PaPaGei: Open Foundation Models for Optical Physiological Signals

## Abstract

Abstract Photoplethysmography (PPG) is the most widely used non-invasive technique for monitoring biosignals and cardiovascular health, with applications in both clinical settings and consumer health through wearable devices. Current machine learning models trained on PPG signals are mostly task-specific and lack generalizability. Previous works often used single-device datasets, did not explore out-of-domain generalization, or did not release their models, hindering reproducibility and further research. We introduce PaPaGei , the first open foundation model for PPG signals. PaPaGei is pre-trained on more than 57,000 hours of 20 million unlabeled segments of PPG signals using publicly available datasets exclusively. We evaluate against popular time-series foundation models and other benchmarks on 20 tasks of 10 diverse datasets spanning cardiovascular health, sleep disorders, pregnancy monitoring, and wellbeing assessment. Our architecture incorporates novel representation learning approaches that leverage differences in PPG signal morphology across individuals, enabling it to capture richer representations than traditional contrastive learning methods. Across 20 tasks, PaPaGei improves classification and regression performance by an average of 6.3% and 2.9%, respectively, compared to other competitive time-series foundation models in at least 14 tasks. PaPaGei is more data- and parameter-efficient than other foundation models or methods, as it outperforms 70x larger models. Beyond accuracy, we also investigate robustness against different skin tones, establishing a benchmark for bias evaluations of future models. Notably, PaPaGei can be used out of the box as both a feature extractor and an encoder for other multimodal models, opening up new opportunities for multimodal health monitoring 1 1 1 Models, data pipelines, and code are available at: https://github.com/nokia-bell-labs/papagei-foundation-model .

## 1 Introduction

Photoplethysmography (PPG), a non-invasive optical sensing technique, is widely used for monitoring cardiovascular health and physiological signals in both clinical and consumer health applications (Charlton et al., ). From hospital pulse oximeters to smartwatches, PPG enables continuous health monitoring in various settings, bridging acute medical care and long-term health management. PPG signals can track a diverse range of health indicators, including cardiovascular health, blood pressure, mood, and sleep disorders (Sadad et al., ; Ave et al., ; Temko, ; Reiss et al., ; Liang et al., ; Haddad et al., ; Schrumpf et al., ).

Despite its widespread adoption, PPG presents significant challenges for machine learning (ML). These include high annotation costs due to the need for domain-specific expertise, especially in consumer health applications where recording conditions and subject heterogeneity vary greatly. PPG signals are also susceptible to noise and motion artifacts (Afandizadeh Zargari et al., ), and inherent variability due to factors like skin tone and body composition (Bent et al., ) complicates the development of generalizable ML models. Consequently, existing PPG datasets are often small, task-specific, and limited in their generalizability, posing a major obstacle to the development of robust and widely applicable models that could fully leverage the potential of this technology.

Figure: Figure 1: PaPaGei Overview. We curate public datasets of diverse PPG signals, and train a foundation model leveraging a novel morphology-aware contrastive learning approach. To evaluate its effectiveness, we apply the embeddings generated by PaPaGei to 20 tasks from 10 different datasets.
Refer to caption: x1.png

The PPG domain lacks general-purpose foundation models, unlike language and vision domains, with most work focused on single-dataset task-specific models. Although PPG signals detect vital signs like heart rate variability and blood oxygen saturation (Abbaspourazad et al., ; Charlton et al., ), the absence of generalizable pre-trained models limits progress. While there are difficulties in obtaining large-scale, high-quality datasets, recent increases in heterogeneous PPG data offer new opportunities (Johnson et al., ; Zhang et al., ; Lee et al., ). To address these challenges, we introduce PaPaGei, a set of robust, pre-trained models capable of serving as a backbone for various PPG-related tasks, capturing rich PPG representations through large-scale pre-training.

Our key contributions are: (1) Large-scale pre-training for PPG signals: To our knowledge, PaPaGei is the first open foundation model pre-trained on PPG signals, using 57,000 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 self-supervised learning framework: We introduce a novel self-supervised learning framework with a unique PPG signal morphology augmentation module. Our approach optimizes agreement between PPG signals with similar blood volume changes while learning signal quality and ratio around the dicrotic notch, a key PPG marker. (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’s 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 components, 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 (SSL) has become a prominent paradigm for learning general representations from unlabeled datasets, with applications in physiological signal analysis including health, fitness, and brain signals (Tonekaboni et al., ; Zhang et al., ; Chen et al., ; Yèche et al., ; Spathis et al., ; Cheng et al., ; Kiyasseh et al., ; Sarkar & Etemad, ). Despite SSL’s popularity, there are no widely used pre-trained models for PPG signals. Recent studies have shown promise in this area, as (Abbaspourazad et al., ) demonstrated that embeddings from ECG and PPG signals can generalize across multiple health-related tasks using proprietary Apple Watch data. Similarly, (Yun et al., ) showed that embedding PPG signals can improve genetic discovery and risk prediction outcomes using the UK Biobank dataset. Other works (Weng et al., ; Ding et al., ; Zhou et al., ) explored PPG embeddings for various applications. However, these studies often used single-device datasets, did not explore out-of-domain generalization, or did not release their models, highlighting the need for openly available, pre-trained PPG foundation models.

While generic time-series foundation models like Chronos (Ansari et al., ) and Moment (Goswami et al., ) have gained popularity, they often lack significant physiological data representation. In contrast, our work takes a domain-specific approach, focusing exclusively on PPG signals. We argue that while knowledge from generic models may transfer to PPG tasks, performance is likely to be limited compared to a PPG-specific model. Similarly, transferring from other domain-specific models (e.g., ECG (McKeen et al., ; Song et al., ) or EEG (Yuan et al., )) to PPG is challenging due to differences in signal characteristics. Our specialized approach aims to capture the nuances and complexities specific to PPG signals, building on the increasing interest in modality-specific foundation models. See Appendix §F for more detailed discussions.

Figure: Figure 2: PaPaGei-S Overview. Before training, for each PPG segment we compute the morphology metrics IPA, SVRI, and SQI, to be used as objectives (left). Next, we pass raw PPG signals through the encoder ($E$), to extract embeddings (middle) and feed them into three heads (right): the projection head ($P$) contrasts similar and dissimilar PPG signals based on sVRI, while two mixture-of-expert heads ($M_{1}$ and $M_{2}$) refine the embeddings by predicting IPA and SQI values, respectively.
Refer to caption: x2.png

## 3 Methods

Given a dataset $\mathcal{D}=\{\mathbf{p}^{1},\mathbf{p}^{2},\cdots,\mathbf{p}^{S}\}$ representing diverse PPG signals from $S$ subjects, a PPG signal $\mathbf{p}^{s}\in\mathbb{R}^{n}$ is defined as a time series that models the changes in light intensity due to arterial blood flow. To model granular changes in PPG signal of each subject $s$, we segment $\mathbf{p}^{s}$ without overlap to obtain $X^{s}=\{\mathbf{x}_{1}^{s},\mathbf{x}_{2}^{s},\cdots\mathbf{x}_{N}\}$. Here, the number of segments $N$ depends on the sampling frequency ($f$) and the desired length of time window.

We use self-supervised learning to train PaPaGei’s foundation model. We propose PaPaGei-P, a patient contrastive approach that maximizes agreement between signals from the same subject. Importantly, we propose PaPaGei-S, a morphology-aware self-supervised approach that maximizes agreement between signals with similar morphology. We present the details of both approaches.

### 3.1 Participant-aware objective

In PaPaGei-P, we train a self-supervised learning model to maximize agreement between the latent embeddings of PPG signals from the same subject. While (Abbaspourazad et al., ) implements a similar strategy, they do not train or evaluate on public datasets.

Training. We define a positive pair as any two distinct segments from the same subject, denoted as $\{(\mathbf{x}^{s}_{i},\mathbf{x}^{s}_{j})|i\neq j\}$. Next, we apply a series of time series augmentations such as random cropping, adding Gaussian noise, time flipping, negation, and magnitude scaling (Tang et al., ). Each augmentation is applied with a predefined probability, determining its likelihood during training. Additionally, each augmentation includes hyper-parameters that control the intensity of the transformation. During training, the augmented versions of two randomly sampled positive pairs are passed through the encoder $E$ and projection $P$ to obtain embeddings denoted $(\mathbf{z}_{i}^{s},\mathbf{z}_{j}^{s})$. Given a batch of embeddings from $N$ distinct subjects with positive pairs of the form $(\mathbf{z}_{i}^{s},\mathbf{z}_{j}^{s})$, the model optimizes the NT-Xent loss given by: $\mathcal{L}_{p}=\frac{1}{2}(\ell_{p}(i,j)+\ell_{p}(j,i))$, where $\ell_{p}(i,j)=-\frac{1}{N}\sum_{i=1}^{N}\log\frac{\exp(sim(\mathbf{z}_{i}^{s},
\mathbf{z}_{j}^{s})/\tau)}{\sum_{s\neq r}\exp(sim(\mathbf{z}_{i}^{s},\mathbf{z
}_{j}^{r})/\tau)}$. In contrast, vanilla SimCLR (Chen et al., ) uses positive pairs as augmented versions of randomly sampled PPG segments.

### 3.2 Morphology-aware objective

In PaPaGei-S, we leverage the PPG signal morphology to train a self-supervised learning model that maximizes agreement between similar physiological features of PPG signals across subjects.

