence of generalizable pre-trained models limits progress (Abbaspourazad et al., 2023). Despite the ongoing challenges of acquiring large-scale, high-quality data, recent expansions in diverse PPG datasets have created new opportunities (Johnson al., 2016; Zhang et al., 2018; Lee et al., 2022). To address these challenges, we introduce PAPAGEI, set of robust, pre-trained models capable of serving as backbone for various PPG-related tasks, capturing rich PPG representations through large-scale pre-training.
The key contributions of PAPAGEI are:
(1) Large-scale pre-training for PPG signals: To our knowledge, PAPAGE: is the first open foun- dation model pre-trained on PPG signals, using 57,00 37,000 hours of data from 20 million signals sourced entirely from public datasets. This establishes a new benchmark for large-scale model development in wearable and clinical health monitoring.
(2) PPG-aware sele-supe learning (SSL. framework: We introduce novel SSL framework with a unique PPG signal morphology augmentation module. Our approach optimizes agreement between PPG signals with similar blood volume changes while pointing the model pay attention to the changes around the systolic peak and dicrotic notch (key PPG markers).
(3) Comprehensive evaluation across diverse out-of-domain health tasks: We evaluate PAPAGEI across 20 tasks, including cardiovascular health, sleep disorders, pregnancy monitoring. and overall well-being. Our results show that the model embeddings contain rich and predictive information applicable to various health conditions, outperforming existing benchmarks.
(4) Extensive robustness studies: We conduct ablation studies to assess the impact of key compo- nents, including signal morphology augmentation, comparisons with established contrastive learning approaches, model size, data efficiency, and the effect of skin tone.
2 RELATED WORK
Self-supervised learning has become prominent paradigm for learning general representations from unlabeled datasets, with applications in physiological signal analysis including health, fitness, and brain signals (Tonekaboni et al., 2021; Zhang et al., 2022; Chen et al., 2021; Yèche et al., 2021; Spathis et al., 2021; Cheng al., 2020; Kiyasseh et al., 2021; Sarkar & Etemad, 2020). Despite its popularity, there are no widely used models for PPG signals pre-trained through SSL. Recently, Ab- baspourazad al. (2023) demonstrated that embeddings derived from PPG signals can predict over 45 diverse downstream health-related tasks using proprietary Apple Watch data. Their approach uses an SSL framework based on patient-level positive pair contrastive learning. Similarly, (Yun et al., 2024) showed that embedding PPG signals can improve genetic discovery and risk prediction outcomes using the UK Biobank dataset. Other works (Weng et al., 2024; Ding al., 2024; Zhou et al., 2024) explored PPG embeddings for various applications. However, these studies often used proprietary datasets, did not explore out-of-domain generalization, or did not release their models, highlighting the need for openly available, pre-trained PPG FMs (Table 17). For example, in contrast to (Abbaspourazad et al., 2023), our work exclusively uses public datasets for large-scale PPG train- ing and introduces novel SSL framework to incorporate PPG morphology. While Abbaspourazad et al. (2023) evaluate a single proprietary dataset, we validate on 10 diverse downstream datasets, showcasing greater generalizability and robustness across varied real-worls scenarios.
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IPA
gipa
hee-ystoic rest-systolic
Mi
Cipa
W
diestulic phese ares
systolic peak dicrotic
AUNI
SVRI
P
y sori
Soart
u
SQI
W
goqi
M2
Lsqi
Similar
Dissimilar
Cypa
Inflection Point Loss
Contrastive Loss
Signal Quality Loss
Figure 2: Overview of PAPAGEI-S. The process begins by computing three morphology metrics (IPA, SVRL, and SQI) for each PPG segment. The raw PPG signals are then processed through an encoder (E) to generate embeddings (H). These same embeddings feed into three specialized heads: a projection head (P) that contrasts PPG signals based on s sVRI values, and two mixture-of -x-peerr--- heads (M1 and M2) that refine the embeddings by predicting IPA and SQI values.
Generic time-series FMs, like Chronos (Ansari et al., 2024) and Moment (Goswami et al., 2024), lack physiological data representation. There is growing interest in modality-specific FMs tailored to physiological signals (Song et al., 2024; Lai et al., 2023) and human activity (Yuan et al., 2024a). Knowledge transfer from time-series FMs might benefit PPG tasks, but their performance is limited compared to PPG-specifie FMs. Adapting other domain-specte -ppecif models, like ECG (McKeen al., 2024; Song et al., 2024) or EEG (Yuan et al., 2024b), challenging due to distinct signal character- istics. We specifically design FMs for PPG signals, contributing the growing movement toward foundation models tailored to individual modalities. See Appendix SH for an extended discussion.
METHODS
Given dataset D = (p',p"..... .DS} representing diverse PPG signals from S participants, PPG signal Dd E R2 is defined as a time-series that captures variations in light intensity caused by arterial blood flow. To model granular changes in PPG signal of each subject
segment p* without overlap to obtain X* = {xj. ... }- Here, the number of segments N depends on the sampling frequency (f) and the desired length of time window. To train foundation models, PAPAGEI-P employs a patient contrastive SSL. approach that maximizes agreement between signals from the same subject. Importantly, we propose PAPAGEI-S, morphology-awar -aware self-supervised approach that maximizes agreement between PPG segments with similar morphology.
3.1 PARTICIPANT-AWAR
ORJECTIVE: PAPAGEI-P
In PAPAGEI-P, we train SSL model maximize agreement between the embeddings of PPG signals from the same subject. While previous studies have demonstrated the effectiveness of this strategy for physiological signals (Kiyasseh al., 2021; Abbaspourazad et al., 2023), our work represents the first attempt to train and evaluate foundation model using publicly available PPG datasets.
Training. We define a positive pair as any two distinct segments of PPG signals from the same subject, denoted as {(x;, xi]i j)- Next, we apply series of time-series augmentations such as random cropping, adding Gaussian noise, time flipping, negation, and magnitude scaling (Tang et al., 2020), each applied with a predefined probability during training. Each augmentation in- cludes hyper-parameters that control the intensity of the data transformation. During training, the augmented version of a randomly sampled positive pair (xf x;) is passed through the encoder E, and subsequently projection P, to obtain an embeddings pair denoted by (2,,2;) Given a batch of embeddings from N positive pairs of the form (z,, 2,), the model optimizes the normalized temperature-scaled cross entropy (NT-Xent) loss (Sohn, 2016; Oord al., 2018; Chen et al., 2020)
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given by: Cp = ((p((i.))++ where (p(1,)) - - Ex= log Eee 1[ exp(sim(z? 2²,
explaim(x, 2 2/1r
and sim(::)) -) is the cosine similarity. In contrast, vanilla SimCLR (Chen et al., 2020) would use pos- itive pairs augmented versions of randomly sampled PPG segments.
3.2 MORPHOLOGY-AWARE OBJECTIVE: PAPAGEI-S
In PAPAGEI-S, we leverage the PPG signal morphology to train SSL model that maximizes agree- ment between similar physiological features of PPG signals across participants.
PPG Morphology. Total peripheral resistance (TPR) the force exerted by the body's 's blood vessels on circulating blood varies under certain medical conditions, such as hypertension and diabetes (Trammel Sapra, 2020). Variations in TPR are reflected in PPG signals, presenting as distinct regions within the waveform. To capture these variations, we introduce a morphology augmentation module before training, which computes three key PPG metries (Figure 2, left): (1) ptress-induced Vascular Response Index (sVRI) (Lyu al., 2015: Zhang al., 2019): the ratio of mean PPG sig- nal between post- to pre-systolic phases, (2) Inflection Point Area ratio (IPA) (Wang et al., 2009): the ratio of systolic to diastolic areas defined by the dicrotic notch, and (3) Signal Quality Index (SQI): skewness of the signal as an indicator of quality (Flgendi, 2016). Prior studies have shown that incorporating the PPG signal quality during training yields positive results (Ding et al., 2024).
We selected these metrics for their complementary nature: sVRI captures variations in amplitude, while IPA measures signal width. To address scenarios where computing IPA is challenging because of noisy signals or different morphology, we incorporate SQL. In particular, we empirically find that 0.05) in signals with a dicrotic notch (Appendix $D.5).
SQI is significantly larger (p- <
sys Σ oye It sVI((x)= (n 888)
IPA(x)
So² dn S x dn
and SQI(x) 1 W
Σ
m3
(1)
2i=1a Ei D4
3/2
m2
where x E R is the PPG segment, sys is the systolic peak, n is the length of time series, and is the dicrotic notch. For SQI, we divide into 5 second windows (w; total windows W) and compute the skewness m, 6x E (xfl) (=(j))) which gives the best signal quality discrimination.
Training. Before training, the morphology augmentation module takes augmented input, by applying Gaussian noise and cropping to time series x, and outputs y = fy
bopeeree yfPa y E Ra
(Figure 2 middle). Next, we discretize yours into a predefined set of b 8 bins to denote pos- itive pairs, where your E {l...., b}. We define positive pairs based on the sVRI labels as {xx,, 1y? = yfuri, , # j}. Note that positive pairs are not defined based on participants.
cxp (sim Zi, 22) /T)
(i(j)) = log
(2)
Σ k=1
2N
1
+ i] cxp (sim(z; (z;, 2TT/
N
Leurs = 2N
[e(2k 1,2 + E(2k, 2k- - 1)]
(3)
k=1
1 N
N
Lipa
Vi
spa
atpa V/
Luqi
U/
898
(4)
N
N
i=1
Lg = o:heurs + (1 cx) (Lipa + ) where a E [0,
(5)
Given a batch of N PPG signals and their morphology, we optimize three heads using the encoder h. J. First, we extract the embeddings Z = (Z,.Zz..... Z,
(E) embeddings H = /h,,h..... h2. from the projection (P), and compute the contrastive loss for sVRI (equation 3). Next, we use the embeddings H to predict the IPA (gtie E R and SQI (gamr E R using the mixture of expert (MoE) heads Mi and M2. Each MoE head is composed of three fully connected neural networks (FCNNs), with the head's output calculated as a weighted sum of the FCNNs, using softmax to determine the weights. These heads are optimized using the mean absolute error (equation 4). The morphology indices encapsulate various PPG characteristics. Our rationale for utilizing MoE is that each expert can specialize in learning distinct properties that contribute to the overall index. Finally, the overall PAPAGRI-S training objective is given in equation 5.
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4 EXPERIMENTS
4.1 PRE-TRAINING
Datasets. We pre-train PAPAGEI on three datasets: (1) VitalDB (Lee et al., 2022), which includes PPG signals collected during surgery from the patient's finger (f=500Hz), (2) the MIMIC-III wave- form database matched subset (Johnson et al., 2016), where finger-tip PPG data is collected from an ICU monitor (f= 125Hz), and (3) the Multi-Ethnic Study of Atherosclerosis (MESA) sleep sub- study (Zhang et al., 2018; Chen et al., 2015), which provides PPG data obtained through finger-tip polysomnography (/= 256Hz). In total, we have 13.5K participants with 20M segments (Table 1).
Pre-processing. To curate single-channel PPG sig- nals across all datasets, we perform the following steps: (1) Apply a 4th-order Chebyshev bandpass fil- ter with low and high pass cut-offs set at 0.5Hz and 12Hz, respectively (Lapitan et al., 2024; Liang et al., 2018c); (2) Segment the signal into 10-second win- dows ((Orphanidou, 2018; Koteska et al., 2022) use 10s windows whereas larger studies use 30s (Ding et al., 2024) and 60s Abbaspourazad et al. (2023)); (3) Detect flatline segments and remove any seg-
Table 1: PAPAGEI'S pre-training datasets.
Dataset
#Participants
#Segments
Hours
VitalDB
5,866
6,248.100
17.355
MIMIC-III
5,596
7.196,401
19,990
MESA
2,055
7.306,705
20,296
Total
13,517
20,751,206
57,641
ment where more than 25% of the data is flat (BioBSS Documentation, 2023); (4) Nor- malize the segments using Z-score (Temko, 2017: Zhou et al., 2017); and (5) Resample the segments to 125Hz. (the lowest sampling rate of our pre-training datasets, MIMIC-III).
Implementation. We adopt ResNet-style CNN encoder, following (Ding et al., 2024).
Table 2: PAPAGEI's evaluation datasets. Gray lines unseen during training (out-of-domain). For those used for pre-training, we keep held-out test-sets and use labels. Task Types are: B=binary, R-regression, M-#classes= muticlass classification.
Abbaspourazad et al. (2023) also utilize an EfficientNet--tylee
CNN. Our model has 18 convo- lutional blocks, starting with filter size of 32, which doubles every 4 blocks. The projection layer is a single FC layer, gener- ating 512-d embedding. In the PAPAGEI-S variant, the expert block (M1 & M2) uses three parallel FCNNs, each with two FC layers, resulting in a 128- d embedding. For augmenta- tions, PAPAGEI-P uses cropping (0.50), negation (0.20). flipping (0.20), and scaling (0.40). PA- PAGEI-S uses cropping (0.25) and Gaussian noise (0.25). PA- PAGEI-S avoids augmentations that alter PPG's morphology.
HID
Datasel
Tusk (Tusk Type)
#Subj.
TI
VitaDBB (Lee et al., 2022)
ICU admission (B)
5866
T2
Operation Type (M-9)
5866
T3
MIMIC III (Moody et al., 2020)
Mortality (B)
5596
T4
MESA (Zhang et 2018)
Smoker
TS
AHI > 3% Oxygen Desat, (R)
2055
T6
AHI > 4% Oxygen Desat. (R)
2055
T7
DuMom28 (Facco et 2015)
Pregnancy stage (B)
3163 (5337)
T8
Gestation Age (K)
3163 (5337)
T9
(Skin Tone) (Toye, 2023)
Systolic (R)
231
T10
Dinstolic BP* (R)
231
TI
PPG-RP Linng al., 2018a)
Systolic RP (R)
219
T12
Diastolic BP (R)
219
T13
Average Heart Rate (R)
219
Hypertension
219
T15
SDB (Garde et 2014)
Sleep Diserdered Breathing (B)
146
ECSMP (Gao at. 2021)
Mood Disturbance (B)
T17
WESAD (Schmädt el al., 2018)
Valence (B)
(4497)
TI8
Arousal (B)
(4497)
T19
PPG-DaLiA (Reiss et al., 2019)
Heart Rate (R)
(64697)
T20
Activaty (M-9)
15 (611677)
We set 0.6 and train eight V100 GPUs for 15,000 steps (Ir= 10-4), with PAPAGEI-P and PAPAGEI-S having 5M and 5.7M parameters, respectively, while previous works use model sizes of 3.3M (Abbaspourazad et al., 2023) (we study scaling in Section 5.2).
