1. Introduction
Depression is one of the prevailing mental health disorders, affecting approximately 280 million people worldwide [
1,
2]. In the wake of the crippling effects of the pandemic, the global burden of depression continued to worsen, by at least 27.6% on average. This debilitating disorder differs from general mood fluctuations and fleeting emotional reactions along the aspects of intensity, recurrence, and anhedonia [
1,
2]. Depression often manifests owing to a complex interaction between socioeconomic, psychological and biological factors, and is exacerbated by comorbid physical conditions in an adverse feedback loop. Early screening is critical for timely intervention and the delivery of effective treatment methods such as cognitive behavioral therapy and/or antidepressant medications. However, the social stigma associated with mental health, the scarcity of trained health-care providers, relatively high rates of misdiagnoses and exorbitant service costs discourage individuals from seeking assistance [
3].
To improve diagnoses fidelity, it is vital to develop universal screening approaches which reveal useful information without being intrusive or biased, as is the case with the currently adopted surveys [
4]. With the proliferation of machine learning (ML) methods in the healthcare domain, where the automatic identification of relevant patterns and relationships among data without specification of a priori hypotheses holds considerable prognostic utility, there is the potential for detecting the presence of elusive disorders such as depression. In parallel, the ubiquity of smartphones and wearable devices such as smartwatches and fitness trackers offer the much sought-after capabilities of long-term unobtrusive monitoring in free-living conditions. Recent studies have employed ML while leveraging passive, non-intrusive modalities such as smartphone call/text logs [
4], social media posts [
5], gyroscope readings [
6] GPS [
7] and heart rate [
8] for detecting states of depression and mental health distress.
These worthwhile approaches show the potential for depression screening using passive smartphone data while maintaining a fair degree of user privacy. Out of the different parameters measured, those acquired through wearable devices enable continuous and objective monitoring of patients [
9]. Moreover, physiological signals, or a combination of them in tandem with ML, can predict symptoms of depression and anxiety [
10,
11]. This fact, coupled with the increasing ownership of wearables, estimated at 1 billion in 2022, suggest that personalized screening be introduced without revealing any user-specific information as opposed to partial alternatives such as location data or personal message history.
Noticeably, in the most recent literature, the sample population typically consists of depressed and completely healthy participants, and physiological signals are compounded with smartphone-derived biomarkers such as device usage, step count, sleep measures and location. In this work, we aim to quantify the effects of using only unimodal and multimodal signals from wearable sensors in terms of heart rate (HR), galvanic skin response (GSR) and accelerometry (ACC) among a predominantly depressed population.
Another facet of emotional state assessment which can benefit from wearable monitoring is affective experience quantification. As purported by [
12], the interplay between the severity of depression and affective emotional activation in terms of valence and arousal can have implications for individual behavior and response to daily stimuli [
13]. This manifests a strong influence on processes such as attention, perception, decision-making, learning and mental well-being [
14]. Valence captures the extent to which an emotion is positive/negative, while arousal captures the intensity of the experienced emotion. Thus, we also explore this domain as an additional aspect of our central work.
The primary contributions of this work are the following:
Validating the potential of implementing ML algorithms with retrospectively collected wearable-derived physiological data for classifying between moderately and severely depressed individuals.
Assessing the quality of low frequency, general signal features extracted using discrete wavelet transforms (DWT) for developing ML algorithms.
Examining the relative efficacies of heart rate, galvanic skin response and accelerometry readings in distinguishing between depression severity and emotional states.
Investigating the role of depression severity in emotional valence and arousal detection.
This paper is organized such that
Section 2 introduces the dataset and outlines the methodology and
Section 3 presents the results and its discussion, with
Section 4 concluding the work.
4. Discussion
The goal was to separate behaviors of people suffering from depression based on emotional stimuli and possibly identifying affective emotional triggers in response to experienced events. As mentioned previously, there is a prevalence of depression among all surveyed individuals in this dataset who also experience higher-intensity emotional states. This warrants an investigation of the impact of depression in dampening/exacerbating emotional responses to various stimuli or events. As reported by [
20], states of depression induce marked dysfunctional regulations of affective experience and affective quality perception. Generally, the neutral arousal/valence state is the most commonly experienced [
21], because of the impaired emotional modulation to affective stimuli [
22].