PPG Morphology. Total peripheral resistance (TPR)—the force exerted by the body’s blood vessels on circulating blood—varies under certain medical conditions, such as hypertension and diabetes (Trammel & Sapra, ). Variations in TPR are reflected in PPG signals, presenting as distinct regions within the waveform (Figure 2, left). To capture these variations, we introduce a morphology augmentation module before training, which computes three key PPG metrics (Figure 2, left): (1) stress-induced Vascular Response Index (sVRI) (Lyu et al., ; Zhang et al., ): the ratio of mean PPG signal between post- to pre-systolic phases, (2) Inflection Point Area ratio (IPA) (Wang et al., ): 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 (Elgendi, ). We choose these metrics because they complement each other, with sVRI capturing amplitude variations, IPA reflecting signal width, and SQI addressing cases where IPA cannot be computed due to poor-quality signals lacking a dicrotic notch. We compute these metrics as follows:

$$ $\displaystyle sVRI(\mathbf{x})=\frac{sys\sum_{i=sys}^{n}x_{i}}{(n-sys)\sum_{i= 1}^{sys}x_{i}},\quad IPA(\mathbf{x})=\frac{\int_{0}^{\hat{n}}\mathbf{x}\,dn}{ \int_{\hat{n}}^{n}\mathbf{x}\,dn},\text{ and}\quad SQI(\textbf{x})=\frac{1}{W} \sum_{w}\frac{m_{3}}{m_{2}^{3/2}},$ (1) $$

where $\mathbf{x}\in\mathbb{R}^{N}$ is the PPG segment, $sys$ is the systolic peak, $n$ is the length of time series, and $\hat{n}$ is the dicrotic notch. For $SQI$, we divide x into 5 second windows ($w$; total windows $W$) and compute the skewness $m_{i}=\frac{1}{5\times f}\sum_{j=1}^{5\times f}(x[j]-\mu_{x}[j])^{i}$, which gives the best signal quality discrimination (Elgendi, ).

Training. Before training, the morphology augmentation module takes an augmented (only Gaussian noise and cropping) input time series $\mathbf{x}$ and outputs $y=\{y^{svri},y^{ipa},y^{sqi}\}\in\mathbb{R}^{3}$ (Figure 2 middle). Next, we discretize $y^{svri}$ into a predefined set of $n=8$ bins to denote positive pairs, where $y^{svri}\in\{0,1,\dots,n\}$. We define positive pairs based on the sVRI labels as $\{(\mathbf{x}_{i},\mathbf{x}_{j})|y^{svri}_{i}=y^{svri}_{j},i\neq j\}$. Note that positive pairs are not defined based on subjects.

Given a batch of $N$ PPG signals and their morphology, we optimize three heads. First, we extract the embeddings $Z=\{\textbf{z}_{1},\textbf{z}_{2},\cdots,\textbf{z}_{N}\}$ from the projection $P$ (Figure 2 right middle), and compute the contrastive loss for sVRI (equation 2). Next, we use the embeddings $H=\{\mathbf{h}_{1},\mathbf{h}_{2},\cdots,\mathbf{h}_{N}\}$ to predict the IPA ($\mathbf{\hat{y}}^{ipa}\in\mathbb{R}^{N}$) and SQI ($\mathbf{\hat{y}}^{sqi}\in\mathbb{R}^{N}$) using the mixture of expert (MoE) heads $M_{1}$ and $M_{2}$. 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 3). 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 PaPaGei-S training objective is given in equation 4.

$$ $\displaystyle\ell_{s}(i,j)$ $\displaystyle=-\frac{1}{N}\sum_{i=1}^{N}\log\frac{\exp\left(sim(\mathbf{z}_{i} ,\mathbf{z}_{j})/\tau\right)}{\sum_{k\neq j}\exp\left(sim(\mathbf{z}_{i}, \mathbf{z}_{k})/\tau\right)}$ (2) $\displaystyle\mathcal{L}_{svri}=\frac{1}{2}(l_{s}(i,j)+l_{s}(j,i))\quad$ $\displaystyle\mathcal{L}_{ipa}=\frac{1}{N}\sum_{i=1}^{N}\left|y^{ipa}_{i}-\hat {y}^{ipa}_{i}\right|\quad\mathcal{L}_{sqi}=\frac{1}{N}\sum_{i=1}^{N}\left|y^{ sqi}_{i}-\hat{y}^{sqi}_{i}\right|$ (3) $\displaystyle\mathcal{L}_{s}=\alpha\mathcal{L}_{svri}$ $\displaystyle+(1-\alpha)\left(\mathcal{L}_{ipa}+\mathcal{L}_{sqi}\right),\text {where}\;\alpha\in[0,1]$ (4) $$

## 4 Experiments

### 4.1 Pre-training

Datasets. We curate three large public datasets to pre-train PaPaGei (as shown in Table 1). The first dataset is VitalDB (Lee et al., ), which includes PPG signals collected during surgery from the finger using a patient monitor ($f$=500Hz). The second dataset is the MIMIC-III waveform database matched subset (Johnson et al., ), where finger-tip PPG data is collected from an ICU monitor ($f$= 125Hz). The third dataset comes from the Multi-Ethnic Study of Atherosclerosis (MESA) sleep sub-study (Zhang et al., ; Chen et al., ), which provides PPG data obtained through finger-tip polysomnography ($f$= 256Hz). In total, we have 13.5K subjects with 20M segments.

**Table 1: Pre-training datasets.**
| Dataset | #Subjects | #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 |

Pre-processing. To curate and harmonize the PPG signals across all datasets, we perform the following steps based on established practices: (1) Apply a 4th-order Chebyshev bandpass filter with low and high pass cut-offs set at 0.5Hz and 12Hz, respectively (Lapitan et al., ; Liang et al., ); (2) Segment the signal into 10-second windows (Orphanidou, ; Koteska et al., ); (3) Detect flatline segments and remove any segment where more than 25% of the data is flat (BioBSS Documentation, ); (4) Normalize the segments using Z-score (Abbaspourazad et al., ; Temko, ; Zhou et al., ); and (5) Resample the segments to 125Hz (the lowest sampling rate of our pre-training datasets, MIMIC-III).

**Table 2: The task evaluation benchmark of PaPaGei. Datasets in grey are unseen during training (out-of-domain). The rest are used for pre-training (held-out test-sets and labels). Task types in brackets are B: binary, R: regression, M-#classes: multiclass classification.**
| #ID | Dataset | Task | #Subj.(#Samp.) |
| --- | --- | --- | --- |
| T1 | VitalDB (Lee et al., ) | ICU admission (B) | 5866 |
| T2 |  | Operation Type (M-9) | 5866 |
| T3 | MIMIC-III (Moody et al., ) | Mortality (B) | 5596 |
| T4 | MESA (Zhang et al., ) | Smoker (B) | 2055 |
| T5 |  | AHI $>$ 3% Oxygen Desat. (R) | 2055 |
| T6 |  | AHI $>$ 4% Oxygen Desat. (R) | 2055 |
| T7 | nuMom2B (Facco et al., ) | Pregnancy stage (B) | 3163 (5337) |
| T8 |  | Gestation Age (R) | 3163 (5337) |
| T9 | VV (Skin Tone) (Toye, ) | Systolic BP* (R) | 231 |
| T10 |  | Diastolic BP* (R) | 231 |
| T11 | PPG-BP (Liang et al., ) | Systolic BP (R) | 219 |
| T12 |  | Diastolic BP (R) | 219 |
| T13 |  | Average Heart Rate (R) | 219 |
| T14 |  | Hypertension (B) | 219 |
| T15 | SDB (Garde et al., ) | Sleep Disordered Breathing (B) | 146 |
| T16 | ECSMP (Gao et al., ) | Mood Disturbance (B) | 89 |
| T17 | WESAD (Schmidt et al., ) | Valence (B) | 15 (4497) |
| T18 |  | Arousal (B) | 15 (4497) |
| T19 | PPG-DaLiA (Reiss et al., ) | Heart Rate (R) | 15 (64697) |
| T20 |  | Activity (M-9) | 15 (64697) |

Implementation. Recent work in PPG modeling has extensively used small-scale CNN architectures (Abbaspourazad et al., ; Ding et al., ). Building on that, we implement a ResNet-style (He et al., ) CNN encoder with 18 convolutional blocks, starting with a filter size of 32, which doubles every 4 blocks. The projection layer is a single FC layer, generating a 512-d embedding. In the PaPaGei-S variant, the expert block ($M_{1}$ & $M_{2}$) uses three parallel FCNNs, each with two FC layers, resulting in a 128-d embedding. For augmentations, PaPaGei-P uses cropping (0.50), negation (0.20), flipping (0.20), and scaling (0.40). PaPaGei-S uses cropping (0.25) and Gaussian noise (0.25). PaPaGei-S avoids augmentations that alter PPG’s morphology. We set $\alpha=0.6$ and train on eight V100 GPUs for 15,000 steps (lr$=10^{-4}$), with PaPaGei-P and PaPaGei-S having 5M and 5.7M parameters, respectively (we study scaling in Section 5.2).

### 4.2 Downstream Tasks

To evaluate the effectiveness of PaPaGei, we benchmark it against a diverse set of datasets, tasks, and baselines, chosen for their large size and clinical relevance (where applicable)(^2^22[https://peterhcharlton.github.io/post/ppg_datasets/](https://peterhcharlton.github.io/post/ppg_datasets/)). A brief description of the tasks with their corresponding #ID is provided in Table 2, with further details in Appendix B.

