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Depressive and manic episodes within bipolar disorder (BD) and major depressive disorder (MDD) involve altered mood, sleep, and activity, alongside physiological alterations wearables can capture.
Firstly, we explored whether physiological wearable data could predict (aim 1) the severity of an acute affective episode at the intra-individual level and (aim 2) the polarity of an acute affective episode and euthymia among different individuals. Secondarily, we explored which physiological data were related to prior predictions, generalization across patients, and associations between affective symptoms and physiological data.
We conducted a prospective exploratory observational study including patients with BD and MDD on acute affective episodes (manic, depressed, and mixed) whose physiological data were recorded using a research-grade wearable (Empatica E4) across 3 consecutive time points (acute, response, and remission of episode). Euthymic patients and healthy controls were recorded during a single session (approximately 48 h). Manic and depressive symptoms were assessed using standardized psychometric scales. Physiological wearable data included the following channels: acceleration (ACC), skin temperature, blood volume pulse, heart rate (HR), and electrodermal activity (EDA). Invalid physiological data were removed using a rule-based filter, and channels were time aligned at 1-second time units and segmented at window lengths of 32 seconds, as best-performing parameters. We developed deep learning predictive models, assessed the channels’ individual contribution using permutation feature importance analysis, and computed physiological data to psychometric scales’ items normalized mutual information (NMI). We present a novel, fully automated method for the preprocessing and analysis of physiological data from a research-grade wearable device, including a viable supervised learning pipeline for time-series analyses.
Overall, 35 sessions (1512 hours) from 12 patients (manic, depressed, mixed, and euthymic) and 7 healthy controls (mean age 39.7, SD 12.6 years; 6/19, 32% female) were analyzed. The severity of mood episodes was predicted with moderate (62%-85%) accuracies (aim 1), and their polarity with moderate (70%) accuracy (aim 2). The most relevant features for the former tasks were ACC, EDA, and HR. There was a fair agreement in feature importance across classification tasks (Kendall W=0.383). Generalization of the former models on unseen patients was of overall low accuracy, except for the intra-individual models. ACC was associated with “increased motor activity” (NMI>0.55), “insomnia” (NMI=0.6), and “motor inhibition” (NMI=0.75). EDA was associated with “aggressive behavior” (NMI=1.0) and “psychic anxiety” (NMI=0.52).
Physiological data from wearables show potential to identify mood episodes and specific symptoms of mania and depression quantitatively, both in BD and MDD. Motor activity and stress-related physiological data (EDA and HR) stand out as potential digital biomarkers for predicting mania and depression, respectively. These findings represent a promising pathway toward personalized psychiatry, in which physiological wearable data could allow the early identification and intervention of mood episodes.
Mood disorders, including bipolar disorder (BD) and major depressive disorder (MDD), are ranked among the top 25 leading causes of disease burden worldwide [
Biomarkers are characteristics that are measured as an indicator of pathogenic processes (disease-associated biomarkers) or responses to an exposure or intervention (drug-related biomarkers) [
In the last decade, there has been an exponential growth in the number of digital biomarker studies in the health domain, especially in cardiovascular and respiratory diseases [
Wearables collecting actigraphy, the noninvasive method of monitoring human rest and activity [
The conjuncture of advances in machine learning [
A prospective exploratory observational study with 3 independent groups (
The inclusion criteria were as follows: (1) aged above 18 years; (2) having a diagnosis according to the DSM-5 [
Study design and recordings. BD: bipolar disorder; HC: healthy controls; HDRS: Hamilton Depression Rating Scale; MDD: major depressive disorder; SCID: Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders; T0: current acute Diagnostic and Statistical Manual of Mental Disorders–5 affective episodes; T1: symptoms’ response; T2: symptomatic remission; YMRS: Young Mania Rating Scale.
