Inferring mood disorder symptoms from multivariate time-series sensory data
Abstract
Mood disorders are increasingly recognized among the leading causes of disease burden worldwide.
Depressive and manic episodes in mood disorders commonly involve altered mood, sleep, and motor activity.
These translate to changes in sensory data that wearable devices can continuously and affordably monitor, thereby positioning themselves as a promising candidate to model mood disorders.
Previous similar endeavors cast this problem in terms of binary classification (cases vs controls) or regress the total score of some commonly used psychometric scale.
Nevertheless, these approaches fail to capture the variability within symptom domains described at the item level in psychometric scales.
In this work, we attempt to infer mood disorder symptoms (e.g., depressed mood, insomnia, irritability) from time-series data collected with the medical grade Empatica E4 wristbands, as part of an exploratory, observational, and longitudinal study.
We propose a multi-label framework to predict individual items from the two most widely used scales for assessing depression and mania.
We experiment with two different approaches to preprocess the high-dimensional and noisy sensory data and attain results within a clinically acceptable level of error.
Introduction
Mood disorders, also referred to as affective disorders, are a group of diagnoses in the Diagnostic and
Statistical Manual 5 th edition [2] classification system, ranked among the top 25 leading causes of disease burden worldwide [18]. These are characterized by depressive and/or (hypo)manic episodes that have disturbances in mood, sleep patterns, and motor activity as predominant features.
Such alterations correlate with changes in physiological parameters that wearable devices can continuously and affordably record in a patient’s natural environment.
Against the backdrop of a disappointingly limited clinical translation of psychiatric genetics and neuroscience research [9], the community has been looking to digital biomarkers from wearables as an alternative (complementary) paradigm [10].
The question we pursue is to which degree time-series data can be used to infer mood disorder symptoms pertaining to depression and mania, the two polarities of mood disorders, as described with
Hamilton Depression Rating Scale-17 (HDRS) [8] and the Young Mania Rating Scale (YMRS) [24] respectively.
These are 17-item and 11-item questionnaires used to measure depressive and manic symptoms respectively and take about 20 minutes each to administer.
Questionnaire individual items are not scored on the same scale but different items have different rank numbers and some have a step-size of two between consecutive ranks to reflect that these should weigh more when computing the scale total score, i.e. the sum over individual items’ score (ranging from 0 to 52 and from 0 to 60 for HDRS and YMRS respectively).
A complete account of HDRS and YMRS items is given in Table A.1 and Table A.2 respectively.
The nature of symptom domains mapped with HDRS and
YMRS varies from mood, sleep, and psychomotor activity.
Our contribution is twofold.
First, we cast depression and mania prediction in a multi-label learning framework, where we aim to infer each item in HDRS and YMRS.
These fine-grained predictions in individual items in the psychometric scales are very desirable, as they provide a richer symptoms description, i.e. they clarify which symptom domains characterize the ongoing mood episode rather than a mere yes-or-no diagnosis.
To the best of our knowledge, this is the first work to predict individual items in HDRS and YMRS for mood disorders.
The majority of previous related works, adopted a binary (e.g., disease vs control, or acute vs remission) classification framework [5, 16] only one attempted to regress the HDRS total score [7].
However, patients with the same total score on YMRS and HDRS might have different clinical presentations.
Take for example the case of two patients with the same depression severity level but one displaying psycho-motor agitation while the other psycho-motor retardation (two items from HDRS): this clinically obvious difference would go lost when reducing a psychometric scale to its total score and, as a result, it would not be used to personalize treatment.
As an additional contribution, further to the standard approach of time-aligning multivariate time-series during preprocessing [1, 13], we experiment with learning an embedding representation of the raw recordings.
Multi-label learning of mood disorder symptoms 2.1 Study population & assessment
The analyses that follow are based on an original dataset being collected as part of a prospective, exploratory, observational, single-center, longitudinal study with a fully pragmatic design embedded into current real-world clinical practice.
This study was conducted in accordance with the ethical principles of the Declaration of Helsinki and Good Clinical Practice and the Hospital Clinic Ethics and Research Board (HCB/2021/104).
All participants provided written informed consent prior to their inclusion in the study.