Figure: 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.
[Uncaptioned image]: x3.png

Identifying patient risk factors is crucial for hospitals to allocate resources effectively. To address this, we evaluate several indicators, including ICU admission (T1), type of operation (T2), mortality (T3), and smoking status (T4). For sleep apnea diagnosis, the American Academy of Sleep Medicine (AASM) recommends using the Apnea/Hypopnea Index (AHI) with at least 3% or 4% oxygen desaturation as a key metric (Ruehland et al., ). Thus, we predict AHI at 3% and 4% desaturation thresholds (T5 & T6) and classify sleep-disordered breathing (T15). For pregnancy outcomes, changes in gestational age and pregnancy stage are linked to risks like hypertensive disorders and small-for-gestational-age delivery (Bouariu et al., ; Wu et al., ; Crump et al., ), enabling us to classify pregnancy stage (T7) and predict gestational age (T8). In cardiovascular 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).

### 4.3 Baselines

We benchmark PaPaGei’s performance against competitive baselines. As open-source foundation models designed for physiological signals, PaPaGei is compared to recent time-series foundation models: Chronos (Ansari et al., ) and MOMENT (Goswami et al., ). 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., ), BYOL (Grill et al., ), and TF-C (Zhang et al., ). In addition, to assess model generalizability on PPG signals, we compare against REGLE, a model pre-trained on UK Biobank’s PPG signals (Yun et al., ). As a simple baseline, we employ a random 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-specific approach serves as a benchmark for comparison with more advanced techniques.

### 4.4 Linear Evaluation

Initially, we split the in-domain and out-of-domain datasets into training, validation, and test sets at 80/10/10 and 60/20/20 ratios. The splitting is performed at the subject level ensuring no overlap between individuals across the sets. The models are evaluated by extracting feature representations from resampled data (125Hz) and applying linear probing for each task. For binary classification tasks, we employ a logistic regression model, with performance measured by the AUROC score. For regression tasks, ridge regression is used, and performance is evaluated based on the mean absolute error (MAE). Multi-class classification tasks are trained using a random forest model, with accuracy as the evaluation metric. To ensure robustness, we compute 95% confidence intervals (CIs) through bootstrapping (500 sampling runs with replacement). More details are provided in the Appendix §A.

## 5 Results

### 5.1 Overall Performance

**Table 3: Downstream comparison against pre-trained models. The model size is denoted next to their names. 95% CIs are reported in square brackets and the best value is bolded.**
|  | REGLE (0.07M) | Chronos (200M) | Moment (385M) | PaPaGei-P (5M) | PaPaGei-S (5.7M) |
| --- | --- | --- | --- | --- | --- |
| Classification - AUROC ($\uparrow$) | (Yun et al., ) | (Ansari et al., ) | (Goswami et al., ) |  |  |
| ICU Admission | 0.57 [0.52-0.62] | 0.73 [0.68-0.80] | 0.72 [0.70-0.80] | 0.73 [0.67-0.78] | 0.79 [0.75-0.82] |
| Mortality | 0.55 [0.52-0.59] | 0.68 [0.65-0.71] | 0.67 [0.63-0.71] | 0.67 [0.63-0.71] | 0.67 [0.63-0.70] |
| Smoker | 0.54 [0.47-0.59] | 0.62 [0.57-0.67] | 0.62 [0.56-0.67] | 0.64 [0.58-0.69] | 0.61 [0.56-0.66] |
| Pregnancy stage | 0.64 [0.57-0.63] | 0.81 [0.79-0.82] | 0.76 [0.74-0.78] | 0.74 [0.72-0.76] | 0.78 [0.75-0.80] |
| Hypertension | 0.47 [0.34-0.58] | 0.57 [0.43-0.71] | 0.75 [0.64-0.85] | 0.74 [0.55-0.90] | 0.77 [0.68-0.87] |
| Sleep Disordered Breathing | 0.45 [0.30-0.61] | 0.58 [0.35-0.82] | 0.45 [0.23-0.66] | 0.54 [0.23-0.66] | 0.70 [0.57-0.84] |
| Mood Disturbance | 0.41 [0.16-0.66] | 0.43 [0.21-0.68] | 0.55 [0.33-0.78] | 0.53 [0.27-0.78] | 0.56 [0.33-0.77] |
| Valence | 0.55 [0.52-0.57] | 0.56 [0.53-0.59] | 0.57 [0.54-0.59] | 0.53 [0.51-0.56] | 0.56 [0.54-0.59] |
| Arousal | 0.51 [0.52-0.58] | 0.57 [0.54-0.60] | 0.56 [0.53-0.58] | 0.58 [0.55-0.61] | 0.55 [0.52-0.57] |
| Average | 0.52 $\pm$ 0.06 | 0.62 $\pm$ 0.10 | 0.63 $\pm$ 0.09 | 0.63 $\pm$ 0.08 | 0.67 $\pm$ 0.09 |
| Regression - MAE ($\downarrow$) |  |  |  |  |  |
| Apnea/Hypopnea Index $>$ 3% | 15.54 [14.20-16.69] | 14.06 [13.05-15.16] | 14.23 [13.04-15.42] | 13.85 [12.43-15.49] | 12.97 [11.87-14.05] |
| Apnea/Hypopnea Index $>$ 4% | 12.64 [11.47-13.78] | 11.57 [10.51-12.72] | 11.80 [10.79-12.93] | 11.24 [9.71-12.87] | 10.56 [9.59-11.62] |
| Gestation Age | 7.28 [7.16-7.39] | 5.69 [5.54-5.85] | 6.24 [6.10-6.37] | 6.40 [6.21-6.59] | 6.05 [5.91-6.17] |
| Systolic BP (VV) | 15.88 [13.67-18.36] | 17.24 [14.57-20.13] | 14.71 [12.38-17.29] | 19.11 [16.26-22.23] | 14.65 [12.50-16.78] |
| Diastolic BP (VV) | 8.65 [7.16-10.27] | 10.53 [8.91-12.19] | 10.53 [8.91-12.19] | 10.87 [9.10-12.98] | 8.29 [6.61-10.22] |
| Systolic BP (PPG-BP) | 16.32 [13.87-19.13] | 16.91 [13.31-19.34] | 14.50 [11.98-17.31] | 13.60 [10.65-16.51] | 14.39 [12.53-16.45] |
| Diastolic BP (PPG-BP) | 9.30 [7.94-10.87] | 10.26 [8.13-12.57] | 9.53 [8.28-10.96] | 8.88 [7.33-10.76] | 8.71 [7.18-10.01] |
| Average HR | 6.88 [5.81-8.12] | 8.51 [7.05-10.07] | 4.41 [3.48-5.48] | 3.47 [2.74-4.32] | 4.00 [3.34-4.67] |
| HR | 16.35 [16.20-16.50] | 9.65 [9.50-9.79] | 8.82 [8.68-8.96] | 10.92 [10.80-11.04] | 11.53 [11.40-11.66] |
| Average | 12.09 $\pm$ 3.83 | 11.60 $\pm$ 3.60 | 10.43 $\pm$ 3.46 | 10.92 $\pm$ 4.25 | 10.12 $\pm$ 3.47 |

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 3 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 regression tasks, PaPaGei-S again demonstrates strong performance, achieving the lowest average MAE (10.12), particularly in tasks related to Apnea/Hypopnea Index and BP measurements. REGLE, a small model trained on a large PPG dataset, generally underperforms compared to other models, suggesting its compact size may limit learning complex patterns. Chronos, a much larger model, shows competitive performance, particularly in Pregnancy Stage and Gestation Age prediction. Moment, the largest model, shows strong performance across many tasks, often matching or slightly outperforming Chronos. Notably, while PaPaGei-P shows competitive results, PaPaGei-S consistently outperforms it, suggesting the signal morphology objectives offer advantages in predictive capability.

Table 4 presents a 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.

Figure: Figure 4: Downstream data-efficiency analysis. Results are averaged over all binary classification (left) and regression tasks (right). PaPaGei-S performs better with increased label availability.
Refer to caption: x19.png

For regression tasks, PaPaGei-S again achieves the lowest average MAE. SimCLR, BYOL, 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.