4.2 DOWNSTREAM TASKS
To evaluate the effectivvness of PAPAGEI, we benchmark it against diverse set of datasets, tasks, and baselines, chosen for their large size and clinical relevance (where applicable) A description of the tasks with their corresponding #ID is provided in Table 2, with further details in Appendix $B. As a motivation, identifying patient risk factors is crucial for hospitals to allocate resources ef- fectively. To address this, we evaluate several indicators, including ICU admission (TI), type of operation (T2), mortality (T3), and smoking status (T4). For sleep apnea diagnosis, the American
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REGLE Pregnancy
Chronos
Moment
Stat. Features
Pregnancy
Pregnancy
Pregnancy
Smo
0.52
3.64 Vortallyy
0.64 Mortality
0.82
0.64 Wortalty
0.82
0.64 Nortality
Hypartension
Hypertension
Hyper tunsion
Hypartansion
0.8
0.8
0.5
0.8 ICU
0.7
Apnea
Apnca
Apnea
Agnea
0.6
96
3.50
Mood Valeece
Mood Va lence
Mozd Valence
Mood Valence
SimCLR
BYOL Smoker
TF-C
PaGei (Ours)
Pregnancy
Smoker
Pregnancy
Pregnincy
Smokar kar
Pregnancy" 0.82
Smakker
0.52
Nortaltty 6.7
0.52
Nortaiity 0.7
0.82
Nortaiity 0.7
Nortaiity 0.7
Hypertension
Hypartansion
Hypartonsion
Hypersension
38
0.78
78
07
07
07
Apnea
Apnea
Apnca
0.5 6 Grousal
Apnco
0.5 Frousal
Arousal
Grousal
Mood Valence
Mood Valence
Mead Valence
Mead Valence
REGLE Gestation
Chronos Sys. Gestation
Moment Gestation
Stat. Features Sys, Gestattonn
Sys.
8.0
8.0
Syz. 8.0
8.0
1
13.0
1.0
110
12.0
12,6
12,0
12.9
AHI 3%
AHI 35
AH 3%
AHI >
10.0
Sys,
2.0
Sys.
17.0 HR
Sys.
12.0 HR
Sye.
17.0
10.0 HR
22.000.0
20.0
10.0
Dib. BP
HR
Da. BP
Dia. BP Aug.
Dio. BP Auy. HR
Sim
BYOL
TF-C
Pa PaPaGel (Ours) Gestation
Gestation
Gestation
Gestation
Sys.
0.0
Sys.
Sys.
8.0
Sys. 0.0
0
13.0
30
Db.
12.0
12.0
Dis. 12.0
12.0
AHI 2%
AHI 35
AHI 3%
AHI
3%
18.0
180
18.8
Sys,
27.0
Sys.
17.0
Sys
17.0
Sys.
27.0
10.0
12.
10.0
30.0
10.0
Dtb. BP
HR
Dia BP Aug. HR
Db. BP
Dib. BP Aug. HR
Figure 3: Radar charts of downstream tasks. (Top) Classification performance in AUROC (larger area is better). (Bottom) Regression performance in MAE (smaller area is better). Pre-trained models in purple: REGLE, Chronos, & Moment. Statistical feature baseline in gray. SSL methods in green: SiMCLR, BYOL, & TF-C. PAPAGEI (ours), in pink. Details are in Tables 3 & 4.
Academy of Sleep Medicine recommends using the Apnea/Hyppopeea Index (AHI) with least 3% or 4% oxygen desaturation as a key metric (Ruehland et al., 2009). Thus, we predict AHI at 3% and 4% desaturation thresholds (T5 & T6) and classify dleep-disordere breathing (T15). For pregnancy outcomes, changes in gestational age and pregnancy stage are linked to risks like hypertensive dis- orders and mool--for-gestational 2023), enabling us to classify pregnancy stage (T7) and predict gestational age (T8). In cardiovascu- lar health, we estimate systolic (T9 & T11) and diastolic (T10& & T12) blood pressure (BP) using two datasets. While PPG-BP (T11 & T12) provides high-frequency, short PPG signals, the VV dataset helps explore skin tone's influence on BP estimation. We also assess hypertension classification (T14), average seated heart rate (T13), and continuous heart rate during activities (T19), along with activity classification (T20). In the emotion domain, we classify PPG signals into mood disturbance levels (T16), valence (T17), and arousal (T18).
delivery (Bouariu et al., 2022: Wu et al., 2020; Crump et al.,
4.3 BASELINES
We benchmark PAPAGEI'S performance against competitive baselines. As open-source founda- tion models designed for physiological signals, PAPAGEI is compared to recent time-series FMs: Chronos (Ansari et al., 2024) and MOMENT (Goswami et al., 2024). To evaluate the merits of our SSL framework, we also compare PAPAGEI with common SSL methods (trained from scratch) such as SimCLR (Chen et al., 2020), BYOL (Grill et al., 2020), and TF-C (Zhang et al., 2022). In addition, to assess model generalizability on PPG signals, we compare against REGLE, a model pre-trained on UK Biobank's 's PPG signals (Yun et al., 2024). As a simple baseline, we employ a ran- dom forest trained on statistical features extracted from the PPG signal, including mean, median, maximum, minimum, and the 25th, 50th, and 75th percentiles ("Stat, Features"). This task specifce approach serves as a benchmark for comparison with more advanced techniques.
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Table 3: Downstream comparison against pre-trained models. Feature extraction parameters are indicated next to each name. 95% CIs are reported in square brackets and the best value is bolded.
REGLE ma73)))
Chronos (HIM))
Moment (18555)
PAPAGEI-P (M))
PAPAGEL-S (1M)
Classification AUROC
2014
(Amari al,
ICU Admission
0.57 (252-662)
0.73 (2064120)
0.72 (0.704550)
0.73 (847-078)
0.79 (075-482)
Mortality
0.55 5355-299)
0.68 (365-1111
0.67 (0:554711)
0.67 (045-711
0.67
Smoker
0.54 5:47-4399
0.62 (1.574187)
0.62 (0.56AU55)
0.64 js (5.5--20))
0.61 (1.661166)
Pregnancy stage
0.64 (357-663)
0.81 (8.24083)
0,76 (8.741778)
0.74 (8.220770)
0.78 (0755800)
Hypertension
0.47 (:344188)
(18541111
0.75 ((
(a35-
0.77 (28641177
Sloep Disordered Breathing
0.45 (:30066)
0.58 (2.5-0.22)
0.45 (0:250055))
0.54 (8:330000)
0.70 (U57-0084)
Mood Disturbance
0.41 gi66
0.43 (..11-0660)
0.55 733
0.53 (8.27-
0.56 (0.35-077)
Valence
0.55 (5522-55)
0.56 (3.50-..59
0.57 (0.544455)
0.53 (8.514336)
0.56 (0.5440.59
Arousal
0,51 (0520.50)
0.57 (..6.0.00
0,56 (5.5...88)
0.58 (0.550060)
0.55 (0.50/055)
Average
0.52 0.06
0.62 + 0.10
0.63 0.09
0.63 + 0.08
0.67 0.09
Regression- MAE (1)
Apneu/Hyppppnes Index > 3%
14.06 (1005-15.11)
14,25 (13004-5.422
13.85 (1244-15-44)
12,97 (1185)-4011
Apnea/Hypopnea Index > 4%
12.64 [1 (1-17-224
11.57 (1091-12.77)
11880 (1079-37.99)
11.24 (971-12-37)
10.56 (9.99-1167)
Gestation Age
7.28 p.16.739) 39g
6.40 (e:11-999)
6.05 (391-6117]
Systolic BP (VV)
15.88 (1566-18.36)
17.24 (14.57/20113)
14.71 (12.66-7.29)
19.11 (1625-11229)
14.65 (12.83-6:28)
Dinstolic BP (VV)
8.65 p.16-10271
10.53 (o00-12.1
10.53 (MM0-12.11
10.87 (M:0-12000)
8,29
Systolic BP (PPG-BP)
16.32 (1889-199131
16.91 (1822-19.44
14,50 (1196-57.31)
13,60 (1055-16.31
14.39 (12.53:26458]
Diastolic BP (PPG-BP)
9.30 (0.7)
10.26 (5.13-12.57)
9.53 (8.28-1090)
8.88 17.35-10.00)
8.71 (2.18-0000)
Avemge HR HR
6.88 (5
(7:051000
4.41 (3.885448
3.47
764 531
4.00 56-44667
16.35 (1620-16691
9.65 (1.000.79
8.82 (3004856)
10.92
(1080-11.44)
11.53 (1.40-1108)
Average MAE (MAPE)
12,09 3,83 (15.299)
11.60 =: 3.60 (14209)
10.43 3.46 (13.29))
10.92 :: 4.25 (1409)
10.12 3.47 (13349)
4.4 LINEAR EVALUATION
We initially split the in- in-domain and out- out-of-domain datasets into training, validation, and test sets us- ing 80/10/10 and 60/20/20 ratios at the participant-level. respectively. Hyperparameter optimization is performed on the training set using nested cross-vaue validation. thus the validation and test sets are merged for evaluation. Models are evaluated by extracting feature representations from resampled data (125Hz) and applying linear probing for each task. For binary classification, we use logistic regression, measuring performance with AUROC. Regression tasks are evaluated using ridge regres- sion and mean absolute error (MAE), with aggregated results reported via symmetric mean absolute percentage error (sMAPE)). Multi-class classification tasks are trained using a random forest model, with accuracy the evaluation metric. To ensure robustness, compute 95% confidence intervals through bootstrapping (500 sampling runs with replacement.. Additional details in Appendix $A.
5 RESULTS
5.1 OVERALL PERFORMANCE
In general, from Figure 3, we observe that PAPAGEI is more accurate across many tasks indicated by the larger AUROC area and smaller MAE area. Table presents a more detailed comparison between PAPAGEI and other pre-trained models.
For classification tasks, PaPaGei-S achieves the highest average AUROC of 0.67, outperforming other models across several tasks, particularly in ICU Admission (0.79). Hypertension (0.77), and Sleep Disordered Breathing (0.70). In re- gression tasks, PaPaGei-S again demonstrates strong performance, achieving the lowest aver- age MAE (10. particularly in tasks related to Apnea/Hlypopnea Index and BP measurements. REGLE, small model trained on large PPG dataset, generally underperforms compared to other models, suggesting its compact size may limit learning complex patterns. Chronos obtains good performance in predicting mortality, preg- nancy stage, and smoking, likely due to their slower rate of change and reduced reliance on granular PPG-specific features. General-purpose models suffice for these high-level outcomes. However, tasks requiring finer PPG-specific granularity, such as heart rate prediction, blood pressure estima- tion, sleep apnea, benefit from PAPAGEI'S 's specialized feature extraction. Notably, PAPAGEI-S consistently outperforms PAPAGEI-P, highlighting the advantages of signal morphology objectives in enhancing predictive accuracy.
TFC
Moment
AUROC
0.66
0.64
0.02
10.5
0.50
9.5
25%
50%
75%
100%
100% Downstream labelled data
25%
50%
75%
Downstream labelled data
Figure Downstream data-efficiency analysis. Results are averaged over all binary classification (left) and regression tasks (right). PAPAGEI-S performs better with increased label availability.
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Table Downstream comparison against baseline and SSL methods. Feature extraction param- eters are indicated next to each name. 95% CIs are reported in square brackets and the best value is bolded. Implementation details are in Appendix $A.
Stat. Peatures
SimCLR
BYOL
TF-C
PAPAGEL-P
PAPAGEL-S
Classification- AUROC
ICU Admission
0.31
0.75
0.78
0.71 maase
0.75
0.79
Mostalaty
0167
0.67
0.67
Smoker
Pregenncy stage
0.74
0.78
Hepertension
(ussea))
(164434)
0.58 (uss4.0
0.77 (B:54:9)
SDB
jusees))
0.61 (aseess
(033660)
0.70 (5:5-
Mood Disturbance
0.32 (124655)
(1314001)
(0374:30)
0.50 (0:3-677)
Valence
(voxess)
0.57 pussi
0.53 (995899))
0. (.56
Arvisal
0.55 (us240))
0.55
(c5425))
0.34 (234405))
0.55 (3554.5
0.58
(254411)
0.55 (0:59437)
Average
0.57 0.11
0.61
+.133
0.10
0.64 0:07
0.08
0.67
Regression- MAB
Apmes/lyppppees Index 3%
15.3 (153-11334)
14.17 (13861534))
14.25 31
13.85 11225-15
12.97 (1120-1488
Apmea/lypopmea Index > 4%
12.52 (400-14444
11.70 (120-4223))
11. 88 1947-13155
12.41 m1s5-33.0
14.24 (571-1201)
10.56 (6116111
Gestatin Age
7.15 (595224)
6.28 (423-449
6.24 (um433))
6,35 (4214
6.40 (611-439)
6.05 (2:314117)
Systolic RP IVV)
15. (1.76 is
16. 18
(1xes 1955)
15.0 poos star
20 paia (aus))
19:1
pachs 10:30
14,65 pi-st 15:36
Dastoli: BP (VY)
9.75 (1.s (1:5-11:27
9.15 tau 18.8
R91 (244 so
g.15 sood)
10.87
(s (2:00
8.29 (s 10:3))
Systolic BP (PPG-BP) BP)
15. 50 (1100-20223)
14.38 (utean)
14.90 99 1oun-1133
14.45 11220-1700
13,60 p000 18:31
14.39 11235-1444
Diattolic BP (PPG BP)
9.35 (144-1190))
9.01 (250-12.90
9.16 0u00-13.00
9:20 (100-0200)
5:88 (132-9876)
8.71 (1.35-0001)
Average HR
7:01 (2468.99)
4.66 (229-220)
4.78 (2866533)
3.58 (2994221
3.47 (2.44422)
4.00 (2344497
HR
13:07 (1290 (3.30)
11.59 (u
12,80
9,99
10.92
(1000 1180
11,55 p1.e
Averaoo MAF (M0P
11.60 3.41 1291
10.79 + p
10.89
3.58 (4.099
10.65 + 3,88 (1877))
10.92 - 4.25
10.12 3.47
Table 4 presents comparison against three SSL methods and a baseline model trained on statistical features. In classification tasks, PaPaGei-S again shows the highest average AUROC, outperforming all others. SimCLR, BYOL, and TF-C generally outperform the statistical feature baseline but fall short of PaPaGei-S's performance. TF-C shows competitive results in some tasks, achieving the highest AUROC for Mood Disturbance and Valence. For regression tasks, PaPaGei-S again achieves the lowest average MAF SimCLR, BOI,, and TF-C show mixed results, as each excels in different tasks. SimCLR comes second in estimating Avg HR, while BYOL does so in Systolic BP (VV). The statistical feature baseline generally underperforms compared to the advanced methods across most tasks. PaPaGei-P, while not consistently outperforming PaPaGei-S, shows strong results that are often competitive with or better than other contrastive learning methods. Overall, both PaPaGei variants offer robust performance across a wide range of tasks.
5.2 ABLATION STUDIES
Pre-training data ablation. We evaluate PAPAGEI-S using different pre-training data combinations. As shown in Fig- ure 5, performance on downstream tasks improves with more upstream data, with the best results achieved when using all three datasets. No- tably, MESA outperforms the oth- despite having the fewest par- ticipants but the highest number of segments. This supports findings from language models (Dubey et al., 2024) and wearable sensing research (Narayanswamy et al., 2024), indi- cating that the volume of segments or hours contributes more to perfor- mance than the number of users.
uife
de
AUROC
15
0.6
Mill
V M
+ M-ll
M M4:1
All
Mill
V+M
M M4 All
Figure Ablation on pre-training data. Average perfor- mance across tasks for models trained on: V (VitalDB), M (MESA), and M-III (MIMIC-III). The mean value is dis- played above the plots. The Wilcoxon signed rank test is applied to evaluate significance between the All dataset and the rest (** 0.05 and 0.05 I p < 0.10).