The following sociodemographic variables were collected: age, sex, DSM-5 psychiatric diagnoses [
When choosing a wearable device for a research project, there are several factors that should be considered, including (1) the signals of interest to be captured (eg, stress-related and actigraphy); (2) the users who will be studied (eg, inpatients, outpatients, and HCs); (3) the pragmatic needs of the study (eg, budget, battery life, placement of the devices, and confidentiality of participants); (4) establishing assessment procedures (eg, stress elicitation task, resting, and sleep); and (5) performing qualitative and quantitative analyses on resulting data (eg, visually inspecting the data registered, quantifying data loss, assessing the quality of data, and comparing the data of different wearable devices) [
For each recording, patients and HCs were provided with an E4 wristband [
The E4 wristband has sensors that collect physiological data at different sampling rates. The physiological data signals from each recording session were collected from the following channels and sampling rates as raw data: 3D acceleration (ACC) in space over time on an x-, y-, and z-axis (ACC, 32 Hz); EDA (4 Hz); skin temperature (TEMP, 4 Hz); and blood volume pulse (BVP, 64 Hz); or in a processed format: interbeat intervals (IBIs, the time between 2 consecutive heart ventricular contractions) and HR (1 Hz). The BVP signal is obtained using a photoplethysmography sensor that measures volume changes in the blood. Empatica uses 2 algorithms on the BVP signal to construct an IBI with which HR (and HRV) can be calculated. The 2 algorithms are optimized to detect heartbeats and discard beats that contain artifacts [
Owing to the naturalistic setting of the recording sessions, the data obtained from the E4 wristband are inherently noisy. For instance, some patients show low levels of compliance during an affective episode (eg, mania), which can lead to poor skin contact from the device, hence inaccurate readings for certain channels, or complete removal of the wearable device, resulting in unusable data. To that end, we removed invalid physiological data enforcing the rules-based filter by Kleckner et al [
We did not use IBI data because of the disproportionately high number of missing values (approximately 70%) relative to data from different channels [
To obtain an equal number of segments from each class for model evaluation, we randomly selected 20 segments from each session and stored them as a held-out test set, which was never observed by the model during either training or validation. We then randomly assigned the remaining segments to the train and validation sets with ratios of 80% and 20%, respectively. Each segment was normalized (scaled to [0, 1]) using the per-channel global (across all segments) minimum and maximum values derived from the train set.
Rules-based filter for invalid physiological data.
Rules | Filter for invalid data | Range |
1 | To prevent “floor” artifacts (eg, electrode loses contact with skin) and “ceiling” artifacts (circuit is overloaded)—EDAa not in a valid range | 0.05 to 60 µSb |
2 | EDA changes too quickly—EDA slope not in a valid range | −10 to +10 µS/second |
3 | Skin temperature suggests the EDA sensor is not being worn—skin temperature not in a valid range | 30 to 40 °C |
4c | HRd not in a valid range | 25 to 250 bpme |
5 | Transitional data surrounding segments identified as invalid via the preceding rules—account for transition effects | Within 5 seconds |
aEDA: electrodermal activity.
bµS: microsiemens.
cAddition to the algorithm used by Kleckner et al [
dHR: heart rate.
ebpm: beats per minute.
The recording segments produced with the preprocessing steps described earlier were used in supervised learning experiments as input to the supervised models. For aim 1, models were trained on 3-class classification tasks (T0-acute, T1-response, and T2-remission) for each individual on an acute affective episode (manic BD, depressed BD, depressed MDD, and mixed BD). For aim 2, one model was trained on a 7-class classification task (manic BD, depressed BD, mixed BD, depressed MDD, euthymic BD, euthymic MDD, and HCs).
Segments from each class under a given task were extracted in the same number to obtain perfectly balanced classes. As sets were designed to be perfectly balanced, we adopted accuracy as our primary metric but also reported the
As part of our exploratory data analysis, to quantify the association between physiological data and affective symptoms measured by the YMRS and HDRS scale items, their normalized mutual information (NMI) was computed.
For each task, with the exception of the one about distinguishing members of a group of only HCs, as we were interested in testing the degree to which a model can generalize to different individuals, unseen during training, and sharing the same psychiatric label (diagnosis and psychopathological status), we prepared a test set of segments from recordings collected from an independent group of individuals. Therefore, the model was tested on this extra, independent holdout set to obtain an estimate of the out-of-sample generalization performance.