**Table 4: Downstream comparison against baseline and other SSL methods. The model size is denoted next to their names. 95% CIs are reported in square brackets and the best value is bolded.**
|  | Stat. Features | SimCLR (5M) | BYOL (12M) | TF-C (9.7M) | PaPaGei-P (5M) | PaPaGei-S (5.7M) |
| --- | --- | --- | --- | --- | --- | --- |
| Classification - AUROC ($\uparrow$) |  | (Chen et al., ) | (Grill et al., ) | (Zhang et al., ) |  |  |
| ICU Admission | 0.71 [0.65-0.78] | 0.75 [0.72-0.79] | 0.78 [0.73-0.81] | 0.71 [0.67-0.75] | 0.73 [0.67-0.78] | 0.79 [0.75-0.82] |
| Mortality | 0.57 [0.54-0.61] | 0.67 [0.63-0.70] | 0.67 [0.64-0.71] | 0.67 [0.63-0.70] | 0.67 [0.63-0.71] | 0.67 [0.63-0.70] |
| Smoker | 0.63 [0.58-0.67] | 0.62 [0.57-0.68] | 0.62 [0.57-0.68] | 0.61 [0.56-0.67] | 0.64 [0.58-0.69] | 0.61 [0.56-0.66] |
| Pregnancy stage | 0.64 [0.62-0.67] | 0.74 [0.72-0.75] | 0.62 [0.57-0.68] | 0.74 [0.72-0.76] | 0.74 [0.72-0.76] | 0.78 [0.75-0.80] |
| Hypertension | 0.66 [0.47-0.83] | 0.75 [0.64-0.86] | 0.74 [0.64-0.84] | 0.76 [0.63-0.86] | 0.74 [0.55-0.90] | 0.77 [0.68-0.87] |
| SDB | 0.32 [0.14-0.55] | 0.61 [0.46-0.76] | 0.59 [0.42-0.74] | 0.58 [0.44-0.73] | 0.54 [0.23-0.66] | 0.70 [0.57-0.84] |
| Mood Disturbance | 0.54 [0.31-0.77] | 0.32 [0.12-0.55] | 0.46 [0.21-0.71] | 0.59 [0.33-0.84] | 0.53 [0.27-0.78] | 0.56 [0.33-0.77] |
| Valence | 0.52 [0.49-0.55] | 0.52 [0.49-0.55] | 0.53 [0.50-0.56] | 0.57 [0.54-0.59] | 0.53 [0.51-0.56] | 0.56 [0.54-0.59] |
| Arousal | 0.55 [0.53-0.58] | 0.55 [0.52-0.58] | 0.54 [0.30-0.78] | 0.55 [0.52-0.58] | 0.58 [0.55-0.61] | 0.55 [0.52-0.57] |
| Average | 0.57 $\pm$ 0.11 | 0.61 $\pm$ 0.13 | 0.62 $\pm$ 0.10 | 0.64 $\pm$ 0.07 | 0.63 $\pm$ 0.08 | 0.67 $\pm$ 0.09 |
| Regression - MAE ($\downarrow$) |  |  |  |  |  |  |
| Apnea/Hypopnea Index $>$ 3% | 15.31 [13.63-17.14] | 14.17 [13.04-15.38] | 14.26 [13.10-15.57] | 15.10 [13.84-16.40] | 13.85 [12.43-15.49] | 12.97 [11.87-14.05] |
| Apnea/Hypopnea Index $>$ 4% | 12.52 [10.92-14.14] | 11.76 [10.65-12.89] | 11.88 [10.71-13.05] | 12.41 [11.33-13.49] | 11.24 [9.71-12.87] | 10.56 [9.59-11.62] |
| Gestation Age | 7.15 [6.99-7.34] | 6.28 [6.21-6.49] | 6.24 [6.09-6.38] | 6.35 [6.21-6.49] | 6.40 [6.21-6.59] | 6.05 [5.91-6.17] |
| Systolic BP (VV) | 15.76 [13.67-18.36] | 16.18 [13.73-18.85] | 15.01 [12.32-17.80] | 15.70 [13.23-18.13] | 19.11 [16.26-22.23] | 14.65 [12.50-16.78] |
| Diastolic BP (VV) | 9.75 [7.16-11.27] | 9.15 [7.65-10.65] | 8.91 [7.48-10.43] | 9.15 [7.65-10.65] | 10.87 [9.10-12.98] | 8.29 [6.61-10.22] |
| Systolic BP (PPG-BP) | 15.50 [11.68-20.25] | 14.38 [11.80-16.88] | 14.99 [13.03-17.38] | 14.45 [12.20-17.00] | 13.60 [10.65-16.51] | 14.39 [12.53-16.45] |
| Diastolic BP (PPG-BP) | 9.35 [7.44-11.66] | 9.01 [7.90-10.60] | 9.16 [8.00-10.50] | 9.20 [7.90-10.60] | 8.88 [7.33-10.76] | 8.71 [7.18-10.01] |
| Average HR | 7.01 [5.48-8.89] | 4.65 [3.99-5.39] | 4.78 [3.88-5.93] | 3.58 [2.90-4.21] | 3.47 [2.74-4.32] | 4.00 [3.34-4.67] |
| HR | 13.07 [12.90-13.23] | 11.59 [11.46-11.72] | 12.80 [12.66-12.94] | 9.99 [9.86-10.12] | 10.92 [10.80-11.04] | 11.53 [11.40-11.66] |
| Average | 11.60 $\pm$ 3.41 | 10.79 $\pm$ 3.63 | 10.89 $\pm$ 3.58 | 10.65 $\pm$ 3.88 | 10.92 $\pm$ 4.25 | 10.12 $\pm$ 3.47 |

### 5.2 Ablation Studies

PaPaGei-S component ablation. We assess the impact of PaPaGei-S components. Figure 5(a) & 5(b) show the full model consistently outperforms individual components in both mean and median metrics. On average, sVRI (0.64, 10.36) outperforms the combinations of sVRI + SQI (0.62, 10.81) and sVRI + IPA (0.64, 10.73), with SQI contributing the least to overall performance.

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 to the second best-performing 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 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, PaPaGei-S achieves the lowest MAE at both 25% and 100% data availability, consistently reducing errors. At the middle breakpoints, the results are mixed with TF-C and Moment being competitive.

Figure: ((a))
Refer to caption: x20.png

Model size and scaling analysis. We evaluated whether larger model sizes improve performance by additionally training PaPaGei-S-35M and PaPaGei-S-139M with 35M and 139M parameters, respectively. Both models have the same number of layers, but the 35M model uses a 32 starting filter size, while the 139M model uses 64. As shown in 5(c), the smallest model with 5M parameters performs best in all tasks except one, indicating that smaller models are better suited for PPG data.

Figure: Figure 6: Ablation on pre-training datasets. Average performance across tasks for Binary classification (left) and regression (right) for models trained on different datasets: V (VitalDB), M (MESA), and M-III (MIMIC-III). The mean value is displayed above the plots.
Refer to caption: x23.png

Notably, the 139M model outperforms the 35M but still falls short of the 5M, suggesting that wider models may offer improved performance.
Furthermore, the added complexity of the 139M model is advantageous for capturing decision boundaries in classification compared to the 35M model, which could be attributed to the classification-like objectives of contrastive learning. However, it struggles with regression tasks, which demand more precise outputs.

Pre-training data ablation. We evaluated PaPaGei-S using different combinations of pre-training data to determine if increasing the data volume enhances performance. Figure 6 demonstrates that performance on downstream tasks consistently improves as we incorporate additional upstream data, with optimal results achieved when utilizing all three datasets. 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., ).

### 5.3 Case Studies

Figure: Figure 7: Pair-wise inter-participant embedding distances in sleep-disordered breathing.
Refer to caption: x24.png

To evaluate the downstream performance of PaPaGei-S, we analyze the embedding distances among users within the sleep-disordered breathing dataset. We also investigate the model’s performance in predicting the Apnea-Hypopnea Index (AHI $>$ 3%) and average Heart Rate.
We analyze BP performance across different skin tones to evaluate robustness to different populations.

Inter-participant embeddings. Figure 7 shows the distribution of pair-wise embedding distances across participants (Kiyasseh et al., ). SimCLR and BYOL exhibit sharper peaks at lower distances, indicating that participants are more closely clustered within the embedding 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 a more balanced distribution, with both large and small peaks, suggesting it captures both similarities and some variation between participants. In contrast, PaPaGei-S provides the widest dispersion of embeddings, highlighting its ability to capture a broader range of features that may be valuable for distinguishing between participants’ medical conditions.

Figure: ((a)) AHI $>$ 3% predictions
Refer to caption: x25.png

Figure: Figure 9: Skin tone analysis for Blood Pressure estimation (VV dataset). Figure 22 provides a breakdown across all skin tones.
Refer to caption: x31.png

Regression predictions. In Figure 8, we observe that all models demonstrate positive correlations between true and predicted AHI.
Compared to Chronos and SimCLR, PaPaGei-S shows superior regression performance (r=0.54 vs. r=0.44 and r=0.41). This suggests that PaPaGei-S provides a more accurate prediction of AHI $>$ 3% compared to Chronos and SimCLR, as it aligns more closely with the true AHI values.
Moreover, PaPaGei-P demonstrates a higher correlation (0.90 vs. 0.77 and 0.83) for HR prediction. Examining the PaPaGei-P histograms reveals that the model effectively captures both tails of the distribution, reducing errors in the high and low heart rate ranges. In contrast, Moment and BYOL exhibit less consistent prediction distributions, leading to higher error rates.

Skin tone analysis. Understanding the impact of PPG models across skin tones is crucial for practical use (Bent et al., ; Sjoding et al., ). Therefore, we examine BP estimation performance across skin tones. As shown in Figure 9, 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, there are no significant differences across all models. While PaPaGei-S obtains the best overall performance, additional work is necessary to improve robustness on darker skin tones.

## 6 Conclusion and Future Work

Our results show that PaPaGei outperforms baselines in 14 tasks, with classification and regression improvements of 4.7%-6.3% and 2.9%-4.9%, respectively. PaPaGei-S excelled in cardiovascular tasks like BP, Hypertension, and HR, which can be attributed to the sVRI and IPA objectives, and PaPaGei-P outperformed 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 as a key component and IPA and SQI providing positive knowledge transfer in multi-task setups. PaPaGei is both data- and size-efficient (5M), making it ideal for medical applications where large models (200M+) are impractical due to 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 personal 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. In conclusion, PaPaGei represents a significant advancement in foundation models for analyzing PPG signals in resource-constrained medical environments, with its open-source nature encouraging further research and development in healthcare applications.

## Reproducibility Statement

Models, data, and code will be made publicly available upon release, ensuring accessibility for future research. Our study exclusively utilizes 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 Section 4 and Appendix §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 will be available with user-friendly examples. We have provided a detailed overview of the model architecture and its hyperparameters in Sections 3, 4.1, and Appendix §A. With these detailed resources, 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 regulations 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, we recognize the need to address potential misuse (Perez-Pozuelo et al., ). Examples of misuse could include unauthorized health monitoring, discriminatory practices in insurance or employment, unfair credit scoring, or 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 ongoing interdisciplinary dialogue to address potential risks and ensure responsible development and deployment of such technologies, recognizing the broader societal impacts of AI 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.