PAPAGEI-S component ablation. We assess the impact of PAPAGEI-S components. Figure 6 shows that the full model (0.67, 10. 12) consistently outperforms individual components in both mean and median metrics. On average, sVRI (0.64, 10.35) outperforms the combinations of sVRI+ SQI (0.62, 10.80) and sVRI + IPA (0.64, 10.73). Our results indicate that combining SQI and IPA yields greater benefits compared to their individual contributions.
Downstream data-efficiency analysis. For limited-data scenarios, we assess the performance of downstream linear probing across varying levels of labeled data availability. We compare the sec- ond best- pert-perfonming baselines from Tables 3 & 4, namely TF-C and Moment. As shown in Figure 4, the classification performance of PAPAGEI-S steadily improves as more labeled data becomes
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available. While TF-C and Moment also show performance gains between 25% and 100% labeled data, their improvements are less consistent and smaller than PAPAGEI-S. In regression tasks, PA- PAGEI-S achieves the lowest MAE both 25% and 100% data availability, consistently reducing emors. At the middle breakpoints, the results are mixed with TF-C and Moment being competitive.
PaPodd
Ramk 2 Romnk
2.44
B87
10.5
13.32
PaPo S 35M
AUROC
Motaly
Shorer
iypeteeatton
Hoad
Volance
un un
er
DO PaPabal Componants
NI
Use so Companents
PM
(b)
(c)
Figure 6: PAPAGEI-S component ablation study (a, b) and scaling analysis (c). (Left) The boxplot shows the performance PAPAGEI-S components across all tasks. The Wilcoxon signed rank test is applied evaluate pair-wise significance (** < 0.05 and 0.05 S p < 0.10). (Right) Heatmap ranks of PAPAGEI-S models with 5M, 35M, and 139M parameters (rank denotes the best performance). Detailed results in Table 13.
Model size and scaling analysis. We investigated the impact of model size on performance by training PAPAGEI-S-35M and AAPAGEI-S-139M with 35M and 139M parameters, respectively. Both models share the same number of layers, but the 35M model uses 32-filter size while the 139M model uses 64. As shown in 6c, the smallest model (5M parameters) consistently outperformed larger models on all but one task. This suggests the 5M model is better suited for our pre-training datasets, aligning with prior findings on the proportionality between data and model size (Narayanswamy et al., 2024). While the 139M model surpassed the 35M, it still lagged behind the 5M, indicating that wider mod- els may improve performance in classification tasks,
SimCIR
2.66
BYDL TF-C
0.4
PaPaGei-S
0.2
Pair wise diabanees achoGa partic pants 508
Figure 7: Pair-wise inter-participant embed- ding distances for SDB.
likely due to the contrastive learning objective. Nevertheless, our scaling analysis shows a non- monotonic trend, indicating other factors strongly influence performance.
Effect of Demographics. We evaluate the effect of demographics (age, sex) and PPG-specific fea- tures (sL IPA. SQI) in Appendix $E. In demographics prediction (Table 16), PAPAGEI-S achieves 7.78 MAE in age regression, 0.85 accuracy in age classífication, and 0.79 accuracy in sex classifi- cation. While
results trail larger closed studies (Abbaspourazadd al., 2023) by 2.18, 0.05, and 0.13 for segment-level SSL, and by 5.59, 0.12, and 0.25 for patient-level SSL, they mark an ad- vancement in open-source efforts. These findings indicate that patient-level positive pair selection in features for downstream prediction. Moreover, the reduced
better captures demographic-relatee performance of PAPAGEI-S can be attributed to evaluations conducted on diverse device setups, as opposed to a single device configuration. Our ablation study (Table 15) shows that PAPAGEI-S out- performs the demo + PPG in out of 18 tasks, particularly tasks with real-time dependence such as heart rate estimation. Importantly, including demographics in addition to PAPAGEI-S creates stronger model. These findings emphasize that demographic features complement rather than compete with PAPAGEI-S, showcasing the potential of integrating PAPAGEI'S advanced feature extraction capabilities with demographic context to improve task outcomes.
5.3 CASE STUDIES
Inter-participant embeddings. Figure 7 shows the distribution of pair-wise embedding distances across participants the SDB dataset (Kiyasseh et al., 2021). SimCLR and BYOL exhibit sharper peaks at lower distances, indicating that participants are more closely clustered within the embed- ding space. This could be interpreted as a mild form of mode collapse, where the model does not fully capture the individual differences between participants. TF-C demonstrates more balanced distribution, with both large and small peaks, suggesting it captures both similarities and some vari- ation between participants. In contrast, PAPAGEI-S provides the widest dispersion of embeddings,

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Published as a conference paper at ICLR 2025
Predicted AHI 3%
100
m=
Chronos
0.18
100
m
SimCLR
100
PaPaGel-S
0.28
50
75
R²
R² 0.16
R² - 0.29
Devetl
0.04
202
Thee ANl 3%
SImCLR PaPadel
Chereos
25
25
0.02
0
0.00
100
20
100
120
True 3%
The AHI 3%
True 3%
AHl
39%
Figure 8: Regression plots and prediction distribution of different models compared to ground truth for AHI > 3%. R² is the coefficient of determination and m is the correlation slope.
highlighting its ability to capture a broader range of features that may be valuable for distinguishing between participants' medical conditions.
Regression predictions. From Figure 8, compared to the pre-trained and SSL baseline, PAPAGEI- demonstrates steeper slopes (m) and higher R² values, reflecting stronger alignment between predictions and true values. Additionally, the prediction distribution for AHI indicates that SimCLR and Chronos tend to regress more toward the mean, while PAPAGEI-S achieves a wider distribution base, highlighting its capacity to capture left tail better. Additional plots are shown in Appendix $F.
Skin tone analysis. We examine BP estimation performance across skin tones because it is crucial for practical use (Bent al., 2020). As shown in Figure 9 (More details in Figure 26), PAPAGEI-S achieves the best BP estimation across light tones. Across dark tones, we notice that BYOL and REGLE obtain the lowest MAE for Systolic BP and Diastolic BP. However, identifying a single model that performs best across all skin tones remains challenging. While PAPAGEI-S obtains the best overall performance, additional work is necessary improve robustness on darker skin tones.
DISCUSSION & CONCLUSION
Our results show that PAPAGEI outperforms baselines in at least 14 tasks, with classification and regression improvements of 4.7%-6.3% and 2.9%-4.9%, respec- tively. PAPAGEI-S excelled in cardiovascular tasks like BP, Hypertension, and HR, which can be attributed to the sVRI and IPA objectives, and PAPAGEI-P outper- formed baselines like Moment, excelling in tasks such as Smoking and Arousal. Ablation studies confirmed that the model with all three SSL objectives performs best, with sVRI highlighted SQI providing positive knowledge transfer in multi-task setups. To assess performance under class imbalance, we examined the Fl-score. PaPaGei achieves the high- est F1 in 6 out of 9 classification tasks, demonstrating its effectiveness in handling data imbalance. For regres- sion, PaPaGei-S achieves the highest H² in 7 tasks (Ap- pendix $D), reflecting better alignment with the true dis- tribution. These results highlight the robustness and ver- satility of PaPaGei-S across classification and regression tasks. PAPAGEI is both data- and size-efficient (5M),
REQUE
Naman:
S10
Systolic BP (VV)
20
10
Diastolic BP (W)
key component and IPA and
Light Tones (<=3) Dark Tones (>3)
Fitzpatrick Skin Tone
Skin tone analysis for Blood Pressure estimation (VV dataset)
Figure 9:
making it ideal for medical applications where large models (200M+) are impractical due on- device limitations or data privacy concerns with cloud model inference. While combining PAPAGEI- P and PAPAGEI-S objectives into one model might seem intuitive, it is impractical because it would constrain positive pairs on both sVRI and the number of participants, resulting in too many unique labels with limited samples per label. Our case studies also showed that PAPAGEI-S captured per- sonal medical information due to well-dispersed embeddings, compared to baselines. Future work should focus on diversifying training data, investigating sampling rate effects, and exploring multi- modal approaches or alternative architectures. Additionally, extracting PPG features for different morphologies is non-trivial, future work benefit from systematic evaluation of PPG features and modeling. In conclusion, PAPAGEI represents significant advancement in foundation models for analyzing PPG signals in resource-constrinn medical environments, with its open-source nature encouraging further research and development in healthcare applications.
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REPRODUCIBILITY STATEMENT
Models, data, and code are publicly available for reproducibility and future research. We exclusively utilize publicly accessible datasets, which can be requested or downloaded from the respective study group websites, allowing others to easily obtain the data for their own analyses. In $4 and Ap- pendix $B, we provide comprehensive descriptions of the datasets, ground-truth annotations, and data pre-processing methods used in our experiments, ensuring transparency in our data handling procedures. The code to run our model is published with user-friende friendlly examples. We have provided detailed overview of the model architecture and its nyperparameterss in $3, $4.1, and Appendix $A. Thus, our work is designed to be reproducible, enabling future research to build upon our findings.
ETHICS STATEMENT
Our research on PAPAGEI, utilizing publicly available PPG datasets, adheres to data privacy regu- lations and promotes transparency through open-source releases. We acknowledge potential biases in the training data and have evaluated performance across diverse datasets, particularly regarding skin tone variations. While PaPaGei offers significant potential for improving non-invasive health monitoring, recognize the need to address potential misuse (Perez-Pozucle ples of misuse could include unauthorized health monitoring, discriminatory practices in insurance employment, unfair credit scoring, exploiting personal health data for targeted marketing. We strongly advocate responsible use solely for beneficial healthcare applications. Our study followed established research ethics guidelines, and we declare no conflicts of interest. We encourage on- going interdisciplinary dialogue to address potential risks and ensure responsible development and deployment of such technologies, recognizing the broader societal impacts of Al in healthcare, We remain committed to ethical AI advancement and welcome further discussion on the critical issues, including the development of governance frameworks to prevent misuse and protect data privacy.
al., 2021). Exam-
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APPENDIX
A TRAINING AND INFERENCE DETAILS
Architecture Pre-training. The architecture of our ResNet 18-block encoder is described in Tables 5, 6, and 7. Each ID convolution layer is configured with kernel size of 3 and stride of while the max-pooling layer utilizes a kernel size of with a stride of We start with a filter size of 32, which doubles every 4 blocks to capture progressively more complex features. Dropout is applied with probability of 0.5 to prevent overfitting. This backbone architecture is used across different methods in experiments to ensure consistency and make for fair com- parison during evaluation. Additionally, our SSL baselines use the same batch size, learning rate, input sampling frequency, and training steps PAPAGEI. We use the same augmentation types and intensity for BYOL (Grill et al., 2020), SimCLR Chen et al. (2020), and PAPAGEI-P. Furthermore, investigated 0.07 and 0.5 temperatures 2020), respectively. In contrast to a smaller embedding size of 256 adopted by (Abbaspourazad al., 2023), we project the learned representations to a 5 122-dimensional embedding after the con- volutional block (we investigated larger embedding sizes of 768 and 1024 and found no signifi- cant performance changes). This embedding is then passed through two Mixture of Experts (MoE) blocks, each containing three experts. Each expert block consists of two sequential linear layers, with sizes 256 and 1, which are used for IPA and SQI prediction tasks. It is noteworthy that both BYO and TF-C require multiple encoders and different projection heads, resulting in variations in model sizes. For these methods, we use existing implementations available online*4, but apply our encoder as the backbone to ensure consistency. Our models pre-trained for 15,000 steps using the Adam optimizer, with a learning rate of 10-4, We use a batch size of 128 for training since after various trials did not observe significant differences in performance with batch sizes of 64 and 256. We performed five iterations of pre-training and selected the best-performing model for each downstream task. For SimCLR and PAPAGEI-P, single model consistently achieves the best performance across all tasks. For BYOL, we select two models that perform best across all tasks. Similarly, for TF-C and PAP PAPAGEI-S, we choose three models with the highest performance. We use this approach as some models excel in certain task groups while others perform better in the rest. Note that more robust approach would involve broader hyperparameter tuning with k-fold vali- dation to obtain the optimal model. However, this requires substantial computational resources for pre-training. Additionally, we did not perform an exhaustive evaluation of different augmentation settings but instead used transformations and values based on prior research (Abbaspourazad et al., 2023; Tang et al., 2020). For model training, we primarily used Py PyTorch (Paszke et al., 2019). The NoXentLoss implementation was sourced from the Py Porch Metric Learning package
MoCo (He al., 2020) and SimCLR (Chen et al.,
Table 5: ResNet-style CNN encoder architec-
ture used in PAPAGEI.
Table 7: Basic Block Type 2
Layer
Output Shape
Table 6: Basic Block
Convl
32, 1250]
Type 1
Layer
Batch Norm
[32, 32, 1250]
BatchNorm
ReLU
32, 1250]
Layer
ReLU
Basic Block Type 1
[32, 32, 1250]
Conv1D
Dropout
(Basic Block Type 2) x 3
[32, 32, 313]
BatchNorm
ConvID
(Basic Block Type 2) x 4
[32, 64, 79]
ReLU
BatchNorm
(Basic Block Type 2) x 4
[32, 128, 20]
Dropout
ReLUU
(Basic Block Type 2) x 4
[32, 256, 5]
1D
Dropout
(Basic Block Type 2) x 2
Conv ID
BatchNorm
[32, 512,
Maxpool
ReLU
[32, 512,
Linear
[32, 512]
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Parameters:
Training and Inference. This section outlines the training and inference parameters used in our methods. Inference parameters are those utilized for feature extraction.
PAPAGEI-P (5M) and SimCLR (5M): Both training and inference involve 5M parameters. For SimCLR, it worth noting that we use the projection features during inference instead of using the encoder only.
BYOL (5M): During training, the online and target encoders each have 5M parameters, and the projector 800K. At inference, only the online encoder used for feature extraction, totaling 5M parameters.
TF-C (10M): The time and frequency encoders each have 5M parameters, followed by smaller projector (< 100K). Since both encoders and projectors are required for inference, the total parameter count is 10M.
PAPAGEI-S (5M): The encoder consists of 5M parameters, while the expert heads con- tribute approximately 400K each. As the expert heads are not used for feature extraction, the inference parameter total remains 5M.
We extracted the projected embedding for linear eval- uation. For Moment and Chronos, we extract the default embedding size, which is 1024 and 768, respectively. We use cross-validated grid search identify the best parameters for our linear probes.
Feature Extraction & Linear Evaluation
The hyperparameters chosen for each model are as follows: (1) Logistic Regression: { pppenalty:: ['11', '12"), C [0.01, 0.1, 1, 10, 100]. 'solver': ['Ibfgs'] J, mnax_iter:: [100, 200]3. (2) Linear Re- gression: I'alpha': [0.1, 1.0. 10.0, 100.0], 'solver': ['auto', 'cholesky', 'sparse_cg" [}. (3) Random
Forest: ('n estimators': [100, 200]. 'max features': ['sqrt', 'log2'], 'max depth': [10, 20, 30],
"min samples split': [2, 5]. "min samples leaf": [1, 21}
B DATASETS AND TASKS
Table 8: The task evaluation benchmark of PAPAGEL. Datasets highlighted in gray are unseen during training, thus, the corresponding tasks out-of-domain. The rest were used for pre-training but their test sets and labels are held out. For task type, B/M/R refer to Binary classification, Multi-class classification (#classes), and Regression, respectively.