All data were collected anonymously and stored encrypted in servers complying with all GDPR and HIPAA regulations.
Patients with an ongoing mood episode, patients with a historical mood disorder diagnosis clinically stable at present, and healthy controls are recruited.
While healthy controls and euthymic patients are assessed only once, measurements on patients with a mood episode are taken at four timepoints: acute phase (upon hospital admission or at the home treatment unit), response onset (usually mid-admission), remission (end of admission or soon after discharge), and recovery ( ∼ 2 months into sustained remission).
During each assessment, participants are interviewed by a specialized psychiatrist collecting socio-demographic and treatment info as well as YMRS and HDRS scores.
At the start of the clinical interview, participants are provided with an E4 Empatica wristband 2 and they are required to wear for about 48 hours.
E4 devices have sensors collecting the following physiological data (sampling rates): 3D acceleration (ACC, 32Hz), inter-beat intervals (IBI, i.e. the time between two consecutive heart ventricular contractions), skin temperature (TEMP, 4Hz), blood volume pressure (BVP, 64Hz), electrodermal activity (EDA, 4Hz), and heart rate (HR, 1Hz).
In total, data obtained from 25 healthy controls and 64 patients with mood disorders are used in this work, and an overview of the clinical-demographic variables of the population is available in Table 1.
Figure A.1 shows the number of recording sessions by diagnosis.
2.2 Preprocessing
The raw data from an E4 Empatica recording session comes as a collection of recorded channels: ACC (3-dimensional), EDA, BVP, IBI, HR, and TEMP. IBI is computed from BVP signals and the reliability of its derivation depends upon the level of movement exhibited by the wearer 3 .
Since IBI [Table]
is mostly used to study heart rate variability, which is not the focus of this study, we thus decided to exclude the channel from this work.
Data from wearable devices (especially in a naturalistic setting) is inherently noisy, we, therefore, quality-controlled our data with the rules by Kleckner et al.
[12] and the addition of a rule to remove HR values that exceed the physiologically plausible range (25bpm).
Each recording session was segmented using a sliding window of w and enforcing no overlap between bordering segments. After a grid search on w ∈ (4 , 2048) [Table] we found that a segment length of w = 64 seconds to be the optimal size.
Since the sampling rate varies across different channels [Table] the raw recordings were time-aligned to the level of a second in wall time.
This method was used in a number of works that involve sensory data [1, 13], nevertheless, the down-sampling process (usually via max-pooling or averaging) can risk removing useful information in the raw recordings.
As an alternative, we instead learned a latent representation for raw channel data, such dimension reduction approach has been proved effective in other tasks such as language and signal processing [15, 19].
To that end, we used either a fully-connected layer or a Gated Recurrent Unit (GRU) [4] layer to learn an embedding for each channel of the same dimensionality, i.e. 128.
We could then concatenate the embeddings and feed them to the regression model in the same manner as the time-aligned data.
2.3 Experimental Design
We randomly divided each recording session from our dataset into train, validation, and test sets with a ratio of 70-15-15.
We took a hard parameter-sharing approach to the multi-label problem, i.e. all 28 items shared the same model trunk, and thus the same base representation of the input data, but had one task-specific output layer.
We experimented with a bidirectional Long Short-Term
Memory (BiLSTM) model [20] with 256 hidden units and tanh activation followed by a dense layer to output 28 items.
The model was trained to minimize the sum of mean squared error (MSE) from individual items weighted proportionally to the step size between consecutive ranks.
The channel embeddings and the model were trained with Adam optimizer [11] end-to-end, with early stopping, for a maximum of 100 epochs.
The codebase is available at github.com/INTREPIBD/TS4H2022.
Results
Towards comparability with previous studies and easier clinical interpretation, we computed the
Root Mean Square Error (RMSE) on YMRS and HDRS in terms of total scores, i.e. RMSE over the sum of YMRS and HDRS, and item average, i.e. the average per-item RMSE over YMRS and
HDRS.
The results are shown in Table 2.
Models trained with embedding representation of the raw recordings result in better test performance across both HDRS and YMRS, with GRU outperforming
MLP embeddings (RMSE on YMRS total score = 5.6340; RMSE on HDRS total score = 4.4089).