## APPENDIX

## Appendix A Training and Inference Details

Architecture & Pre-training. The architecture of our ResNet 18-block encoder is described in Tables 7, 7, and 7. Each 1D convolution layer is configured with a kernel size of 3 and a stride of 2, while the max-pooling layer utilizes a kernel size of 3 with a stride of 1. We start with a filter size of 32, which doubles every 4 blocks to capture progressively more complex features. Dropout is applied with a probability of 0.5 to prevent overfitting. This backbone architecture is used across different methods in our experiments to ensure consistency and make for a fair comparison during evaluation. In PaPaGei-S, we project the learned representations to a 512-dimensional embedding after the convolutional block (we investigated larger embedding sizes of 768 and 1024 and found no significant 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 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(^3^33[https://github.com/chengding0713/SiamQuality](https://github.com/chengding0713/SiamQuality))(^4^44[https://github.com/mims-harvard/TFC-pretraining](https://github.com/mims-harvard/TFC-pretraining)), but apply our encoder as the backbone to ensure consistency. Our models are 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 we did not observe significant differences in performance with batch sizes of 64 and 256. 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., ; Tang et al., ). For model training, we primarily used PyTorch (Paszke et al., ). The NTXentLoss implementation was sourced from the PyTorch Metric Learning package(^5^55[https://github.com/KevinMusgrave/pytorch-metric-learning](https://github.com/KevinMusgrave/pytorch-metric-learning)).

**Table 5: ResNet-style CNN encoder architecture used in PaPaGei.**
| Layer | Output Shape |
| --- | --- |
| Conv1 | [32, 32, 1250] |
| Batch Norm | [32, 32, 1250] |
| ReLU | [32, 32, 1250] |
| Basic Block Type 1 | [32, 32, 1250] |
| (Basic Block Type 2) $\times$ 3 | [32, 32, 313] |
| (Basic Block Type 2) $\times$ 4 | [32, 64, 79] |
| (Basic Block Type 2) $\times$ 4 | [32, 128, 20] |
| (Basic Block Type 2) $\times$ 4 | [32, 256, 5] |
| (Basic Block Type 2) $\times$ 2 | [32, 512, 3] |
| BatchNorm | [32, 512, 3] |
| ReLU | [32, 512, 3] |
| Linear | [32, 512] |

Feature Extraction & Linear Evaluation
We extracted the projected embedding for linear evaluation. For Moment and Chronos, we extract the default embedding size, which is 1024 and 768, respectively. We use cross-validated grid search to identify the best parameters for our linear probes. The hyperparameters chosen for each model are as follows: (1) Logistic Regression: {’penalty’: [’l1’, ’l2’], ’C’: [0.01, 0.1, 1, 10, 100], ’solver’: [’lbfgs’], ’max_iter’: [100, 200]}. (2) Linear Regression: {’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, 2]}

## Appendix B Datasets and Tasks

**Table 8: The task evaluation benchmark of PaPaGei. Datasets highlighted in grey are unseen during training, thus, the corresponding tasks are 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.**
| #ID | Dataset | SR (Hz) | Collected by | Task | Task Type | #Subjects (#Samples) |
| --- | --- | --- | --- | --- | --- | --- |
| T1 | VitalDB (Lee et al., ) | 500 | ICU monitor | ICU admission (Yes/No) | B | 5866 |
| T2 |  |  |  | Operation Type | M (11) | 5866 |
| T3 | MIMIC-III (Moody et al., ) | 125 | ICU Monitor | Mortality | B | 5596 |
| T4 | MESA (Zhang et al., ) | 256 | Polysomnography finger | Smoker | B | 2055 |
| T5 |  |  |  | AHI $>$ 3% Oxygen Desat. | R | 2055 |
| T6 |  |  |  | AHI $>$ 4% Oxygen Desat. | R | 2055 |
| T7 | nuMom2B (Facco et al., ) | 75 | Polysomnography finger | Pregnancy stage (early/late) | B | 3163 (5337) |
| T8 |  |  |  | Gestation Age | R | 3163 (5337) |
| T9 | VV (Skin Tone) (Toye, ) | 60 | Finger | Systolic BP | R | 231 |
| T10 |  |  |  | Diastolic BP | R | 231 |
| T11 | PPG-BP (Liang et al., ) | 1000 | Finger Pulse Ox | Systolic BP | R | 219 |
| T12 |  |  |  | Diastolic BP | R | 219 |
| T13 |  |  |  | Average Heart Rate | R | 219 |
| T14 |  |  |  | Hypertension | B | 219 |
| T15 | SDB (Garde et al., ) | 62.5 | Finger Pulse Ox | Sleep Disordered Breathing | B | 146 |
| T16 | ECSMP (Gao et al., ) | 64 | Wrist | Mood Disturbance | B | 89 |
| T17 | WESAD (Schmidt et al., ) | 64 | Wrist | Valence | B | 15 (4497) |
| T18 |  |  |  | Arousal | B | 15 (4497) |
| T19 | PPG-DaLiA (Reiss et al., ) | 64 | Wrist | Heart Rate | R | 15 (64697) |
| T20 |  |  |  | Activity | M (9) | 15 (64697) |

VitalDB. The VitalDB dataset provides comprehensive monitoring of vital signs and physiological parameters from 6,388 surgical cases. This high-resolution dataset includes a wide range of intraoperative monitoring variables such as heart rate, blood pressure, oxygen saturation, and other critical physiological signals, collected at frequent intervals throughout surgery. The surgical operation belongs to one of the eleven categories: colorectal, biliary/pancreas, stomach, major resection, minor resection, breast, transplantation, thyroid, hepatic, vascular, and others. After the data cleaning process, we narrowed the dataset down to 5,866 subjects 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 subjects fall within the age range of 50 to 70, with a significant proportion being around 60 years old. The ICU label corresponds to whether the person was admitted to the ICU or not.

Figure: Figure 10: VitalDB dataset descriptive statistics.
Refer to caption: x32.png

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, we focus specifically on extracting photoplethysmogram (PPG) data, provided it is available for each patient. To ensure the quality of the data, we set a criterion of at least 1 minute of usable PPG signal that must be present. After performing a thorough data cleaning process, we end up with a cohort of 5,596 subjects with reliable PPG data. As illustrated in Figure 11, the dataset shows a gender imbalance, with a higher proportion of male patients compared to female patients. Additionally, the majority of subjects are aged 60 years or older, reflecting a typical ICU population that often includes elderly patients with critical health conditions.

Figure: Figure 11: MIMIC-III dataset descriptive statistics.
Refer to caption: x35.png

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 parameters. After the data cleaning process, we retained 2,055 subjects for analysis. As shown in Figure 12, the dataset shows a slightly larger proportion of female participants. The age distribution reveals that most subjects are between 60 and 80 years old, reflecting an older adult population, which is commonly studied concerning sleep disorders and cardiovascular risks.

In this study, we use the Apnea-Hypopnea Index (AHI) with at least 3% and 4% oxygen desaturation as the primary measure for diagnosing sleep apnea, as recommended by the American Academy of Sleep Medicine (AASM) (Ruehland et al., ). 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 us to account for both current and former smokers, capturing a broader range of smoking-related health risks within our analysis.

Figure: Figure 12: MESA dataset descriptive statistics.
Refer to caption: x37.png

NuMoM2B. Changes in gestational age and pregnancy stage are risk factors associated with adverse pregnancy outcomes such as hypertensive disorders and small-for-gestational-age delivery (Bouariu et al., ; Parikh et al., ; Wu et al., ; Crump et al., ). These diseases affect heart function that can be measured using the PPG sensor (Feli et al., ). The Nulliparous Pregnancy Outcomes Study: monitoring mothers-to-be (nuMoM2B) sub-study examines the relationship between adverse pregnancy outcomes and sleep disorders. In particular, an overnight polysomnograph that collects PPG data is administered to the women at their homes during 6-15 weeks (early) 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 is bimodal, which we use as a predictor in our regression task. For pregnancy stage, we classify visit 1 as early and visit 3 as late.

Figure: Figure 13: NuMoM2B dataset descriptive statistics.
Refer to caption: x42.png

VitalVideos (VV) (Skin Tone). The Vital Videos study is an ongoing project that collects data on vital signs, videos, and blood pressure across a variety of conditions, including variations in lighting, background, and skin tone. For our analysis, we used data from two groups, totalling 231 subjects, from Europe and Sub-Saharan Africa. As shown in Figure 14, most subjects have a Fitzpatrick skin tone of 5 or 6, indicating darker skin. The dataset is primarily composed of female participants, with an age range between 40 and 60 years. Additionally, the majority of subjects 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.

Figure: Figure 14: Vital Videos dataset descriptive statistics.
Refer to caption: x45.png

PPG-BP. The PPG-BP consists of short PPG recordings from 219 subjects collected at 1000Hz. For each subject, there are three 2.1s recordings. For our analysis, we zero pad them to 10s. In Figure 15, the age distribution shows that most subjects 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 is 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 subjects ranges between 70 and 90 beats per minute.

Figure: Figure 15: PPG-BP dataset descriptive statistics.
Refer to caption: x50.png

Figure: Figure 16: SDB dataset descriptive statistics.
Refer to caption: x56.png

SDB. The sleep-disordered breathing dataset includes data from 146 children, collected through polysomnography with finger recordings lasting over three hours. Ground truth labels are provided as Apnea-Hypopnea Index (AHI) values, categorized into four levels: 0 (normal), 1 (mild, AHI between 5 and 15), 2 (moderate, AHI between 15 and 30), and 3 (severe, AHI over 30) as shown in Figure 16. For our classification task, we group AHI 0 as indicating no sleep breathing disorder, while AHI levels 1 through 3 are classified as the presence of a sleep breathing disorder.