WID
Dutnset
SR (HIz)
Collected by
Tusk
Task Type
#Participunts ((Samples)
T1
VitaDDB (Lec ct. 2022)
500
ICU monitor
ICU admission (Yes/No)
5356
T2
Operation Type Mortality
M (11)
5866
T3
MIMICIII III (Meody al., 2020)
125
ICU Monitor
5596
(Zhang al., 2018)
256
Polysomnograplyy finger
Smoker
T5
AHI > 38 Oxygen Desar.
R
2035
T6
AHI 2-4% Oxygen Desat.
R
2055
nuMom2D (Facco al., 2015)
Polysomnographny finger
Pregnancy stage (carly/late)
(5337)
T8
Gestation Age
R
3163 (5337)
T9
VV (Skin Tone) (Toye, 2023)
60
Finger
Systalic BP
R
231
Distollcc
231
T11
PPG-BP (Liong ot al., 2a)
1000
Finger Pulse Ox
Systolic BP
R
219
T12
Diastolic BP
R
219
T13
Avenge Heart Rate
219
Hypertension
219
T15
SD8 (Garde et al., 2014)
62.5
Finger Pulse Ox
Sleep Disordered Breathing
B
146
T16
ECSMP (Gao al., 2021)
Wrist
Mood Disturbance
B
89
(Schmidt
Valence
(4497)
T18
Arousal
B
15 (1497)
T19
PPG-DaLiA (Reiss et al., 2019)
64
Wrist
Heart Rate
R
15 (64697)
Activity
(64697)
VitaIDB. The VitaIDB dataset provides comprehensive monitoring of vital signs and physiological parameters from 6,388 surgical cases. This high-resolution dataset includes wide range of intraop- erative monitoring variables such as heart rate, blood pressure, oxygen saturation, and other critical physiological signals, collected at frequent intervals throughout surgery. The surgical operation be- longs one of the eleven categories: colorectal, biliary/pancreas, stomach, major resection, minor resection, breast, transplantation, thyroid, hepatic, vascular, and others. After the data cleaning pro- cess, narrowed the dataset down to 5,866 participants with complete and usable information. As depicted in Figure 10, we observe that the gender distribution is relatively balanced, with nearly equal representation of male and female patients. Additionally, the majority of the participants fall
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within the age range of 50 70, with significant proportion being around 60 years old. The ICU label corresponds to whether the person was admitted to the ICU or not.
VitalDB
500
400
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Count
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300
4000
200
Count
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Count
100
1000
2000
0
0
20
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60
80
1
age
ICU
Figure 10: VitaIDB dataset descriptive statistics.
MIMIC-III In our analysis, we utilize the MIMIC-III waveform database matched subset, which comprises data from 10,282 ICU patients. From this dataset, focus specifically extracting photoplethysmogram (PPG) data, provided it is available for each patient. To ensure the quality of the data,
criterion of least minute of usable PPG signal that must be present. After performing a thorough data cleaning process, we end up with cohort of 5,596 participants with reliable PPG data. As illustrated Figure 11, the dataset shows a gender imbalance, with higher proportion of male patients compared to female patients. Additionally, the majority of participants are aged 60 years or older, reflecting typical ICU population that often includes elderly patients with critical health conditions.
MESA. The Multi-Ethnic Study of Atherosclerosis (MESA) sleep sub-study gathered data from 2,237 participants through overnight, unattended polysomnography to assess various sleep parame- ters. After the data cleaning process, we retained 2,055 participants for analysis.
shown in Figure 12, the dataset shows a slightly larger proportion of female participants. The age distribution reveals that most participants are between 60 and 80 years old, reflecting an older adult population, which commonly studied concerning sleep disorders and cardiovascular risks.
Index (AHI) with at least 3% and 4% oxygen desaturation the primary measure for diagnosing sleep apnea, recommended by the American Academy of Sleep Medicine (Ruehland et al., 2009). These thresholds indicate the severity of sleep apnea, with oxygen desaturation during apneas/hypopneas being a critical factor. We predict these AHI values directly in our regression models. Additionally, we classify participants with any history of smoking as smokers. This approach allows to account for both current and former smokers, capturing broader range of imoking-related health risks within our analysis.
this study, we use the Apnea-Hypopen
NuMoM2B. Changes in gestational age and pregnancy stage are risk factors associated with adverse pregnancy outcomes such as hypertensive disorders and foral-lor-gettaionnall delivery (Bouariu al., 2022: Parikh al., 2021: Wu et al., 2020; Crump al., 2023). These diseases affect heart
MIMIC-III
400 1
MIMIC-II
300
3000
Count
200
Count
2000
100
1000
0
20
40
60
80
M
F
age
sex
Figure 11: MIMIC-III dataset descriptive statistics.
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MESA
MESA
MESA
200
400
Count
Count
150
Count
100
100
200
50
50
100
0
50
100
20
age
AHI
3%
AHI
4%
MESA
MESA
1000
1000
Count
Count
500
500
0
female
male
yes
no Smoking
not reported
sex
Figure 12: MESA dataset descriptive statistics.
NuMoM2B
NuMaM2B
1250
NuMoM2B
3000
300
1000
Count
200
Count
Count
2000
500
100
1000
250
0
20
30
40
10
20
3
age
Gestation Age
Pregnancy Stage
Figure 13: NuMoM2B dataset descriptive statistics.
function that can be measured using the PPG sensor (Feli al., 2024). The Nulliparous Pregnancy Outcomes Study: monitoring mothers-to-be (nuMoM2B) sub-study examines the relationship be- tween adverse pregnancy outcomes and sleep disorders. In particular, overnight polysomnograph that collects PPG data is administered to the women at their homes during 6-15 weeks (carly) and 22-31 weeks (late) of pregnancy. Therefore, our tasks are to classify between early and late-stage pregnancy as well as predict the gestation age of the fetus. In Figure 13, we observe that maternal age peaks around 28 years. The gestational age distribution bimodal, which we use as a predictor in our regression task. For pregnancy stage, we classify visit as early and visit 3 as late.
VitalVideos (VV) (Skin Tone). The Vital Videos study an ongoing project that collects data on vital signs, videos, and blood pressure across variety of conditions, including variations in lighting. background, and skin tone. For our analysis, we used data from two groups, totalling 231 participants. from Europe and Sub-Saharan Africa. As shown in Figure 14, most participants have Fitzpatrick skin tone of 5 or 6. indicating darker skin. The dataset is primarily composed female participants, with age range between 40 and 60 years. Additionally, the majority of participants had a systolic blood pressure of around 125 and a diastolic pressure of around 80, suggesting that most individuals in the study were relatively healthy.
PPG-BP. The PPG-BP consists of short PPG recordings from 219 participants collected at 1000Hz. For each subject, there three 2s recordings. For analysis, we zero pad them to 10s. In Figure 15, the age distribution shows that most participants are between 40 and 80 years old, with fewer participants under 40. Furthermore, the majority of individuals have hypertension. In terms of gender, the dataset has slightly more females than males. The distribution of systolic blood pressure centered around 120-140. indicating a population with normal to moderately elevated blood pressure, while diastolic blood pressure predominantly falls between 70 and 90. Lastly, the average heart rate for most participants ranges between 70 and 90 beats per minute.
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Vital Videos (VV)
Vital Videos (VV)
40
75
Count
30
Count
50
20
25
10
0
20
40
80
1
2
4 5 6
age
Fitzpatrick Scale
Vital Videos (WV)
150
Count
100
40
(VV)
Vital Videos (VV)
40
Count
30
50
Count
30
20
20
0
10
10
F
M
100
120
140
sex
Systolic BP
Diastolic BP
Figure 14: Vital Videos dataset descriptive statistics.
PPG-BP
PPG-BP
PPG-BP
30
Count
100
Count
100
Count
50
50
20
40
60
80
Female
Male
age
Hypertension
PPG-BP
PPG-BP
PPG-BP
30
40
30
Count
20
Count
30
Count
20
20
10
10
80
100
120
140
160
180
60
80
100
50
60
70
80
90
100
Systolic BP
Diastolic
HR
Figure 15: PPG-BP dataset descriptive statistics.
SDB. The sleep-disordered breathing dataset includes data from 146 children, collected through polysomnog- raphy with finger recordings lasting over three hours. Ground truth labels are provided as Apnea-Hypopnea In- dex (AHI) values, categorized into four levels: 0 (nor- mal), (mild, AHI between 5 and 15), 2 (moderate, AHI between 15 and 30), and 3 (severe, AHI over 30) shown in Figure 16. For our classification task, we group AHI O indicating sleep breathing disorder, while AHI lev- els I through are classified as the presence sleep breathing disorder.
SDB
Count
50
sleep Disordered Breathing
Figure 16: statistics.
SDB
dataset descriptive
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ECSMP. The ECSMP dataset was gathered to study the relationship between emotion, cognition, and sleep in 89 participants. As shown Figure 17, the majority of the participants are young measured using
adult females, with average age of around 25 years. Mood disturbances the Profile of Mood States (POMS) scale, which captures various aspects of emotional states. To classify participants into high versus low mood disturbance categories, binarized the Total Mood Disturbance (TMD) values by using the median as the cutoff point.
ECSMP
ECSMP
20
ECSMP
Count
Count
40
10
Count
10
20
20
22
24
28
100
160 Mood Disturbance
120
140
180
age
Figure 17: ECSMP dataset descriptive statistics.
WESAD. The wearable stress and affect detection dataset is a modti- dataset collected from 15 participants using various sensor modalities. In this study, participants were exposed to videos designed elicit different affective states, such as amusement, meditation, stress, and baseline conditions. Following each session, participants completed the belf-Assessment Manikins (SAM) questionnaire (Bradley & Lang. 1994), which provided the ground-truth values for valence and arousal. In our analysis, we binarized these values by categorizing valence and arousal low (I) when less than 5 and high (0) otherwise. Then, we perform regression at the segment level. As shown in Figure 18, arousal levels are generally low, while valence tends to be high in most cases.
WESAD
WESAD
1500
1500
Count
1000
Count
1000
500
500
1.0
2.0 3.0
4.0 5.0
6.0 Arousal
7.0 8.0
9.0
1.0 2.0
3.0
4.0 5.0
6.0
7.0
8.0
9.0
Valence
Figure 18: WESAD dataset descriptive statistics.
This dataset collects PPG signals from 15 participants for heart rate estimation while performing various daily activities. These activities include sitting, ascending/descending stairs, table soccer, cycling, driving, lunch break, walking, and working. As a result, the dataset captures wide range of heart rates, varying from 60 to 150 beats per minute, depending being performed. To align the PPG signal with the activity labels, we use a 8s window with and 2s overlap and shift, respectively. After this, we resample and pad the signal facilitate modeling.
PPG-DaLiA.
the specific activity
DaLiA
2000
1500
DaLiA
Count
15000
1000
Count
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500
5000
0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
50
100
150
Activity
HR
Figure 19: PPG-DaLiA dataset descriptive statistics.
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C
REPRESENTATIVE SIGNALS FROM PRE-TRAINING DATASETS
VitalDB @ 500Hz
25
0
0
1000
2000
3000
4000
5000
MESA @ 256HZ
0
-250
0
500
1000
1500
2000
2500
MIMIC-III @ 125HZ
0.25
0.00
0
200
400
600
800
1000
1200
Figure 20: Representative 10-second raw PPG segments from VitaLDB, MESA, and MIMIC-III We observe that each signal's amplitude (y-axis) and sampling rate differ.
VitalDB @ 125Hz
2.5
0.0
0
200
400
600
800
1000
1200
MESA @ 125HZ
-2
0
200
400
600
800
1000
1200
MIMIC-III @ 125HZ
2
0
0
200
400
600
800
1000
1200
Figure 21: Normalized and resampled 10-second pre-processed PPG segments from VitaIDB, MESA, and MIMIC-III. These signals represent the final form before being fed to our models. We observe that the signal characteristics across datasets are more consistent.
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D ADDITIONAL RESULTS
D.1 MULTI-CLASS CLASSIFICATION
Table 9: Multi-class classification comparison against pre-trained models. Feature extraction parameters are indicated next to each name.. 95% Cls are reported square brackets and the best value is bolded.
REGLE (0:7M))
Chronos (2000)
Moment (355M)
PAPAGEI-P
PAPAGEI-S (580
Clussification ACC (1)
(hin 2004
all
(Cosmami 2004
Operation Type
0.21 (0.18:0.23]
0.25 (0.22 0.29
0.27 0.23-0.311
0.30 (0.26-0.33
0.30 (0.27 0.32
Activity
0.29 (0.28-0.29]
0.41 (0.40-042)
0.41 [0.40-042]]
0.38 (0.37-0.39)
0.37 (0.36.0577)
Average
0.04
0.33
0.34 0.07
0.04
0.33
Table 10: Multi-class classification comparison against CL methods. Feature extraction parame- ters are indicated next to each name.. 95% CIs are reported in square brackets and the best value is bolded.
Stat. Features
SimCLR (500
BYOL (5M
TF-C (164)
PAPAGEI-P (30 PAPAGEI-S (3N)
Classification ACC (t)
Operation Type
0.27 (0.44,33))
0.27 00300200
0.31 027034
0.27 (2770029
0.30 (026-033))
0.30 (3
Activity
0.37 (0.666.30)
0.36 (0336)31
0.34
0.37 (035-011
0.38
0.37 (1161177
Average
0.32 + 0.05
0.31 + 0.04
0.32 + 0.01
0.32 = 0.05
0.34 0.04
0.33 = 0.03
D.2 F1-SCORE AND R² EVALUATION METRICS.
Table 11: Downstream comparison against pre-trained models (additional metrics: F1-score and R²), Feature extraction parameters are indicated next to each name. 95% CIs are reported in square brackets and the best value is bolded.