The achieved performance measures are in line with a recent study conducted with a similar demographic (race, age and education levels) asserting the potentially useful yet limited predictability of depression severity with wearable devices [
23]. It appeared that greater severity of depressive symptoms showed associations with larger variation of night-time heart rate between the hours of 4 a.m. and 6 a.m. Additionally, this led to the findings (adjusted for covariates) that severity was also correlated robustly with weekday circadian activity rhythms. Thereby, our work focusing on individuals during conscious ESM activities serves as a complementary study to the general continuous diurnal and nocturnal biomarkers evaluated in [
23].
According to [
24], low-arousal states being associated with low-valence fine-grained states such as sadness, lethargy or fatigue is correlated with stronger levels of depression. This is because high arousal occurs when the cortical circuits in the brain are engaged and allocates attention in response to a particular stimuli [
25]. However, our results do not agree with these findings, leading to our hypothesis that either the self-reported BDI-II scores were not reflective of the true underlying mental state, or that the self-reported valence and arousal is overly positive by choice of omission. A potential psychological link unifying our results in light of the previous studies is the theory of ambivalence over emotional expression. This is a condition wherein individuals have the propensity to avoid expression of emotions [
26], owing to the effects of depression. In [
27], it is rationalized that inciting high-arousal states in people with heightened depression thorough memory recalling tests increases help-seeking intentions. It could very well be that the stimuli or events experienced by the participants of the DAPPER dataset creation were involved in positive or familiar environments during the course of study.
Although the heterogeneous nature of different consumer-grade wearables occasionally leads to likely noise saturation, inaccurate values and uncalibrated errors [
28], it is rationalized that the characteristic patterns associated with certain mental/physical diseases can indeed be reflected [
29]. Additionally, it is not known if the individuals were on prescribed anti-depressants, engaged in psychological counselling or under any treatment which may introduce confounding variables that cannot be adjusted for.
While Deep Learning has demonstrated exemplary results in several fields and recent studies, there are still few limitations and challenges in the biomedical domain pertaining to class imbalance and data complexity, which discouraged its use in our work. As put forward in [
30], Deep Learning models tend to capture spurious relations in the training data within clinical studies involving biomedical signals. This occurs, in context of this work, due to the implicit nature of some emotional states such as valence/arousal and depression, which do not manifest across all subjects with the same intensity or magnitude [
31]. Thus, it appears that higher volumes of data readings from wearables are necessary to combat the relative sparsity of the wearable measurements, i.e, the ratio of the duration of
normal physiological behavior to the duration of context-specific instantaneous responses to certain stimuli, and achieve higher performance scores.
Ref. [
32] also indicates that with skewed data, the models prioritize the majority group due to higher prior probability. Unlike with summarized measures such as wavelet decomposed features, augmenting continuous raw signals and the rectification of class imbalance requires more complex techniques to account for the profound understanding of the morphology and patterns [
33]. We believe the lightweight approaches (DWT + standard ML) are relatively more conducive towards power-efficient deployment on wearable or edge devices, as shown in [
34].
We envision this research as an initial baseline for performance benchmarking on the DAPPER dataset, as well as an assessment of the relationship between depression and ephemeral emotional states.
Author Contributions
Conceptualization, R.A., A.S. and F.A.; data curation, A.A. and S.G.; investigation, J.R., A.A. and S.G.; methodology, J.R.; project administration, R.A. and A.S.; resources, F.A. and A.S.; software, J.R.; supervision, R.A., A.S. and F.A.; validation, J.R., A.A. and S.G.; writing—original draft, J.R., A.A. and S.G.; writing—review and editing, R.A., A.S. and F.A. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
A consent has been obtained from the patient(s) to use this dataset for research purposes and publishing this paper.
Data Availability Statement
Conflicts of Interest
The authors declare no conflict of interest.
Abbreviations
The following abbreviations are used in this manuscript:
ACC | Accelerometry |
BDI-II | Beck’s Depression Inventory |
CB | CatBoost |
DAPPER | Daily Ambulatory Psychological and Physiological recording for Emotional Research |
DWT | Discrete Wavelet Transforms |
GSR | Galvanic Skin Response |
HR | Heart Rate |
KNN | K-Nearest Neighbors |
LGB | Light Gradient Boosting |
ML | Machine Learning |
RF | Random Forest |
SVC | Support Vector Classifier |
XGB | eXtreme Gradient Boosting |
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