ECSMP. The ECSMP dataset was gathered to study the relationship between emotion, cognition, and sleep in 89 subjects. As shown in Figure 17, the majority of the participants are young adult females, with an average age of around 25 years. Mood disturbances were measured using the Profile of Mood States (POMS) scale, which captures various aspects of emotional states. To classify participants into high versus low mood disturbance categories, we binarized the Total Mood Disturbance (TMD) values by using the median as the cutoff point.

Figure: Figure 17: ECSMP dataset descriptive statistics.
Refer to caption: x57.png

WESAD. The wearable stress and affect detection dataset is a multi-modal dataset collected from 15 subjects using various sensor modalities. In this study, participants were exposed to videos designed to elicit different affective states, such as amusement, meditation, stress, and baseline conditions. Following each session, participants completed the Self-Assessment Manikins (SAM) questionnaire (Bradley & Lang, ), which provided the ground-truth values for valence and arousal. In our analysis, we binarized these values by categorizing valence and arousal as low (1) 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.

Figure: Figure 18: WESAD dataset descriptive statistics.
Refer to caption: x60.png

PPG-DaLiA. This dataset collects PPG signals from 15 subjects 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 a wide range of heart rates, varying from 60 to 150 beats per minute, depending on the specific activity being performed.

Figure: Figure 19: PPG-DaLiA dataset descriptive statistics.
Refer to caption: x62.png

## Appendix C Representative Signals from Pre-training Datasets

Figure: 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.
Refer to caption: x64.png

Figure: Figure 21: Normalized and resampled 10-second pre-processed PPG segments from VitalDB, 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.
Refer to caption: x65.png

## Appendix D Additional Results

### D.1 Multi-class Classification

**Table 9: Multi-class classification comparison against pre-trained models. The parameter size and reference are denoted next to their names. 95% CIs are reported in square brackets and the best value is bolded.**
|  | REGLE (0.07M) | Chronos (200M) | Moment (385M) | PaPaGei-P (5M) | PaPaGei-S (5.7M) |
| --- | --- | --- | --- | --- | --- |
| Classification - ACC ($\uparrow$) | (Yun et al., ) | (Ansari et al., ) | (Goswami et al., ) |  |  |
| Operation Type | 0.21 [0.18-0.23] | 0.25 [0.22-0.29] | 0.27 [0.23-0.31] | 0.30 [0.26-0.33] | 0.30 [0.27-0.32] |
| Activity | 0.29 [0.28-0.29] | 0.41 [0.40-0.42] | 0.41 [0.40-0.42] | 0.38 [0.37-0.39] | 0.37 [0.36-0.37] |
| Average | 0.25 $\pm$ 0.04 | 0.33 $\pm$ 0.08 | 0.34 $\pm$ 0.07 | 0.34 $\pm$ 0.04 | 0.33 $\pm$ 0.03 |

**Table 10: Multi-class classification comparison against CL methods. The parameter size and reference are denoted next to their names. 95% CIs are reported in square brackets and the best value is bolded.**
|  | Stat. Features | SimCLR | BYOL | TF-C | PaPaGei-P (5M) | PaPaGei-S (5.7M) |
| --- | --- | --- | --- | --- | --- | --- |
| Classification - ACC ($\uparrow$) |  | (Chen et al., ) | (Grill et al., ) | (Zhang et al., ) |  |  |
| Operation Type | 0.27 [0.24-0.32] | 0.27 [0.24-0.29] | 0.31 0.27-0.34 | 0.27 [0.27-0.29] | 0.30 [0.26-0.33] | 0.30 [0.27-0.32] |
| Activity | 0.37 [0.36-0.38] | 0.36 [0.35-0.37] | 0.34 [0.33-0.35] | 0.37 [0.36-0.38] | 0.38 [0.37-0.39] | 0.37 [0.36-0.37] |
| Average | 0.32 $\pm$ 0.05 | 0.31 $\pm$ 0.04 | 0.32 $\pm$ 0.01 | 0.32 $\pm$ 0.05 | 0.34 $\pm$ 0.04 | 0.33 $\pm$ 0.03 |

### D.2 Other Evaluation Metrics

**Table 11: Downstream comparison against pre-trained models (additional metrics: F1-score and $R^{2}$). The parameter size and reference are denoted next to their names. 95% CIs are reported in square brackets and the best value is bolded.**
|  | REGLE (0.07M) | Chronos (200M) | Moment (385M) | PaPaGei-P (5M) | PaPaGei-S (5.7M) |
| --- | --- | --- | --- | --- | --- |
| Classification - F1-Score ($\uparrow$) | (Yun et al., ) | (Ansari et al., ) | (Goswami et al., ) |  |  |
| ICU Admission | 0.00 [0.00-0.00] | 0.20 [0.11-0.30] | 0.12 [0.04-0.20] | 0.12 [0.04-0.20] | 0.26 [0.18-0.33] |
| Mortality | 0.00 [0.00-0.00] | 0.14 [0.09-0.19] | 0.16 [0.11-0.21] | 0.22 [0.16-0.27] | 0.17 [0.13-0.22] |
| Smoker | 0.16 [0.10-0.23] | 0.51 [0.44-0.58] | 0.40 [0.33-0.47] | 0.45 [0.38-0.51] | 0.45 [0.37-0.50] |
| Pregnancy stage | 0.49 [0.47-0.52] | 0.69 [0.67-0.71] | 0.63 [0.60-0.65] | 0.62 [0.59-0.64] | 0.65 [0.62-0.67] |
| Hypertension | 0.77 [0.70-0.84] | 0.68 [0.58-0.77] | 0.75 [0.66-0.84] | 0.84 [0.72-0.92] | 0.78 [0.70-0.86] |
| Sleep Disordered Breathing | 0.00 [0.00-0.00] | 0.33 [0.00-0.60] | 0.22 [0.00-0.47] | 0.32 [0.00-0.60] | 0.47 [0.23-0.67] |
| Mood Disturbance | 0.00 [0.00-0.00] | 0.36 [0.10-0.59] | 0.23 [0.00-0.47] | 0.37 [0.00-0.66] | 0.32 [0.00-0.58] |
| Valence | 0.00 [0.00-0.00] | 0.10 [0.07-0.14] | 0.12 [0.09-0.16] | 0.17 [0.13-0.21] | 0.03 [0.01-0.04] |
| Arousal | 0.83 [0.81-0.84] | 0.82 [0.80-0.83] | 0.81 [0.79-0.82] | 0.81 [0.79-0.82] | 0.83 [0.81-0.84] |
| Average | 0.25 $\pm$ 0.33 | 0.42 $\pm$ 0.24 | 0.38 $\pm$ 0.26 | 0.43 $\pm$ 0.25 | 0.44 $\pm$ 0.25 |
| Regression - $R^{2}$ ($\uparrow$) |  |  |  |  |  |
| Apnea/Hypopnea Index $>$ 3% | 0.02 [0.00-0.03] | 0.18 [0.08-0.26] | 0.14 [0.06-0.22] | 0.15 [0.05-0.24] | 0.29 [0.22-0.36] |
| Apnea/Hypopnea Index $>$ 4% | 0.01 [0.00-0.03] | 0.16 [0.08-0.22] | 0.13 [0.05-0.20] | 0.12 [0.03-0.22] | 0.28 [0.20-0.34] |
| Gestation Age | 0.04 [0.02-0.06] | 0.28 [0.24-0.31] | 0.20 [0.17-0.23] | 0.18 [0.14-0.22] | 0.22 [0.19-0.25] |
| Systolic BP (VV) | -0.03 [-0.18-0.01] | -0.24 [-0.72-0.03] | 0.06 [-0.25-0.28] | -0.41 [-0.77-(-0.15)] | 0.15 [-0.09-0.30] |
| Diastolic BP (VV) | 0.01 [-0.09-0.06] | -0.29 [-0.87-(-0.01)] | 0.01 [-0.25-0.14] | -0.48 [-1.02-(-0.20)] | 0.10 [-0.11-0.23] |
| Systolic BP (PPG-BP) | -0.07 [-0.21-0.04] | -0.13 [-0.36-0.06] | 0.07 [-0.31 -0.31] | 0.36 [0.16-0.49] | 0.20 [0.02-0.31] |
| Diastolic BP (PPG-BP) | 0.01 [-0.05-0.02] | -0.10 [-0.45-0.07] | -0.03 [-0.31-0.13] | 0.22 [-0.13-0.40] | 0.08 [-0.07-0.17] |
| Average HR | 0.37 [0.17-0.51] | 0.02 [-0.16-0.17] | 0.68 [0.45-0.80] | 0.79 [0.57-0.90] | 0.78 [0.69-0.83] |
| HR | 0.00 [0.00-0.01] | 0.57 [0.56-0.59] | 0.63 [0.61-0.64] | 0.52 [0.51-0.53] | 0.48 [0.42-0.46] |
| Average | 0.04 $\pm$ 0.12 | 0.05 $\pm$ 0.25 | 0.21 $\pm$ 0.24 | 0.16 $\pm$ 0.38 | 0.28 $\pm$ 0.20 |