REGLE (UUM)
Chronos (UEM)
Moment (WMM)
PAPAGEI-P (M))
PAPAGEI-S (SM)
Classification F1 F1-Score (1)
(Yind
(Ancri 2004)
ICU Admission
0.00 (0.000000]
0.20 (0.11-0.00
0,12 (0.04-220
0.12 [0.04-2200)
0,26 (0.15-0.33
Mortality
0.00 puv 0.00
0.14 0.19)
0.16 (0.31 a21
0.22
(0.16 0.71
0.17 (.23
Smoker
10-023
(0-4-0358 58]
0.40 (0:19-047)
(0.18-551)
9039-0100)
Pregnancy stage
0.49 [p.47-0.52]
0.69 (u67-00711
0.63 (0.60-2.55)
0.62
(0.59-064)
0.65 (0.62-067]
Hypertension
0.77 0.34
0.68 puss 0.771
0.75 (ou66 0.44
0.84 (0.72 0.92
0.78 (8.70-085)
Sleep Disordered Breating
(000-0000)
(1000100)
0.22 (0:00-447))
0.32 (0000-
0.47 (123-107)
Mood Disturbance
0.00 (a0004000]
0.36
0.23 (0.00-244)
0.37 (0.00-066)
0.32 (8.00-0553)
Valence
0.00 (100-0000)
0.10 po07-0
0.12 pouce-a 16)
0.17 (0.1--221)
01.33 (0.0-0004)
Arousal
0.83 (.541-0344
0.82 [0.000.833]
0.81 (0.79-0882)
0.81
(0.79-082)
0.83
(0.81-0.44)
Average
0.33
0.24
0.38
0.25
0.25
Regression R² (t)
Apnea/Hypopnea Index 3%
0.02 (0060001
0.18 (10540266]
0.14 (0:0--222))
0.15 0206-024)
0.29
(a (12224196)
Apnea/Hlypopea Index 4%
0.01 [006-005]
0.16 (005-0.22]
0.13 (0:05-0200)
0.12 (0.00-1:22)
0.28 (0.20-0.34)
Gestation Age
0.04 o.06
0.28 p2210111
0.20 (o.17-023)
0.18 (0.14-0.22)
0.22 (0.19.025]
Systolic BP (VV)
-0.03 (0.18-001)
-0.24 1077 auo
0.06 1-0.5 222
-0.41 (1177)(1155)
0.15 0.0090.300
Diastolic BP (VV)
0.01 [40046006)
-0.29 (os7-1on))
0.01 (-0:55-0144
-0.48 (1002402000)
0.10 (-01114)33)
Systolic BP (PPG-BP)
-0.07 [021-004)
-0.13 (436-000)
0.07 [-0.1 -031]
0,36 (0.16-0.49)
0.20 (0.02.0.11)
Diastolic BP (PPG-BP)
0.01 (00050000)
-0.10 (o4sa0))
-0.03 [-0.3140.13]
0.22 (-0113-0.40)
0.08 (-0007-0117]
Average HR
0.37 [0.1700511]
0.02 (0.10.77]
0.68 (0.45-0.00]
0.79 (0.57:0.0))
0.78 (0.69-083)
HR
0.00 (100-000)
0.57 (1.600399]
0.63 (061-1644)
0.52 (0.1-415)))
0.48 (0.42.446)
Average
0.04 + 0.12
0.05 + 0.25
0.21 + 0.24
0.16 0.38
0.28 + 0.20
D.3 ABLATION RESULTS
In this section, we provide the numeric results for the scaling analysis (Table 13) and PAPAGEI-S component analysis (Table 14).
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Table 12: Downstream comparison against CL models (additional metrics: Fi-score and R²). Feature extraction parameters are indicated next to each name. 95% Cls are reported in square brackets and the best value is bolded.
Stat. Features
SimCLR 1MM)
(Y
TF-C (LONT)
PAPAGEL-P ost
PAPAGEL-S (M
Classification FI-Score (t)
ICU Admission
0.30 (o.15-0440
0.19 20.10
0.17 20
0.10 (1
0.12 p0o0-.200
0.26 pi (015-:33
Mortality
0.03 (p0r-00))
0.15 19:10-0:01
0.13 17)
0.15
0.22
0.17 [0:3-20]
Smoker
0.47 (000-0.51)
0.43 (0:15-0u49)
0.49 (1122130)
0.37
(). (0:0000441
0.45 (03-0:11
0.45 (0774-20)
Pregnancy stage
0.43 (041-444)
0.60 (9.5)-063)
0.60 passanss
0.59 (1.6-12221
0.62 (139-194)
0.65 (A824887]
Hypertension
0.73 posss-uusg
0.82 (0/73-0u9))
0.81 (175-1183)
0.81
(i (1770000)
0.84 p012-091
0.78 (170416)
Sleep Disordered Breathing
0.00 puac-e00)
0.46
0.45 pa23-1922
0.19
0.32
0.47 (0150001]
Mood Disturbance
0.21 (60000443)
0.21 0.0-0.44)
0.37 (100-067)
0.56
(0:7000000
0.37 (000000))
0.32 (0.000.58)
Vulence
0.04 (00z-000
0.09
0.01 pus0-10s
0.07 puss-mm
0.17 pu3-0211
0.03 (101-104))
Arousal
0.82 (081-0083
0.81 0.32)
0.83 (131-0844
0.81 (0.000083)
0.81
(0. (079-82)
0.83 (0.514554)
Average
0.33 1: 0.28
0.42 0.26
0.43 1. 0.27
0.40 0.27
0.43 1. 0.25
0.44 1. 0.25
Regression- 11 (1)
Apnea/Hypopnea Index 3%
-0.00 10006-0.00)
0.16 0:23
0.16 (006-0222
0.06 100000111
0.15 (0050234
0.29 (0:200.00)
Apnea/Hyypggna Index 4%
-0.01 (-000-0.01)
0.13
0.13 1oor-n199
0.13 (-0000001
0.12 (000-0222)
0.28 (0:0-4:44)
Gestation Age
0.19
pussau22
0.18 (0:10021)
(13440222
(0:94025]
Systolic BP (VV)
-0.10 (-011-4110)
-0.05
(-345-00211
.0.03 14:37-0
-0.05 (-0 1.
-0.41 p40770 1511
0.15 (-009-0
Diastolic (VV)
-0.15 (4.11-4111)
-0.01
-0.09 (4.45-0.16)
(-100-40.000
14:11-033
Systolic BP (PPG-BP)
0.12 (-0040221)
0.09
(02200.31)
0,10
(0.660330)
0.13 (-006-220)
0.36
(0.15-0.40)
0.20 (.224.31)
Diastolic (PPG-BP)
pauss-1144)
0.00
(4:1-1111)
0.02 (4015-001)
p-015-4444 ong
Average HR
0.15 [0000-0333)
0.74
(064-0030)
0.65 (15004.77)
0.82 (0.340:8))
0.79 (057-0.00)
0.78 (..0-.53)
HR
0.34 (032-0036)
0.45
(0.44.0447)
0.36 (035:0337)
0.54 (..53.).5))
0.52 (.51..55)
0.48 (.224.0]
Average
0.05 +: 0.14
0.17 1: 0.25
0.18 1: 0.20
0.19 1 0.28
0.16 -:: 0.38
0.28 11 0,20
Table 13: Scaling: Downstream comparison for different PAPAGEI-S models. 95% CIs are reported in square brackets and the best value is bolded.
PAPAGEI-S-5M
PAPAGEI-S-35M
PAPAGEI-S-139M
Classification AUROC (f)
ICU Admission
0.79 (0.75-0.32]
0.72 (0.68-075)
0.77 (0.730.30)
Mortality
0.67 [063 0.70]
0.66 (063 (L70)
0.66 (0.630.69)
Smoker
0.61 (0-66006))
0.58 (053-16))
0.59 (o (0-54-065)
Pregnancy stage Hypertension
0.78 (.775-080)
0.77 1075 (.79)
0,76 007740778))
0.77 () (0.88-0:7]]
0.75 (164-185)
0.77 (6-6--087)
Sleep Disordered Breathing
0.70 [0.57-0.34]
0.59 (044-0044)
0.62 (0.46-0788)
Mood Disturbunce
0,56 (0:33-0777]
0.53 (0.30 (.73)
0.54 0.729.778)
Valence
0.56 () (0:54-0:9]
0.53 (096-1966)
0.54 (I (1-066))
Arousal
0.55 (0.52-0.57]
0.52 (0.9-0.55)
0.55 (0. (0.32.0.58)
Average
0.67 0.09
0.63 0.09
0.63 : 0.10
Regression- MAE (L)
Apnea/Hypopnea Index 3%
12.97 (11.8714455)
13.07 (11.92-1425)
12.86 (11.79-1294]
Apncu/Hypopnea Index >> 4%
10.56 11.62)
10,79 (9.85 11,83]
10.65 11,6211.69]
Gestation Age
6.05 [591-6.71
6.10 (5.94:6224)
6.17 (6.02-6330]
Systolic BP (VV)
14.65 (12.5016.55)
15.10 11310117111
14.95 (12.87:7701]
Diastolic HP (VV)
8.29 (661-1022)
9.20 (6/99-11122)
8.95 91-1099)
Systolic BP (PPG-BP)
14.39 (12.55-16445)
16.70 (14.25-19.8)
16.20 (13.73-18855]
Diastolic BP (PPG-BP)
8.71 17.15 1001)]
9,48 (5.24 10.90)
9.32 17.00 10.09)
Average HR
4,00 (531-1667)
4,76 (1.94-80))
4,71 (3.0-1
HR
11.53 (114001166))
12.86 (12773122000
12.20 12.44-12.344
Average
10.12 3.47
10.89 + 3.73
10.76 4 3.57
D.4 STATISTICAL SIGNIFICANCE OF MODEL COMPARISON
In addition to confidence intervals, we perform the following steps evaluate the significance across models on a per task basis (Tables 3 & 4). First, we ran the Friedmann Chi Square test, and identified statistically significant differences across PAPAGEI and the baseline models at p < 0.05. Next, we created critical difference (CD) diagrams to rank the best performing models, as suggested by the literature to compare models over multiple datasets (Demšar, 2006). The CDs indicate that PAPAGEI performs the best across both classification and regression tasks. Furthermore, it has statistically significant average rank as indicated by the lack of horizontal line.
html+
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Table 14: PAPAGEI component ablation study results.
sVRI
sVRI + SQI
sVRI + IPA
Full
Classification AUROC
ICU Admission
0.79
0.75
0.78
0.79
Mortality
0.67
0165
0.67
0.67
Smoker
0.59
0.60
Pregnancy stage
0,78
0.72
0.78
Hypertension
0.77
0.72
0.75
0.77
Sleep Disordered Breathing
0.62
0.64
0.70
Mood Disturbance
0.53
0.56
0.55
0.56
Valence
0.54
0.55
0.53
0.56
Arousal
0.44
0.51
0.49
0.55
Regression MAE (4)
Apnea/Hypopnea Index >
13.36
13.74
13.42
12.97
Apnew/Hypopneu Index >> 4%
11.01
11.43
11.29
10.56
Gestation Age
6.18
6.32
6.15
6.05
Systolic BP (VV)
14.62
15.97
15.33
14.65
Diastolic BP (VV)
8.32
8.76
8.29
Systolic BP (PPG-BP)
15.03
14.39
16.15
14.39
Diastolic BP (PPG-BP)
9.12
8.76
9.06
8,71
Average
4,00
HR
11.51
11.97
11.88
11.53
Classification: Critical difference diagram of average score ranks 0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Regle (0.22)-
(0.9) PaPaGei
Stat. Features (0.4)
(0.66) Chronos
BYOL (0.51)
(0.65) Moment
SimCLR (0.54)
(0.62) TF-C
Figure 22: Critical Difference Diagram for Classification Tasks. The axis represents the average rank of the model. The horizontal connector lines indicate no significant differences between the models.
From the critical difference diagrams observe that PAPAGEI is significantly better across clas- sification (Figure 22) and regression (Figure 23) tasks. This arises because PAPAGEI is the highest ranking model across most tasks. Furthermore, observe Moment is strong model across both classification and regression tasks. Whereas Chronos and TF-C perform well for classification tasks only.
We conduct additional statistical significance comparisons using a structured approach. First, we randomly sample score from within the confidence intervals for each task across all models. Next,
Regression: Critical difference diagram of average ranks 0.2
0.3
0.4
0.5
0.6
0.7
0.8
PaPaGei (0.19)-
(0.81) Stat. Features
SimCLR (0.48)
(0.81) Regle
Moment (0.5)
(0.65) Chronos
BYOL (0.53)
(0.53) TF-C
Figure 23: Critical Difference Diagram for Regression Tasks. The axis represents the average rank the model. The horizontal connector lines indicate no significant differences between the models.
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Published as a conference paper at ICLR 2025
apply the CD ranking procedure to the sampled scores. This process is repeated 1,000 times, and the ranks are averaged. The entire experiment conducted five times for Tables 3 and 4, with the results presented in Figure 24. The colored cells indicate that PAPAGEI is statistically significant compared to the respective model p p < 0.05. Our findings show that PaPaGii consistently achieves the best average rank, ranging between 0.82-0.90 for AUROC and 09-0..2 for MAE. Across 35 comparisons (PaPaGei vs. the other models, repeated five times), PaPaGei demonstrates significant improvements in 30 of 35 AUROC comparisons and 32 out of 35 MAE comparisons. Among the baseline models, we acknowledge that Chronos and TF-C are strong competitors capable of performing comparably to PAPAGEI.
0.37
0.33
Experiments
Experiments V IV
0.38
0.21
0.5
0.65
0.84
0.25
0.34
0.23
0.77
0.65
0.56
0.54
0.5
0.87
0.67
0.81
0.47
0.47
0.51
0.72
0.6
0.25
IV
0.54
0.19
0.63
0.61
0.54
0.45
0.61
0.9
0.58
0.8
0.6
0.53
0.6
0.61
0.53
0.25
0.47
0.26
0.68
0.57
0.5
0.51
0.68
0.82
0.65
0.78
0.65
0.56
0.56
0.64
0.43
0.24
Stat, Features
REGLE
Chronos
Moment
SimCLR
BYOL
TF-C
PaPaGel
Stat. Features
REGLE
Chronos
Moment
SimCLR
BYOL
TF-C
PaPaGei
Methods (AUROC)
Methods (MAE)
Figure 24:
Bootstrap ranking repeated for five experiments: AUROC (left) and MAE (right). Col-
ored cells indicate that PAPAGEI is significant to the baseline p < 0.05.
D.5 STATISTICAL ASSESSMENT BETWEEN IPA AND SQI
Recall that we incorporate SQI handle situations where the dicrotic notch cannot be computed due to poor-signal quality or different morphologies. We performed a permutation test (Rice, 2008) to statistically evaluate PPG segments where IPA unavailable. By splitting SQI values into no IPA and IPA groups and testing significance over 1000 permutations, we observed statistically sig- nificant differences (p < 0.05) in both mean (+0.18) and median (+0.32) SQI values, with the IPA group having larger SQL. These findings empirically motivate SQI's ability to handle limited PPG morphology.
E ADDITIONAL BASELINES: DEMOGRAPHICS & PPG MORPHOLOGY FEATURES
In this section, we evaluate the effectiveness of demographics (Demo: age, scx) and PPG mor- phology (sVRI, IPA, SQI) predict both regression (ridge) and classification (logistic regression) tasks: (1) Ablation Study: We compared PaPaGei with three peeeiness-dmmorrappe features alone, and demographics + PPG features. Our results show that while demographics is stronger baseline than statistical features, PaPaGei outperforms the demographics + PPG baseline in 14 out of 18 tasks. (2) Effeet of Demographies: We trained a model combining PaPaGei-S with demographics. The results indicate that incorporating demographic features with PaPaGei-S creates stronger model than using PaPaGei-S alone.
alone, PPG
From Table 15, we observe the following classification performance (Positive is better): ICU (+0.13), Mortality (+0.01), Smoker (-0.02), Pregnancy Stage (+0.22), Hypertension (0.00), SDB (no demographics), Mood Disturbance (-0.08), Valence (-0.01), Arousal (+0.04). Regression Tasks (Negative is better): AHI > 3% (-1.43), AHI 4% (-1.61), gestation age (-1.54), SBP-VV (-0.31), DBP-VV (-0.46), SBP (-0.11) DBP (-0.65), Avg. HR (-4.07), HR (-2.93). PaPaGei-S performs better for real-time sleep and cardiovascular outcomes such as sleep apnea, heart rate and blood pressure, respectively. In particular, we notice that outcomes such heart rate benefit substantially from PPG rather than demographics. Demographics are useful in tasks without real-time depen- dence such smoking, which is established be associated with age and sex (Chung al., 2020).