An illustration of the per-item test performance (average and standard deviation of the residuals, i.e. signed difference between predicted and observed item values) is provided in Figure 1.
Residuals are generally lower on the HDRS items.
Some items stand out as having either very high (e.g. YMRS-6,
[Table]
Note that certain items (e.g. YMRS-6 and YMRS-8) have an interval of 2, which, naturally, can lead to larger prediction errors.
Table A.1 and Table A.2 list the items in HDRS and YMRS.
YMRS-8, and HDRS-7) or very low (e.g. YMRS-9, HDRS-16) residuals.
We should consider, however, that YMRS and HDRS are not designed to have the same range and have indeed as expected different distributions in our sample (Table 1).
Furthermore, items admit different rank numbers (3, 4, or 5) and step-size (1 or 2) between consecutive ranks.
Still, it is noteworthy that some comparable items have indeed quite different residuals distribution.
Discussion
It should be noted that acquiring data from a psychiatric population is a laborious process.
On this note, previous studies trying to infer mental states from wearable device data in an actual psychiatric cohort had a sample size varying from about a dozen to only a few dozen subjects [1, 5, 6, 7, 21].
On the other hand, our analyses used a large sample size (N = 89).
With reference to our research question, our results show that regressing YMRS and HDRS with a clinically acceptable error is viable.
To provide some clinical perspective, it is common practice in psychiatry to discretize YMRS and HDRS total scores in five symptom severity bands.
A five and three-point interval are the smallest bin widths for YMRS and HDRS respectively [3, 14], e.g. a YMRS total score in the range of [20 - 25] is considered a mild mania, and an HDRS total score in [19 - 22] is considered as severe depression.
This shows that on average our model would be off by two score bands at most, in case of a true score falling on the edge of a tight severity bin (i.e. the ones reported above).
Furthermore, our framework goes one step beyond merely providing YMRS and HDRS total scores since it outputs individual item ranks.
This is of great clinical relevance since significant clinically meaningful information would be overlooked when reducing the fine-grained psychometric scales into a single score.
On a more technical note, we noted that learning a latent representation of each channel can outperform time-aligning raw recordings, the preferred approach in previous works.
4.1 Future works
While it is common in the psychiatry literature to treat YMRS and HDRS items as continuous variables [17], each item is actually ordinal.
For instance, YMRS-4 evaluates the sleep quality of the subject, from no decrease in sleep (rank 0) to denying the need for sleep (rank 4), which has a natural ordering.
Therefore, instead of regressing on each item as nominal scales, ordinal regression can take advantage of the ordered nature of the psychiatric scales [22].
Secondly, linking the item residuals to other clinical-demographic variables available in our dataset is another future research direction.
Thirdly, as psychometric measurements are noisy and prone to low rate of inter-specialists as well as intra-specialist agreement [23], unsupervised approaches would seem a promising pathway to data representations associated with clinically meaningful outcomes.
Lastly, even though we ensured there were no overlapping segments, data from the same recording session could exist in the train, validation, and test sets depending on the random shuffle operation, where no-disease relevant features can potentially be learned by the model.
A crucial direction of work is to assess out-of-sample performance, i.e. examine whether a model developed on a given population can generalize to new, previously unseen subjects. Acknowledgments and Disclosure of Funding
This study was supported by a grant from Baszucki Brain Research Fund and the Instituto de
Salud Carlos III (FIS PI21/00340, TIMEBASE STUDY).
BML and FC are supported by the United
Kingdom Research and Innovation (grant EP/S02431X/1), UKRI Centre for Doctoral Training in
Biomedical AI at the University of Edinburgh, School of Informatics.
GA is supported by a Rio
Hortega 2021 grant (CM21/00017) from the Spanish Ministry of Health financed by the Instituto de Salud Carlos III (ISCIII) and co-financed by the Fondo Social Europeo Plus (FSE+).
Diego
Hidalgo-Mazzei’s research is supported by a Juan Rodés grant (JR18/00021) ISCIII.
A Appendix
[Table]
[Table]
characterized
Depressive
Disorder (BD characterized presence a clinically stable present be