**Table 12: Downstream comparison against CL models (additional metrics: F1-score and $R^{2}$). The parameter size and reference are denoted next to their names. 95% CIs are reported in square brackets and the best value is bolded.**
|  | Stat. Features | SimCLR | BYOL | TF-C | PaPaGei-P (5M) | PaPaGei-S (5.7M) |
| --- | --- | --- | --- | --- | --- | --- |
| Classification - F1-Score ($\uparrow$) |  |  |  |  |  |  |
| ICU Admission | 0.30 [0.18-0.40] | 0.19 [0.12-0.26] | 0.17 [0.11-0.22] | 0.10 [0.05-0.16] | 0.12 [0.04-0.20] | 0.26 [0.18-0.33] |
| Mortality | 0.03 [0.01-0.06] | 0.15 [0.10-0.20] | 0.13 [0.08-0.17] | 0.15 [0.10-0.20] | 0.22 [0.16-0.27] | 0.17 [0.13-0.22] |
| Smoker | 0.47 [0.40-0.53] | 0.43 [0.35-0.49] | 0.49 [0.42-0.56] | 0.37 [0.30-0.44] | 0.45 [0.38-0.51] | 0.45 [0.37-0.50] |
| Pregnancy stage | 0.43 [0.41-0.47] | 0.60 [0.57-0.63] | 0.60 [0.57-0.63] | 0.59 [0.56-0.62] | 0.62 [0.59-0.64] | 0.65 [0.62-0.67] |
| Hypertension | 0.73 [0.58-0.85] | 0.82 [0.73-0.89] | 0.81 [0.73-0.88] | 0.81 [0.72-0.89] | 0.84 [0.72-0.92] | 0.78 [0.70-0.86] |
| Sleep Disordered Breathing | 0.00 [0.00-0.00] | 0.46 [0.21-0.64] | 0.45 [0.23-0.62] | 0.19 [0.00-0.36] | 0.32 [0.00-0.60] | 0.47 [0.23-0.67] |
| Mood Disturbance | 0.21 [0.00-0.47] | 0.21 [0.00-0.44] | 0.37 [0.00-0.67] | 0.56 [0.27-0.80] | 0.37 [0.00-0.66] | 0.32 [0.00-0.58] |
| Valence | 0.04 [0.02-0.07] | 0.09 [0.06-0.12] | 0.01 [0.00-0.03] | 0.07 [0.04-0.09] | 0.17 [0.13-0.21] | 0.03 [0.01-0.04] |
| Arousal | 0.82 [0.81-0.83] | 0.81 [0.79-0.82] | 0.83 [0.81-0.84] | 0.81 [0.80-0.83] | 0.81 [0.79-0.82] | 0.83 [0.81-0.84] |
| Average | 0.33 $\pm$ 0.28 | 0.42 $\pm$ 0.26 | 0.43 $\pm$ 0.27 | 0.40 $\pm$ 0.27 | 0.43 $\pm$ 0.25 | 0.44 $\pm$ 0.25 |
| Regression - $R^{2}$ ($\uparrow$) |  |  |  |  |  |  |
| Apnea/Hypopnea Index $>$ 3% | -0.00 [-0.06-0.03] | 0.16 [0.07-0.23] | 0.16 [0.08-0.22] | 0.06 [-0.00-0.13] | 0.15 [0.05-0.24] | 0.29 [0.22-0.36] |
| Apnea/Hypopnea Index $>$ 4% | -0.01 [-0.07-0.03] | 0.13 [0.06-0.21] | 0.13 [0.05-0.19] | 0.13 [-0.06-0.26] | 0.12 [0.03-0.22] | 0.28 [0.20-0.34] |
| Gestation Age | 0.07 [0.04-0.10] | 0.19 [0.15-0.21] | 0.19 [0.15-0.22] | 0.18 [0.15-0.21] | 0.18 [0.14-0.22] | 0.22 [0.19-0.25] |
| Systolic BP (VV) | -0.10 [-0.51-0.10] | -0.05 [-0.44-0.21] | -0.03 [-0.37-0.18] | -0.05 [-0.36-0.12] | -0.41 [-0.77-(-0.15)] | 0.15 [-0.09-0.30] |
| Diastolic BP (VV) | -0.15 [-0.31-0.11] | -0.14 [-0.29-0.08] | -0.01 [-0.40-0.20] | -0.09 [-0.45-0.16] | -0.48 [-1.02-(-0.20)] | 0.10 [-0.11-0.23] |
| Systolic BP (PPG-BP) | 0.12 [-0.04-0.21] | 0.09 [-0.20-0.31] | 0.10 [-0.16-0.30] | 0.13 [-0.06-0.26] | 0.36 [0.16-0.49] | 0.20 [0.02-0.31] |
| Diastolic BP (PPG-BP) | 0.01 [-0.18-0.14] | 0.00 [-0.20-0.18] | 0.05 [-0.11-0.17] | 0.02 [-0.15-0.12] | 0.22 [-0.13-0.40] | 0.08 [-0.07-0.17] |
| Average HR | 0.15 [-0.10-0.33] | 0.74 [0.64-0.80] | 0.65 [0.50-0.77] | 0.82 [0.73-0.88] | 0.79 [0.57-0.90] | 0.78 [0.69-0.83] |
| HR | 0.34 [0.32-0.36] | 0.45 [0.44-0.47] | 0.36 [0.35-0.37] | 0.54 [0.53-0.55] | 0.52 [0.51-0.53] | 0.48 [0.42-0.46] |
| Average | 0.05 $\pm$ 0.14 | 0.17 $\pm$ 0.25 | 0.18 $\pm$ 0.20 | 0.19 $\pm$ 0.28 | 0.16 $\pm$ 0.38 | 0.28 $\pm$ 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 ($\uparrow$) |  |  |  |
| ICU Admission | 0.79 [0.75-0.82] | 0.72 [0.68-0.75] | 0.77 [0.73-0.80] |
| Mortality | 0.67 [0.63-0.70] | 0.66 [0.63-0.70] | 0.66 [0.63-0.69] |
| Smoker | 0.61 [0.56-0.66] | 0.58 [0.52-0.64] | 0.59 [0.54-0.65] |
| Pregnancy stage | 0.78 [0.75-0.80] | 0.77 [0.75-0.79] | 0.76 [0.74-0.78] |
| Hypertension | 0.77 [0.68-0.87] | 0.75 [0.64-0.85] | 0.77 [0.65-0.87] |
| Sleep Disordered Breathing | 0.70 [0.57-0.84] | 0.59 [0.44-0.74] | 0.62 [0.46-0.78] |
| Mood Disturbance | 0.56 [0.33-0.77] | 0.53 [0.30-0.73] | 0.54 [0.29-0.78] |
| Valence | 0.56 [0.54-0.59] | 0.53 [0.50-0.56] | 0.54 [0.51-0.56] |
| Arousal | 0.55 [0.52-0.57] | 0.52 [0.49-0.55] | 0.55 [0.52-0.58] |
| Average | 0.67 $\pm$ 0.09 | 0.63 $\pm$ 0.09 | 0.63 $\pm$ 0.10 |
| Regression - MAE ($\downarrow$) |  |  |  |
| Apnea/Hypopnea Index $>$ 3% | 12.97 [11.87-14.05] | 13.07 [11.92-14.25] | 12.86 [11.79-13.94] |
| Apnea/Hypopnea Index $>$ 4% | 10.56 [9.59-11.62] | 10.79 [9.85-11.83] | 10.65 [9.62-11.68] |
| Gestation Age | 6.05 [5.91-6.17] | 6.10 [5.94-6.24] | 6.17 [6.02-6.30] |
| Systolic BP (VV) | 14.65 [12.50-16.78] | 15.10 [13.10-17.21] | 14.95 [12.87-17.01] |
| Diastolic BP (VV) | 8.29 [6.61-10.22] | 9.20 [6.93-11.12] | 8.95 [6.72-10.95] |
| Systolic BP (PPG-BP) | 14.39 [12.53-16.45] | 16.70 [14.25-19.38] | 16.20 [13.73-18.85] |
| Diastolic BP (PPG-BP) | 8.71 [7.18-10.01] | 9.48 [8.24-10.90] | 9.32 [7.90-10.69] |
| Average HR | 4.00 [3.34-4.67] | 4.76 [3.94-5.86] | 4.71 [3.86-5.60] |
| HR | 11.53 [11.40-11.66] | 12.86 [12.73-12.99] | 12.20 [12.07-12.34] |
| Average | 10.12 $\pm$ 3.47 | 10.89 $\pm$ 3.73 | 10.76 $\pm$ 3.57 |

## Appendix E Effect of Skin Tone

Figure: Figure 22: Detailed skin tone analysis for Blood Pressure estimation (VV dataset).
Refer to caption: x66.png

## Appendix F 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., ), BYOL (Grill et al., ), and masked autoencoders (MAE) (He et al., ). Timeseries-specific objectives like TNC and TF-C have also shown promise (Tonekaboni et al., ; Zhang et al., ). SSL has gained traction in the domain of physiological signal analysis, with applications to health records (Chen et al., ; Yèche et al., ), fitness and personalization (Spathis et al., ), as well as brain (Cheng et al., ) and heart signals (Kiyasseh et al., ; Sarkar & Etemad, ).

However, despite the popularity of SSL, there are no widely used pre-trained models for PPG data. A recent study (Abbaspourazad et al., ) demonstrated that embeddings derived from ECG and PPG signals can generalize across multiple health outcomes using proprietary large-scale data. While this work showcased the potential of foundation models for physiological signals, it was based on a single 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., ) on the UK Biobank dataset showed that embedding PPG signals can improve genetic discovery 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 on genetics. Another work on the same data showed that PPG embeddings are promising for cardiovascular risk prediction (Weng et al., ). SiamQuality (Ding et al., ) also trained an unreleased model on 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 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 to 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 to learn universal representations that can be applied across various domains. For instance, Chronos (Ansari et al., ) was trained on an impressive 84 billion observations (analogous to tokens in NLP) from 28 distinct datasets. However, it’s notable that this diverse collection does not include physiological data. Similarly, Moment (Goswami et al., ) was trained on billions of observations from a wide-ranging dataset that includes weather, traffic, energy, and other domains. While Moment does incorporate a small amount of ECG data, it comprises only a tiny percentage of the overall data pool.