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Table 15: Demographics & PPG Morphology Baseline Results.
Stat. Fentures
Demo
PPG
Demo PPG
PAPAGEI-S or
PAPAGEISS Demo
Classifiationn AUROC (t)
ICU Admission
0.71 J:46-67u
0.64 (osssss)
0.59 (15s4154)
0.66 (11-433
0.79 (177-40))
0.77 (004411)
Mortality
(ossosi)
0.66
0167
0.70 (u57-211
Smoker
0.63 (:56-667)
0.64 (0300220)
0.56 (1322402)
0.66 (1814311)
0.64 (t.5--uee)
0.62 (0.544.55)
Pregnancy stage
0.64 (x2.00
0.52 (:30v.55)
0.55 (0:5-0551
0.56 (.530
0.77 10.75 0:40
0.78 (6:6-.991
Hypertension
0.66
0.77 (o05-080)
0.53 [(884)4]]
0.77 (1554.5))
0.77 (240-0877)
0.80 (6.76.99)
SDB
0.52 (011-000
(151-
0.70 (1.57-054)
Mond Disturhance
0.54 (:310077
0.54 po soea
0.64 (u22
0.63 i (3550
0156 yt 30-377777
0.52 (025-:11
Valence
0.52 (0-0-000
0.57 po50-oo
0.44 (-11-
0.57 pussass)
0156 (:55--999
0.55 (057-555)
Arousal
R:53-00)
(:33
(0480.5
0.54 (0320.55)
(:35-6911
0.58 (6.40.39)
Regression- MAE ())
Apnca/Hyypppeaa Index 3%
15.31 (1965-17:14)
14.53 (1526-1554
15.09 (1401.16.4)
14.40 (3.2--661
12,97 (1.87-1000)
12.35 (11.7-1844))
Apnes/Hyypognca Index 4%
(1802-14144
12.28 (1119-12.9))
12.65 (1133-1333)
(11:0-13.39
(133-11.58)
Gestation Age
7.15 js (5.963 s4]
7.69 (Ta1-2:77)
7.61 (1.11-2.0)
7.59 (7.51-2:66)
6.05 (5.14.177)
6.02 (5.854.17)
Systelic BP (VV)
15.76 (1267-11000)
14.96 (13.21-17.38]
15.82 (13.46-88311
15.01 (15.00-7788)
14.65 (12.9416671)
14.27 (11:12-6444)
Diastolic (VV)
9.75
8.75
9. 0.20
8.78
8.29
8.26
Systolic BP (PPG-BP))
15 55
(1 (18-30225)
11 (1136-1533))
15,76 (15.36 10]
13.74 (11.37-16 66]
13,60 (6665-1651)
13,20 (11.47-1546))
Diastolic BP (PPG-BP)
9.35 17 1144-12000
9. 9.26 (12901806)
9.36 (138--1911)
9.28 (6.00-10.59]
8.71 p.re-1burt)
8.61 (7.34-088)
Average HR
7.01 (348-4.99)
9.12 (196-100111
8.07 [10866711
8.23 (6.520078)
3,47 (2.74-333)
4,00 (335-4229)
(12.0-13.233
15.18 (1805-15.333
16.75 [1600-1699)
14.46 (14.12-0463)
10.92 (1828431MM)
12.38 (1190012999)
Importantly, demographies do not add much already homogeneous populations. For example, consider the NuMoM2B dataset which has women within a specific age range. Here, we observe that PaPaGei obtains much higher AUROC and MAE than the supervised baselines.
Furthermore, We observe that adding demographics to PaPaGei-S embeddings improves over Pa- PaGei in the following tasks: Mortality (+0.03). Hypertension (+0.03), AHI > 3% (-0.62), AHI > 4% (-0.09), gestation age (-0.03), SBP VV (-0.38), DBP VV (-0.03), SBP (0.40), DBP (0.10), Based on these results, PaPaGei-S + Demo is a stronger model in many cases. Importantly, these results indicate that PaPaGei-S embeddings learn features that are complementary to demographics are not simply proxies for age or sex. However, it is important to note that while demographic features can be valuable for personalization, they may not always be readily available, and in reality, we cannot use them in isolation to predict real-time outcomes such as blood pressure or heart rate. Therefore, our PaPaGei models are designed to function effectively with real-time sensor data alone, ensuring their applicability in situations where complete demographic information is not accessible.
These findings underscore important point: demographic features not competing with Pa- PaGei but rather complement it, as previously established in studies including demographics with sensor data (Spathis et al., 2022). This highlights the synergistic potential of combining PaPaGei's advanced feature extraction with demographic context for improved task performance.
Predicting Demographics Targets. Using the PAPAGEI features, we predict downstream demo- graphics such as age and sex (Table 16). PAPAGEI-S achieves 7.78 MAE in age regression, 0.85 accuracy in age classification, and 0.79 accuracy in sex classification. Although our results trail larger closed studies (Abbaspourazad et al., 2023) by 2.18, 0,05, and 0.13 for segment-level SSL, and by 5.59, 12, and 0.25 for patient-level SSL, they mark an advancement open-source ef- forts. The superior performance of Abbaspourazad et al. (2023) can be attributed to two primary factors. First, the letient-level positive pair strategy achieves the best performance across all tasks. This approach encourages the model to form distinct clusters for each patient, effectively captur- ing demographic factors such as age and sex. In contrast, a segment-level approach pushes the model to cluster similar segments across individuals with varying demographics, potentially mixing demographic-specci information. Second, the single-device setup with a larger dataset is useful for effective model training (Table 17). Conversely, our evaluation, which spans three devices and utilizes smaller datasets, must handle greater data heterogeneity, thus making it more challenging.
F ADDITIONAL PREDICTION PLOTS
The regression plots to evaluate the agreement between true and predicted values in shown in Figure 25. From the Figure, we observe that PAPAGEI'S 's predictions are more aligned to the true values for AHI 3% (R²² 0.28), Avg. HR (R² 0.79), gestation age (R² 0.28), SBP (R² 0.36), and DBP (R² = 0.22). Moreover, from the distribution plots in Figure 25, we notice that PAPAGEI has stronger overlap for AHI > 4%, Avg. HR and DBP, indicating its ability to capture the tails for
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Table 16: Predicting personal characteristics with embeddings. Downstream prediction on age regression, age classification, and sex classification in our pre-training datasets (VitalDB, MESA, MIMIC-III). The regression and classification tasks are reported using MAE and AUROC, respec- tively. Note: training and testing are conducted with completely different cohorts in the two studies, hence comparisons are difficult.
Study
Age Regression ())
Age Classification (1) Sex Classification
Ahbaspourazad et al. (20t3)()atient)
3.19
0.97
0.99
Abbaspourazad et al. (2023)Seemmet)
6.60
0.90
0.87
PAPAGEI-S (Ours)
8.78 (8.47-8.09]
0.85 (0.83-0.87]
0.74 (0.72.0.76]
Table 17: Comparison of large-scale PPG studies. * indicates partial availability. The participants and hours indicate pre-training data.
Study
#Patticipants (#Hours)
#Devices (Types)
Open Data
Open Weights
Open Code
#Tasks (NDatasets)
Abbuspourazad (2023)
141,207 (333K)
(Smartwatch)
3-46
Ding et al. (2024)
28,539 (300K)
5-6 (ICU, Smartaatch)
X
x
4 (7)
Vun (2024)
170,714 (Varying)"
(Finger)
/
(4)
PAPAGRI (Ours)
13,517 (57K)
(IC,, Smanwatch, Finger, "Phone Ox.)
4
(10)
these tasks. Interestingly, notice that all models are unable capture the bi-modal nature of the gestation age measurements. Here, Chronos performs better than other methods to capture readings from the first visit.
G EFFECT OF SKIN TONE
We present the skin tone analysis in more granular way in Figure 26. Here, PAPAGEI-S clearly performs better than PAPAGEI-P in most cases. Overall, we notice that PAPAGEI-S is good for lighter skin tones in the 1-2 range for SBP and 2-3 range for DBP. While PAPAGEI-S does not perform the best for darker skin tones, it's performance comparable to other models for skin tone ratings of 4 and 5. Overall, these results indicate that PAPAGEI-S is relatively robust skin variations, and that additional future work is needed to make it better darker skin tones.
H EXTENDED RELATED WORK
Self-supervised learning (SSL) is the most prominent paradigm for learning general representations from large unlabeled datasets, including methods like SimCLR (Chen et al., 2020), BOOL (Grill et al., 2020). and masked autoencoders (MAE) (He et al., 2022). Timeseries-specffc objectives like TNC and TF-C have also shown promise (Tonekaboni et al., 2021; Zhang al., 2022). SSL has gained traction in the domain of physiological signal analysis, with applications to health records (Chen al., 2021; Yèche al., 2021), fitness and personalization (Spathis al., 2021), as well as brain (Cheng et al., 2020) and heart signals (Kiyassch et al., 2021; Sarkar & Etemad, 2020).
However, despite the popularity of SSL, there are no widely used FMs for PPG data. While (Ab- baspourazad et al., 2023) showcased the potential of foundation models for physiological signals, was based on single proprietary dataset and device (Apple Watch) while the models were not released, limiting its practical use in the research community. Similarly. REGLE's work (Yun et al., 2024) on the UK Biobank dataset showed that embedding PPG signals can improve genetic discov- ery and risk prediction outcomes. Although parts of that model and pipeline are public, the dataset is not openly accessible, and the primary goal was not to create a foundation model for PPG but rather to focus genetics. Another work the same data showed that PPG embeddings for cardiovascular risk prediction (Weng et al., 2024). SiamQuality (Ding et al., 2024) also trained unreleased model 36 million PPG signals using proprietary data. Importantly, most of these works pre-trained on a single-device dataset and did not explore out-of-domain datasets or conduct transfer learning experiments, which are crucial for assessing the true generalizability of foundation
promising
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Chronos
SimCLR
PaPaGei-S
100
100
0.96
AHI 48
Predicted
50
R² 0.16
m=
0.13
PL- 0.27
0.28
chrenes
SLmOLR
0,02
0.00
-20
20
40
60
80
100
120
(a) AHI 4% predictions
Prodicted HR
100
Moment
BYOL
PaPaGei-P
0.67
0.92
R
0.68
0.65
0.79
80
EYGL
0.02
PaPaGel-
60
60
0.01
0.90
80
00
100
60
80
100
60
80
100
True avg. a HR
Tuu avg. HR
True avg. HR
Avg. HR
(b) Avg. HR predictions
Prodicted Gestation Age
Chronns
m 0.22
BYOL
50
= 0.25
PaPaGel-S
0.31
R² = 0.28
30
30
1552 Sestation Ape
Chrores
20
SYOL
10
Gestation Age
True Gestatian Age
20
Gestation Age
6,000
12 Gcration Age
40
(c) Gestation Age
Predicted SBP (PPG-89) -B9))
180
Moment
TF-C
180
PaPaGei P
140
120
160
m
R²
0.02
140
R² 0.13
m= 0.19
140
R² 0,36
m = 0.29
Donsier
0.02
0.04
TF C
Moment
Systolic BP (PPG-8P)
100
100
80
80
80
0.00
(PPG-BP)
100
(PP)-BP)
100
(PPG-8P)
60
80
100
Systalic BP (PPS-8P)
120
140
140
180
200
(d) Systolic BP (PPG-BP)
Predicted (PPG-BP)
120
Moment
PaPaGel.P
0.15
== 0.008
= 0.25
100
R²
0.03
100
0100
Moment
80
80
80
0.59
BYOL PalaaGeii-P
60
60
60
0,25
0,000
True DBP (PPG-BP)
50
100 True DBP (PPG-BP)
75
50
True DBP (PPG-8P)
75
100
40
Disstolc BP (PPG BP!
120
(e) Diastolic (PPG-BP)
Figure 25: Regression plots and prediction distribution of different models compared to ground truth for (a) Apnea/Hyyoppea Index > 4%, (b) Average Heart Rate, (c) Gestation Age, (d) Systolic BP (PPG-BP), and (e) Diastolic BP (PPG-BP). R² is the coefficient of determination and m the correlation slope.
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Systolic BP (WV)
REGLE
Chronos
Moment
SimCLR
BYOL
TF-C
5
PaPaGei-P
PaPaGei-S
(Pale white)
2 (Fair)
(Darker White) 4 [Light Brown] Fitzpatrick Skin Tone Scale
(Brown)
(Black)
Diastolic BP (VV)
12.5
REGLE
10.0
Chronos
MAE
Moment
SimCLR
BYOL
TF-C
PaPaGei-P
PaPaGei-S
[Pale white)
(Fair)
[Darker WhiteM (Light Brown] (Brown) Fitzpatrick skin tone scale
(Black)
Figure 26: Detailed skin tone analysis for Blood Pressure estimation (VV dataset).
models. These studies highlight the potential of PPG-based foundation models but also underscore the need for openly available, pre-trained models that can be widely used and adapted by the research community.
On the other hand, generic time series foundation models have begun gain popularity, mirroring the trend seen in Large Language Models (LLMs). These models are pre-trained on massive corpora of diverse time series data, aiming learn universal representations that can be applied across various domains. For instance, Chronos (Ansari et al., 2024) was trained on an impressive 84 billion observations (analogous tokens NI.P) from 28 distinct datasets. However, it's notable that this diverse collection does not include physiological data. Similarly, Moment (Goswami et al., 2024) was trained on billions of observations from wide-ranging dataset that includes weather, traffic, energy, and other domains. While Moment does incorporate a small amount of ECG data, comprises only tiny percentage of the overall data pool.
In contrast to these generic approaches, work takes domain- specific focus. We curate large pre-training and evaluation benchmark dedicated exclusively to PPG data. While knowledge gained from generic time series foundation models may transfer domain-specific tasks like PPG, expect the performance to be limited compared to a model trained specifically on PPG data. Fur- thermore, foundation models for ECG (McKeen al., 2024; Song al., 2024) BEG (Yuan al., 2024b) have shown promise but transferring from one domain-specific model (e.g., ECG) to another (PPG) likely be even more challenging, different. For instance, Lai et al. (2023) trained large-scale 12-lead ECG model for detecting 60 diagnostic terms, while McKeen al. (2024) developed an open-source ECG FM using 1.6 mil- lion 12-lead signals. In brain signal analysis, Yuan et al. (2024b) introduced Brant-2, an EEG and SEEG model supporting tasks like sleep staging and seizure detection. Building on this progress, adopt
the underlying signal characteristics
approach focused on photopltthysmographhe (PPG) signals. Building the increasing interest in modality-specinn foundation models, our specialized approach allows us to capture nuances and complexities specific to PPG signals.
An increasingly popular approach involves feeding timeseries data and prompts directly to Large Language Models (LLMs) (Gruver et al., 2024). However, despite promising results, LLMs strug- gle with sigh-dimnnsional signals due to their text-based processing (Spathis & Kawsar, 2024). A modality-specific encoder like PAPAGEI addresses this limitation by providing representations of
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raw signals (Belyaeva et al., 2023), which can be combined with text and fed into more powerful multimodal foundation models, such as AnyMAL (Moon et al., 2023). This approach offers several advantages: computational efficiency through fixed LLM, flexibility due to the modular design of encoder, adapter, and L.M components, and interoperability with other high-performing models (e.g., state-of-the-ar IMU encoder (Yuan et al., 2024a)). Crucially, this encoder-LLLM approach does not require paired data with other modalities to train a single multimodal model. However, it may introduce complexity by limiting end-to-end gradient propagation and reduce interpretability in encoder-LLM communication compared to natural language prompts. Despite these trade-offs. PAPAGEI serves dual purposes: as a generic feature extractor for various PPG signals and applica- tions, and as a modality encoder in next-generation frontier models. This versatility positions it as valuable tool for advancing multimodal sensory Al systems.
I EXTENDED DISCUSSION
In $5.1, we observed that PAPAGEI outperforms baselines in at least 14 out of 20 tasks, with average classification and regression improvements of 4.7%-6.3% and 2.9%..49%, respectively. PAPAGEI- S performed best for cardiovascular parameters like BP, Hypertension, and Avg. HR, which are closely linked to metrics such sVRI and IPA (Liang et al., 2018b). Additionally, PAPAGEI-P surpassed baselines FMs like Moment and is well-suited for tasks such as Smoking and Arousal.
By ablating different components of PAPAGEI-S (Section 5.2). found that the full model per- forms best, with sVRI contributing the most. Adding IPA or SQI separately did not improve per- formance, suggesting that (a) IPA and SQI positively transfer in multi-task setup, and (b) design choice to include both to compensate for situations where IPA cannot be computed is effec- tive (Section 3.2). While combining PAPAGEI-P and PAPAGEI-S may seem intuitive, constraining positive pairs on both sVRI and participants leads to too many unique labels with limited samples. In our scalability analysis, we observed that the smallest model (5M parameters) outperformed others, aligning with other studies using CNNs with 3.3M parameters for biosignals (A (Abbaspourazad et al., 2023), likely due to the size of PPG datasets. Larger models like Chronos or Moment are impractical for wearables due to their size and privacy concerns with bloud-based inference for health data. Ad- ditionally, PAPAGEI-S more data-efficient for linear probing, showing greater performance gains with increased data availability, making it promising backbone for small studies in future research.
Our studies in Section 5.3 reveal that PAPAGEI-S embeddings are more dispersed across partici- pants, enhancing performance, while regression predictions more accurately reflect the true distribu- tion. We attribute this to our positive pair selection, which chooses positive pairs across individuals based on sVRL Moreover, our skin tone analysis shows that the method performs better lighter skin tones, likely due to the model being trained predominantly on such data. For darker skin tones, performance was similar across models for diastolic BP, with REGLE and BYOL performing best, highlighting the need for future work creating more robust models for diverse skin tones.
To provide future direction regarding the use of PAPAGEI, provide some suggestions. For in- stance, let's consider the nuMoM2B dataset which consists of pregnant women. PAPAGEI-S obtains an AUROC of 0.78 in pregnancy stage classification and 6.05 is gestation age classification. Com- pared to the pre- pre-training population with diverse age and gender, the nuMoM2B consists of women generally aged between 20-35. Furthermore, the gestation age readings are collected approximately around the first and third trimester. Given these factors, the target nuMoM2B dataset has many vari- ables contributing toward distribution shift. Therefore, PaPaGei-S can be fine-tuned to address the shift in the following ways: (1) We can align the pre-trained embeddings to the nuMoM2B embed- ding using unsupervised or semi-supervised domain adaptation. (2) Domain Generalization is also an option during the training phase to improve generalization robustness. (3) Newer methods such as LoRA can provide another way to quickly fine-tune. (4) Importantly. given that more women are present in the first visit compared to the third visit, we can optimize different metrics to improve accuracy under the imbalance. For example, AUPRC can be optimized instead of AUROC. Fairness of classification across genders can also be considered during training. Exploring these avenues to further enhance the performance and applicability of PaPaGei is promising direction for future studies. Moreover, future work may benefit from exploring PPG specific augmentations such as GAN-based approaches (Kiyasseh et al., 2020); and systematically evaluating different augmenta- tions to provide insights into useful PPG augmentations.
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Reviews:
[{"review_id": "ybWg2WKWfQ", "reviewer": "Reviewer_DtmN", "summary": "This paper proposes two open foundation models for PPG signals, PAPAGEI-P (patient-aware) and PAPAGEI-S (morphology-aware). These models mainly differ in their training strategy—one uses a self-supervised approach to maximize agreement between embeddings of the same patient and the other maximizes the agreement between signal features across patients. The two models are tested on several datasets to evaluate their performance on different tasks, including binary and multi-class classification and regression, and they are compared with other general foundation models and SSL approaches. Ablation studies are carried out to understand the performance impact of the datasets used for pre-training, the impact of each model component, the effect of different levels of labeled data, and model size and scaling. Some case studies are also presented to evaluate different subject- and data-related aspects.", "questions": "1. Do you have any suggestions to improve the model's performance in tasks where performance is low or when class imbalance is an issue?\n2. Regarding the out-of-domain datasets, do they all use different devices from the ones in the pretraining datasets?\n3. And have you looked into other biases that could be present in the datasets (sex, age, gender, weight, health conditions)?\n\nSome other suggestions:\n- Report the train and validation training loss curves for both models would help understand the convergence stability in both approaches\n- Make sure to reference all the appendices in the main text", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "1. The paper claims to propose the first open foundation model for PPG signals, which tries to improve the current models' performances using publicly available data and data augmentation, while also being relatively light-weight (~5M parameters)\n2. The paper is well-organized, rich in technical details regarding the data preprocessing, model architecture and training, and performs extensive testing on different tasks. It also includes ablation studies that prove the performance gains of each component. \n3. The methods and results are well explained and covered, and are accompanied by relevant tables and figures to facilitate comprehension and readability\n4. The framework presented can have practical implications for cardiovascular monitoring and be used for several applications", "weaknesses": "1. While performing slightly better in some tasks relative to other foundation and SSL models, the proposed models lack comparison with task-specific non-SSL models. Despite including statistical feature models for baseline, which can give a basic comparison, state-of-the-art task-specific models, with or without engineered features, are not included. Such comparison would provide insights into whether the SSL approach yields better representations than supervised-learning ones.\n2. Task performances are evaluated using AUROC and MAE, but the manuscript doesn't seem to comment on the F1 scores and R2 in some tasks, which are often related to class imbalance and yield poor results. Addressing this topic or proposing a strategy to handle these cases would be appreciated.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "7JYgeO5R2N", "reviewer": "Reviewer_MitC", "summary": "This work proposes foundational models for photoplethysmography (PPG) signals. The presented model is trained more than 57,000 hours of 20 million unlabeled segments of PPG signals using publicly available datasets. Authors evaluate the model on 20 tasks of 10 diverse datasets including cardiovascular health, sleep disorders, and pregnancy monitoring.", "questions": "1) I checked the code, but I could not find the processing and evaluation of DaLiA. Could the authors specify the path to it? Thanks!\n\n2) The authors stated that \"Additionally, we did not perform an exhaustive evaluation of different augmentation settings but instead used transformations and values based on prior research  (Abbaspourazad et al., 2023; Tang et al., 2020).\". The importance of the augmentations are well-known in self-supervised learning, especially depending on the application. The application of the second reference is inertial measurements instead of PPG signals. And, only the cropping and Gaussian noise are similar with the first reference. Is there a specific reason why PPG-based data augmentations are not explored?", "limitations": "", "rating": 1, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 1, "strengths": "The motivation of the works regarding the open and public foundational models with datasets is important. The paper focuses on applications of healthcare with monitoring which is relatively less studied in machine learning community. The authors perform the skin tone analysis, which can be important to evaluate fairness.", "weaknesses": "The technical contribution of the paper is limited. All the components, extracted features, architectures, and augmentations, of the framework are not novel. Only the integrated loss values seem novel in training but there is no clear motivational evidence for this integration. Worse, extracting those features is not trivial for noisy PPG segments, especially the sVRI and IPA as they depend on the waveform. This limits the training to clean datasets, which are collected under very limited motion, including ICUs. For example, VitalDB, one of the datasets that authors used for pre-training, is collected during surgery. This is a significant drawback in terms of the generalization of the framework.\n\nSecond, the manuscript does not explain why this framework (extracting waveform features and using them during training) performs well. For example, DaLiA is a noisy dataset where extracting those morphology features is extremely prone to error. Although DaLiA is not used for training, the manuscript does not explain why these features help extract useful representations from noisy datasets.\n\n\nThird, the extracted statistical features are extremely basic (mean, median, maximum, minimum, and percentiles), but their performance is very close to the proposed model in several tasks (most confidence interval overlaps). Compared to these features with a neural network of 5M parameters, the results are not significant, which makes the usefulness of the presented work controversial.  \n\n\nFourth, SimCLR and BYOL are self-supervised learning frameworks that allow someone to change the encoder architecture. In the comparison, BYOL has 12M parameters. Why not explore other networks to match the parameter count and then compare? \nFor example, the authors did an ablation study regarding the model size of their network and stated \"As shown in 5c, the smallest model with 5M parameters performs best in all tasks except one, indicating that smaller models are better suited for PPG data.\" According to this statement, small models with other self-supervised baselines might perform better than the presented work. This statement by the authors raises significant questions about the fairness of the evaluation. \n\n\nFifth, some statements in the manuscript are too powerful, if they are not completely wrong. For example, the authors stated \"PAPAGEI-S uses cropping (0.25) and Gaussian noise (0.25). PAPAGEI-S avoids augmentations that alter PPG’s morphology.\" Adding a Gaussian noise would change the morphology of the features. If the morphology refers to the waveform shape of the signal in the manuscript. All the applied transformations should have zero or linear phase response, Gaussian noise would change the phase values nonlinearly so the statement is wrong. Another statement \"We propose PAPAGEI-P, a patient contrastive approach that maximizes agreement between signals from the same subject.\", the subject/patient wise contrastive learning is already proposed by previous works (i.e., CLOCS,  ICML 21). Proposing a method that is already out might mislead the readers, the language of the manuscript needs a major revision.\n\n\n---\n----Post rebuttal----\n\nI have decreased my rating from 3 to 1 after identifying significant technical flaws in the implementation.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "KdW7zLequ1", "reviewer": "Reviewer_XN3M", "summary": "This paper provides an open foundation model (open code, data, model) for a physiological signal, PPG. The authors have pre-trained a model on a combination of 3 open-source PPG dataset, and evaluated on various datasets for various downstream targets. The comparisons with some off-the-shelf time-series models, and pre-training algorithms have been presented, and several ablations studies regarding different components have been done.", "questions": "* Do all datasets contain single-channel PPG? If yes, it is worth mentioning in the paper.\n* It is interesting that combining three pre-training datasets, does not provide much additional information regarding downstream targets. What’s the authors’ hypothesis? It is worth adding a discussion to the paper\n* I am not sure about this statement: “Interestingly, in single datasets, MESA performs the best, despite having the fewest participants but the highest number of segments, echoing similar results in the language model domain (Dubey et al., 2024).”. Do the authors have a justification for why they think this is relevant to Llama3? If yes, please add to the paper, and if not, please consider changing the language. \n* What’s the authors hypothesis for PaPaGei-S being better than PaPaGei-P in general? I find it very surprising that defining the positive pairs based on quality of the segments (and not subjects) improve the performance. In fact, one could argue PaPaGei-S should have a slightly worse performance, because it does not leverage pairing good quality and bad quality segments. Did the authors investigate the performance of a few downstream targets, w.r.t the change in segment quality in PaPaGei-P vs. PaPaGei-S?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "* While pre-training foundation models for PPG has been done before with closed-source models/datasets some with open-source and some with closed-source code, this work presents the first open-source foundation model for PPG, trained on open datasets, with open-source code, that can foster the use of PPG for health applications and for health research. \n* The paper, in my opinion, is well-written. I enjoyed reading the paper, the comparisons are well-presented and the paper is easy to follow. I would like to thank the authors for their efforts in the quality of the analyses and presentations in the figures.  \n* I really appreciate the level of details in the paper including the ablations studies; authors did a good job for providing good level of information and detailed ablations for a curious reader.", "weaknesses": "* One of my main comments is regarding the novelty of the presented work, the language in the paper with respect to the prior work, and lack of comparison with the prior work in terms of performance. It appears to me that the idea in this paper resembles significant similarity to a prior work [1] (which is cited in this work multiple times) in terms of: 1) training a PPG foundation model on a large-scale dataset, 2) methodology in terms of loss, positive pairs, random augmentations, 3) evaluation across multiple targets, and demonstrating that one PPG foundation model is strongly predictive of a wide array of downstream health conditions. The main distinction of this work is openness of dataset, code and model. I do appreciate the openness aspect, however, the language in the paper sadly does not properly reflect the relationship to the prior work. For example, the only mention is in the introduction/related work is “Recent studies have shown promise in this area, as (Abbaspourazad et al., 2023) demonstrated that embeddings from ECG and PPG signals can generalize across multiple health-related tasks using proprietary Apple Watch data.”, which does not reflect the proper relationship between the two papers and the 3 points stated above. I highly recommend authors to reflect the appropriate relationship upfront for fairness:\n    * It is also surprising to me that throughout the dataset/results, there’s no comparison table to the prior work [1] in terms of performance and choices of PaPaGei. Given the direct resemblance of the 2 works, my suggestion to the authors are: \n         * Putting a table/discussion in the methods/datasets for comparison of dataset used in this study with respect to the prior work for PPG foundation models [1] in terms of number of subjects, amount of data, openness vs. closedness, number (and kinds) of different devices and etc. In addition, comparison of the (only major) training choices, size of models, compression ratios (how many channels, how many seconds of input compressed to what embedding size). \n         * Putting a comparison results evaluation table/discussion for major downstream targets in comparison to reported numbers in [1], at the very least for demographics comparison such as age/sex/body mass index, whose evaluations are missing in the current manuscript (they are important!), and has not been compared to the closed work [1]. This will demonstrate the effectiveness of PaPaGei training/model, and provide the performance gap (if any) with closed source models (authors can note that evaluation datasets are different).