In contrast to these generic approaches, our work takes a domain-specific focus. We curate a large pre-training and evaluation benchmark dedicated exclusively to PPG data. While knowledge gained from generic time series foundation models may transfer to domain-specific tasks like PPG, we expect the performance to be limited compared to a model trained specifically on PPG data. Furthermore, foundation models for ECG (McKeen et al., ; Song et al., ) or EEG (Yuan et al., ) have shown promise but transferring from one domain-specific model (e.g., ECG) to another (PPG) is likely to be even more challenging, as the underlying signal characteristics can be quite different. Building on the increasing interest in modality-specific 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., ). However, despite promising results, LLMs struggle with high-dimensional signals due to their text-based processing (Spathis & Kawsar, ). A modality-specific encoder like PaPaGei addresses this limitation by providing representations of raw signals (Belyaeva et al., ), which can be combined with text and fed into more powerful multimodal foundation models, such as AnyMAL (Moon et al., ). This approach offers several advantages: computational efficiency through a fixed LLM, flexibility due to the modular design of encoder, adapter, and LLM components, and interoperability with other high-performing models (e.g., a state-of-the-art IMU encoder (Yuan et al., )). Crucially, this encoder-LLM 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 applications, and as a modality encoder in next-generation frontier models. This versatility positions it as a valuable tool for advancing multimodal sensory AI systems.

## Appendix G Extended Discussion

Our results show 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%-4.9%, respectively (Section 5.1). PaPaGei-S performed best for cardiovascular parameters like BP, Hypertension, and Avg. HR, which are closely linked to components such as sVRI and IPA (Liang et al., ). Additionally, PaPaGei-P surpassed pre-trained baselines like Moment and is well-suited for tasks such as Smoking and Arousal.

By ablating different components of PaPaGei-S (Section 5.2), we found that the full model performs best, with sVRI contributing the most. Adding IPA or SQI separately did not improve performance, suggesting that (a) IPA and SQI positively transfer in a multi-task setup, and (b) our design choice to include both to compensate for situations where IPA cannot be computed is effective (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 (Abbaspourazad et al., ), 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 cloud-based inference for health data. Additionally, PaPaGei-S is more data-efficient for linear probing, showing greater performance gains with increased data availability, making it a promising backbone for small studies in future research.

Our studies in Section 5.3 reveal that PaPaGei-S embeddings are more dispersed across participants, enhancing performance, while regression predictions more accurately reflect the true distribution. We attribute this to our positive pair selection, which chooses positive pairs across individuals based on sVRI. Moreover, our skin tone analysis shows that the method performs better on 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.

## Peer Reviews

confidence: 4
contribution: 3
initial_rating: 8
presentation: 4
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?
2. Regarding the out-of-domain datasets, do they all use different devices from the ones in the pretraining datasets?
3. And have you looked into other biases that could be present in the datasets (sex, age, gender, weight, health conditions)?

Some other suggestions:
- Report the train and validation training loss curves for both models would help understand the convergence stability in both approaches
- Make sure to reference all the appendices in the main text
soundness: 4
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)
2. 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. 
3. The methods and results are well explained and covered, and are accompanied by relevant tables and figures to facilitate comprehension and readability
4. The framework presented can have practical implications for cardiovascular monitoring and be used for several applications
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.
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.
2. 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.

---

confidence: 4
contribution: 1
initial_rating: 3
presentation: 2
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!

2) 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?
soundness: 3
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.
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.
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.

Second, 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.


Third, 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.  


Fourth, 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? 
For 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. 


Fifth, 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.


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----Post rebuttal----

I have decreased my rating from 3 to 1 after identifying significant technical flaws in the implementation.

---

confidence: 4
contribution: 2
initial_rating: 8
presentation: 3
questions: * Do all datasets contain single-channel PPG? If yes, it is worth mentioning in the paper.
* 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
* 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. 
* 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?
soundness: 3
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. 
* 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.  
* 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.
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.
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:
    * 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: 
         * 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). 
         * 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).
    * 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. 
    * 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. 
    
* 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:
    * 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.
    *  Providing P-values and n for significance comparisons is essential. Similar comment for other comparisons such as Table 4, Figures 4, 5 and 6.
* 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:
    * 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). 
    * Also kinda related to this, there’s no mention of how and what temperature was selected.


[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.

[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.

[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.

[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.

---

confidence: 5
contribution: 3
initial_rating: 6
presentation: 4
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: 

1. 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. 
2. 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).
3. 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).   
4. 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). 
5. 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).  
6. 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.  
7. 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.
soundness: 3
strengths: ## This paper represents strong contributions in the following areas: 
### Originality: 
Although 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.  

**Furthermore the authors should be commended for two important things which, combined, have not been published previously:**
1. 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).  
2. They shared their codebase directly in the submission, which is organized and sufficiently well-commented to understand in a reasonable amount of time. 

### Clarity
The paper is well written, with clear descriptions and explanations in several key areas: 
1. Data set provenance (Appendix B is very helpful), and the purpose(s) for which each data set was used;
2. Data pre-processing;
3. High-level strategy and technical details of the pre-training method and loss/objective functions;
4. Downstream task evaluation and performance reporting (the subset of cases that I checked were straightforward to evaluate and understand);
5. 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. 

### Significance 
1. 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. 
2. PPG foundation models, in particular open/reproducible foundation models, have the potential for high impact in both health research and practical applications. 

### Quality
Claims and conclusions were generally well-reasoned and justified, with some key exceptions (discussed in the following section).
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.  

The 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. 

In 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).
weaknesses: # Brief Summary #
This 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:
1. Absence of downstream evaluation on basic demographic targets such as age, height/weight, BMI or sex. 
2. [Most concerning] Inappropriate choice of baseline model (RF using basic signal statistics) for performance comparison with their models. 
3. 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. 



# Additional Discussion Details #

## Issue 1: Absence of downstream evaluation on basic demographic targets
PPG 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. 

### Suggestions to address Issue 1:
Several 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). 


## Issue 2 [Most concerning]: Inappropriate choice of baseline model

The 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. 

Lastly, 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).  

The 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.  


### My Findings regarding demographic (and constant/mean) predictor performance on a subset of the public PPG datasets: 

I 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): 

1. **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). 
2. **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)
3. **Avg HR** Estimation (regression): I observed **MAE =8.11 (much worse than any reported model)** with a demographics-only predictor. 
4. **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)
5. **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)

I 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. 


### Suggestions to address Issue 2:
1. 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. 
2. 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.  


## Issue 3:  Poor evidence for regression models capturing the distribution tails. 

I 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)


### Suggestions to address Issue 3:

1. 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. 
2. 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. 
3. 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. 
4. Repeat the same Bland-Altman plots for the other regression tasks (gestational age and BP).

Meta-review: This paper was reviewed by four experts in the field and received 8, 1, 8, 8 as the final ratings. The reviewers acknowledged that this work presents the first open-source foundation model for PPG, trained on open datasets with open-source code, the methods and results are well explained, and are accompanied by relevant tables and figures, and that the proposed method can have practical implications for cardiovascular monitoring and be used for several applications. 

The reviewers mentioned that the F1 score and $R^{2}$ should also be used as evaluation metrics, besides AUROC and MAE. In the rebuttal, the authors have analyzed the performance of PapaGei in terms of the F1 score and $R^{2}$, demonstrating its robustness and versatility for classification and regression tasks. It was also mentioned that p-values and statistical tests of significance should be provided to evaluate the proposed method against the baselines. In the rebuttal, the authors have performed the Wilcoxon Signed Rank test to evaluate the statistical significance of PaPaGei at 90% and 95% confidence levels. Other queries regarding discussion on foundation models for physiological signals, evaluation of demographic characteristics, and the reason for PaPaGei-S outperforming PaPaGei-P have all been addressed convincingly by the authors in the rebuttal. 

Reviewer MitC had raised a couple of concerns about the implementation of the proposed method. This was discussed extensively with the authors during the rebuttal period and also between the reviewers and the AC during the post-rebuttal discussion period. The main concerns were the following: (i) the projection head of SimCLR is non-linear, however, that has not been used in the implementation; and (ii) after the training of SimCLR, the non-linear projection layers should be discarded from the architecture and only the encoder should be used during inference; however, these components were not discarded, as per the implementation. Reviewer XN3M also agreed that the implementation does not follow the SimCLR algorithm rigorously. However, this reviewer also mentioned that the comparison across the baselines is still fair, since the same implementation strategy was used for all the baseline methods as well. The only issue is that it may not be appropriate to refer to this as "SimCLR", since some of the algorithmic details of the vanilla SimCLR were not rigorously followed in the implementation. The AC agrees with this argument and does not consider this to be a reason to reject the paper. 

Reviewers DtmN, XN3M and d898 have a strong positive opinion about the paper and its contributions. Reviewer XN3M has even commended the authors' efforts in revising their paper and addressing his concerns within a short time window. Based on the reviewers' feedback, the decision is to recommend the paper for acceptance to ICLR 2025. The authors are encouraged to carefully look into the issues mentioned by Reviewer MitC (discussed above) and revise their writing accordingly for the final version of the paper. We congratulate the authors on the acceptance of their paper!

Decision: Accept (Poster)