\n    * Also, the language with respect to another prior work [2] can be enhanced. Pre-training PPG models where positive pairs are with respect to signal quality was done prior to this work but there was no direct mention of this in the paper [2]. Moreover, this prior work has open-source code  (closed-source dataset) which was again not mentioned properly. I suggest authors consider citing these prior work properly. \n    * In addition, the idea of pre-training foundation models on other physiological signals on large datasets in the related work section is missing. Papers such as [3] for ECG and [4] for IMU (just examples) should be added. \n    \n* One of my main concerns about the reported evaluations is the significance of the reported metrics in downstream comparisons. To provide an example, in Table 3, authors provide point estimates and confidence bounds, but for a lot of the targets, the confidence bounds are very wide and overlapping across methods. For instance, for “smoker”, it is not clear to me whether PaPaGei-P is significantly better than Chronos. My suggestion to the authors are:\n    * This begs the question about the effectiveness of PaPaGei, given that an off-the-shelf time-series model that was not trained on PPG (Chronos), can capture similar amount of information regarding an important vascular target (smoking), therefore providing discussion would be valuable for the readers.\n    *  Providing P-values and n for significance comparisons is essential. Similar comment for other comparisons such as Table 4, Figures 4, 5 and 6.\n* In terms of comparison with SimCLR and BYOL, the authors can do a better job explaining the training choices and implementation, and demonstrate fair comparisons. They say “It is noteworthy that both BYOL and TF-C require multiple encoders and different projection heads, resulting in variations in model sizes. For these methods, we use existing implementations available online, but apply our encoder as the backbone to ensure consistency.”. I appreciate that they used similar encoder for fair comparisons, but other implementation details is not clear to be identical. My suggestions/questions to be clarified in the paper are:\n    * What details are the same and what details are different? (for example positive pairs, augmentations, batch size, learning rate, and any other useful details to demonstrate fair comparisons). \n    * Also kinda related to this, there’s no mention of how and what temperature was selected.\n\n\n[1] Abbaspourazad, S., Elachqar, O., Miller, A. C., Emrani, S., Nallasamy, U., & Shapiro, I. (2023). Large-scale training of foundation models for wearable biosignals. arXiv preprint arXiv:2312.05409.\n\n[2] Ding, C., Guo, Z., Chen, Z., Lee, R. J., Rudin, C., & Hu, X. (2024). SiamQuality: a ConvNet-based foundation model for photoplethysmography signals. Physiological Measurement, 45(8), 085004.\n\n[3] Lai, J., Tan, H., Wang, J., Ji, L., Guo, J., Han, B., ... & Yang, W. (2023). Practical intelligent diagnostic algorithm for wearable 12-lead ECG via self-supervised learning on large-scale dataset. Nature Communications, 14(1), 3741.\n\n[4] Yuan, Hang, et al. \"Self-supervised learning for human activity recognition using 700,000 person-days of wearable data.\" NPJ digital medicine 7.1 (2024): 91.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "01tVh7twwq", "reviewer": "Reviewer_d898", "summary": "This paper reports on development and downstream testing of a pair of new foundation models (PaPaGei-P and PaPaGei-S) for photoplethysmography (PPG) signals in comparison to several pre-trained models and alternative pre-training methods.  All pre-training and downstream evaluation was accomplished using publicly-available PPG data sets, and the authors have shared their codebase to enable end-to-end reproducibility of the work reported in the paper.   In addition to the open codebase and use of public data sets for model development (which is itself new for published PPG foundation models) the novelty of this paper includes the incorporation of PPG-specific physiologically-informed loss functions during the pre-training phase.  \n\nThe authors perform evaluation of the PPG encoder generality and utility using a battery of 20 downstream tasks, some of which have been published previously (for example blood pressure estimation and hypertension classification) and others of which are new (for example gestational age estimation).   They report performance comparison for their models against a pair recently published time series foundation models (Chronos and Moment), against an existing PPG-specific pre-trained model (REGLE), against three well-established self-supervised learning methods (SimCLR, BYOL, and TF-C), and against a baseline approach consisting of simple signal statistics fed into random forest models. \n\nIn addition to the performance comparison with other models and modeling approaches, the authors also report model size analysis, model sensitivity to skin tone, and systematic ablation analysis (evaluating the benefit of each source data set and PPG-specific loss function used in pre-training).", "questions": "The paper is generally very well-written (in terms of both clarity and technical content) and overall I enjoyed reading it. I have a few additional comments/questions regarding style and content: \n\n1. Reporting the average MAE across tasks in Tables 3 and 4 (and in Figure 4) may not be the best way to summarize the regression performance, since the regression targets have several different units (e.g. mmHg, bpm, and weeks).   To provide a ‘fair’ summary across all the regression tasks with different units, it would make more sense to use a unitless metric such as mean absolute percent error or (possibly) the mean F-statistic across all tasks. \n2. In Figure 5a/b and 6, it is not obvious to the reader whether these differences are statistically significant.  Were any tests for significance performed?  If so, it would be helpful to report this briefly (simply using the name of the test and stating the significance level).\n3. Expanding on Figure 5a/b (component ablation) it would be informative to see a summary of the performance changes across all downstream tasks, which may provide some insight into which PPG-specific pre-training losses are influential for certain tasks and not others.  This could be an additional appendix table (it doesn’t need to fit into the main text).   \n4. Figure 5c shows that, on average, the middle-sized (35M) parameter model has the worst performance while the smallest (5M) parameter model has the best performance.  Were models smaller than 5M evaluated?  Including performance for models smaller than 5M (perhaps going as small as 0.07M , the size of REGLE) would give an indication of whether 5M truly represents the optimal model size.  This would also make the overall trend more clear (a non-monotonic trend for only 3 model sizes is hard to trust). \n5. I like the analysis of inter-participant embeddings (section 5.3 and Figure 7) since this is agonistic to subject-level tasks.  However, looking at Figure 7 it feels “wrong” that PaPaGei-S has some non-negligible density for distance <0.  Is this an artifact of distribution smoothing?   As an alternative to the pairwise distance distribution, it could also be informative to evaluate other measures of unsupervised embedding quality such as Rankme (or similar).  \n6. I’m not sure if I understand some of the language used in the Skin Tone Analysis section, specifically the phrase “however, there are no significant differences across all models” in reference to Figure 9.    Does “no significant differences across all models” refer to a comparison of light skin tones vs. dark skin tones (grouping models), or does it refer to model-to-model differences for dark skin tones vs. light skin tones, or to something else?  It would help if this can be stated with more precise language.  \n7. For Figure 9, it would also be informative to plot the confidence intervals as whiskers on the bar plot.  This would make it more clear (visually) whether the model-to-model differences are significant.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "## This paper represents strong contributions in the following areas: \n### Originality: \nAlthough the idea of foundation models is not new, there have been few published examples of foundation model development and evaluation for physiological signals such as PPG.  Here, in addition to comparing the performance of multiple pre-trained time series foundation models, as well as adapting different contrastive and momentum-based SSL methods, the authors have also incorporated some PPG-specific elements into the pre-training (loss functions for SQI, sVRI and IPA) that have not been reported elsewhere.  \n\n**Furthermore the authors should be commended for two important things which, combined, have not been published previously:**\n1. They performed all training and downstream evaluation using data sets that are publicly available, enabling straightforward reproducibility and verification of their results (as well as use of their models by other researchers for future work).  \n2. They shared their codebase directly in the submission, which is organized and sufficiently well-commented to understand in a reasonable amount of time. \n\n### Clarity\nThe paper is well written, with clear descriptions and explanations in several key areas: \n1. Data set provenance (Appendix B is very helpful), and the purpose(s) for which each data set was used;\n2. Data pre-processing;\n3. High-level strategy and technical details of the pre-training method and loss/objective functions;\n4. Downstream task evaluation and performance reporting (the subset of cases that I checked were straightforward to evaluate and understand);\n5. Analysis that was performed in addition to performance reporting on downstream tasks, such as dispersion of subject embeddings and sensitivity of some tasks to skin tone. \n\n### Significance \n1. The exploration of PPG-specific dimensions of the SSL training is valuable and important, as well as the authors’ comparison of their model with off-the-shelf time series foundation models and established (but not PPG-specific) self supervision methods.  This has the potential to impact work in other areas at the intersection of machine learning and physiological sensing. \n2. PPG foundation models, in particular open/reproducible foundation models, have the potential for high impact in both health research and practical applications. \n\n### Quality\nClaims and conclusions were generally well-reasoned and justified, with some key exceptions (discussed in the following section).", "weaknesses": "# Brief Summary #\nThis paper had some important weaknesses that are listed briefly below, with more detailed comments and some suggested steps to address these weaknesses following the list:\n1. Absence of downstream evaluation on basic demographic targets such as age, height/weight, BMI or sex. \n2. [Most concerning] Inappropriate choice of baseline model (RF using basic signal statistics) for performance comparison with their models. \n3. In the Case Studies section discussing the downstream AHI regression performance, the authors do not provide good evidence indicating that the model captures the distribution tails well. \n\n\n\n# Additional Discussion Details #\n\n## Issue 1: Absence of downstream evaluation on basic demographic targets\nPPG is known from past publications (including at least one cited by the authors) to capture information highly predictive of age, body mass index (BMI) and sex.  These demographic factors are also highly predictive of additional health targets, indicating that a data-driven model may use demographic features as prediction ‘shortcuts’, rather than utilizing features that are specific to the health target of interest.  Performance of the authors’ models (and other models) in predicting demographic targets can give an indication of how well the models capture information relevant to these tasks, as well as provide some clues as to whether the models may be using demographic-related features as ‘shortcuts’ for other downstream tasks. \n\n### Suggestions to address Issue 1:\nSeveral of the data sets used for downstream evaluation have sufficient demographic metadata and subject numbers to train and test downstream tasks for age classification/regression, BMI classification/regression, and sex classification (even after reserving ~20% of data for testing).  Using the appropriate data sets (for example just MIMIC-III, MESA and VitalDB) the authors should compare performance from their PPG models against the other models (Chronos, REGLE, SimCLR, BYOL, etc).   To avoid any issues associated with splitting the pre-training data into train/val/test, this analysis could also be accomplished in combination with the pre-training data ablation experiments (e.g. reporting demographics prediction results only for one of the three large data sets). \n\n\n## Issue 2 [Most concerning]: Inappropriate choice of baseline model\n\nThe choice of baseline model consisting of RF with PPG signal statistics represents an inappropriately weak baseline.  In a few ways this approach is even weaker in practice than as stated in the paper (for example the signals are z-scored in pre-processing, meaning that the signal mean is guaranteed zero for all segments and therefore provides no information to the RF model).  Additionally, the section describing the additional PaPaGei-S objectives highlights the physiological relevance of the IPA, SVRI, and SQI (and the authors seem to have all the code/tools necessary to perform these calculations across a large number of PPG segments), which would provide a simple strategy to improve the statistics-driven baseline model. \n\nLastly, as mentioned for the previous item (Issue 1) PPG is known to capture information highly predictive of age, body mass index (BMI) and sex, with the corresponding features then used by downstream models as ‘shortcuts’ for predicting a variety of targets that have demographic dependence.  It would be highly informative to include a simple baseline model that predicts each target value/class using the demographic factors as inputs (the model could be LR, or Ridge, depending on the target task).  \n\nThe reason that this incorporation of baseline models with demographic inputs (producing a ‘stronger’ baseline for comparison) is important is that it can help **reveal whether the PaPaGei models truly represent a meaningful breakthrough** or are simply equivalent to (or even worse than) much simpler established approaches.  \n\n\n### My Findings regarding demographic (and constant/mean) predictor performance on a subset of the public PPG datasets: \n\nI appreciate that the authors utilized public and (in several cases) conveniently-accessible data sets for their downstream evaluation. This made it possible for me to spot check the performance of some simple baseline models on several of the tasks.  Due to time limitations I could not do this analysis for every task, but am reporting observations for tasks associated with the data sets PPG-BP (Liang, etc al. 2018), and ECSMP (Gao et al., 2021), which covers the following downstream tasks: SBP (R), DBP (R), Avg HR (R), Hypertension (C), Mood Disturbance (C).  For these tasks I was able to achieve the following performance using either a constant mean predictor (only for DBP regression task) or a demographics-based predictor incorporating Age, BMI, sex (where available): \n\n1. **DBP** Estimation (regression): I observed **MAE = 8.71** (identical to PaPaGei-S performance, **better than PaPaGei-P performance)** using a constant mean predictor; I observed **MAE = 8.07 (better than all models)** using a demographics-only predictor (OLS fit with age, sex, BMI up to quadratic terms). \n2. **SBP** Estimation (regression): I observed **MAE = 13.56 (better than evaluated models including PaPaGei)** using a demographics-only predictor (OLS fit with age, sex, BMI up to quadratic terms), and **MAE = 12.48** using demographics + heart rate (OLS fit)\n3. **Avg HR** Estimation (regression): I observed **MAE =8.11 (much worse than any reported model)** with a demographics-only predictor. \n4. **Hypertension** (classification):  Using the same class grouping reported by the authors, I observed **ROC AUC = 0.78 (better than all evaluated models including PaPaGei)** with a demographics-only LR predictor (age, sex, BMI, HR up to quadratic terms)\n5. **Mood Disorder** (classification):  Using the same class grouping reported by the authors, I observed **ROC AUC = 63 (better than all evaluated models including PaPaGei)** with a demographics-only LR predictor (age and sex up to quadratic terms)\n\nI did not personally check any other data sets or tasks due to time limitations.  Despite the caveat that for the results above I did not split the data into train/test sets, for 4 our of 5 cases the performance from these much simpler models (at most 15 parameters) was better than the models having millions of parameters.  Combined with the knowledge that PPG captures significant demographic information, this finding is strongly suggestive that the models may simply be using demographic-related features as ‘shortcuts’ for the target task. \n\n\n### Suggestions to address Issue 2:\n1. At a minimum, the RF baseline model should incorporate additional easy-to-derive signal features such as IPA, SVRI, and SQI (other standard pulse waveform metrics are also reasonable to include if time permits— see  Elgendi, M., Liang, Y., & Ward, R. (2018). Toward Generating More Diagnostic Features from Photoplethysmogram Waveforms. Diseases, 6(1).)   This would provide a more effective baseline model without adding any learned feature extraction. \n2. In place of (or in addition to 1 above), it is strongly recommended that the authors also compare with a baseline model consisting of demographic factors as inputs (the model could be LR, Ridge or OLS depending on the target task).  Including heart rate as a predictor in addition to demographic variables would also be a reasonable comparison, since all models appear to be fairly good at estimating heart rate from PPG.  \n\n\n## Issue 3:  Poor evidence for regression models capturing the distribution tails. \n\nI disagree with the authors that the analysis and plots in Figure 8 (particularly 8a) provide evidence that the predicted values match the distribution of the true values.  For the plots in Figure 8a the regression slope is clearly much less than 1.0, indicating that the models tend to over-predict for small True AHI data points and tend to under-predict for large True AHI values (i.e. significant model bias present)\n\n\n### Suggestions to address Issue 3:\n\n1. For the plots in Figure 8a, use the same range for the x- and y-axes.  Overlay the true and predicted AHI distributions on a common axis (can be a separate plot), which will make it more clear that the distributions are not well matched. \n2. Report the regression slope in addition to the R^2 value.  The slope is likely to be much less than 1.0 for all models (although likely highest for PaPaGei), indicative of model bias. \n3. As an alternative (or in addition) to the truth-vs-predicted scatterplots, also include a Bland-Altman plot. Bland-Altman is a straightforward way to illustrate whether a prediction model is biased. \n4. Repeat the same Bland-Altman plots for the other regression tasks (gestational age and BP).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)