We elected a Bidirectional Long Short-Term Memory (BiLSTM) model [
To assess the channels’ individual impact on the test set performance in the aforementioned tasks, we adopted a perturbation-based approach. For each channel at a time, we randomly permuted its values in the test set segments and computed the difference in performance relative to the baseline model. We chose this approach because it has a straightforward interpretation and provides a highly compressed, global insight into the importance of the channels. Agreement on channels’ relevance across different tasks was measured using the Kendall W.
The codebase was written in Python (version 3.8; Python Software Foundation), where the deep learning models were implemented in TensorFlow and developed on a single NVIDIA RTX 2080Ti. The repository for this study can be found on the internet [
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 before their inclusion in the study. All data were collected anonymously and stored encrypted in servers complying with all General Data Protection Regulation and Health Insurance Portability and Accountability Act regulations.
A total of 35 sessions from 12 patients (manic, depressed, mixed, and euthymic) and 7 HCs (mean age 39.7, SD 12.6 years; 6/19, 32% female) were analyzed, totaling 1512 hours recorded. The median percentage of data per recording session dropped from further analysis of quality control was 11.05 (range 2.50-34.21). A clinical demographic overview of the study sample is presented in
Clinical demographic overview of the study sample.
Diagnosis | Age (years) | Sex | HDRSa score | YMRSb score | ||||
|
|
|
T0c | T1d | T2e | T0 | T1 | T2 |
Manic BDf | 40 | Male | 5 | 4 | 4 | 24 | 8 | 2 |
Manic BDg | 21 | Male | 3 | 5 | 4 | 23 | 15 | 1 |
Depressed BDh | 33 | Male | 23 | 6 | 4 | 0 | 0 | 0 |
Depressed BDg,h | 36 | Male | 17 | 12 | 3 | 2 | 4 | 2 |
Mixed BD | 30 | Female | 8 | 4 | 4 | 30 | 20 | 5 |
Mixed BDg | 40 | Male | 11 | 2 | 1 | 29 | 10 | 3 |
Depressed MDDi | 57 | Male | 33 | 13 | 7 | 7 | 2 | 0 |
Depressed MDDg | 45 | Male | 27 | 11 | 7 | 4 | 1 | 1 |
Euthymic BD | 54 | Male | 3 | —j | — | 0 | — | — |
Euthymic BDg | 61 | Male | 1 | — | — | 3 | — | — |
Euthymic MDD | 60 | Female | 4 | — | — | 0 | — | — |
Euthymic MDDg | 60 | Male | 3 | — | — | 0 | — | — |
HCk | 32 | Female | 0 | — | — | 0 | — | — |
HCg | 34 | Male | 0 | — | — | 0 | — | — |
HC | 28 | Female | 0 | — | — | 1 | — | — |
HC | 29 | Male | 0 | — | — | 2 | — | — |
HC | 31 | Male | 2 | — | — | 1 | — | — |
HC | 32 | Female | 1 | — | — | 3 | — | — |
HC | 31 | Female | 0 | — | — | 1 | — | — |
aHDRS: Hamilton Depression Rating Scale.
bYMRS: Young Mania Rating Scale.
cT0: current acute Diagnostic and Statistical Manual of Mental Disorders–5 affective episodes or only register for euthymic patients and healthy controls.
dT1: symptoms’ response.
eT2: symptomatic remission.
fBD: bipolar disorder.
gThe recording segments extracted from the marked subjects were used to check the models’ ability to generalize to clinically similar subjects, unseen during training.
hAll registers performed at the hospitalization at home or outpatient units.
iMDD: major depressive disorder.
jEuthymic patients and healthy controls were recorded during a single session (T0).
kHC: healthy control.
The 3-class classification tasks (T0-acute, T1-response, T2-remission; accuracy expected by chance: 1/3=33%) to predict the severity of an acute affective episode showed accuracies ranging from 62% (depressed BD) to 85% (depressed MDD). The generalization models on unseen patients showed accuracies ranging from 28% (depressed MDD) to 57% (manic BD;
The permutation importance analysis for the classification tasks for aims 1 and 2 is shown in
Prediction of the severity of an acute affective episode: model and generalization on unseen patients.
Individuals with affective episodes and performance metric | Model | Generalization | |
|
|||
|
Accuracyb (%) | 70 | 56.67 |
|
0.6978 | 0.5279 | |
|
Precision | 0.6979 | 0.5381 |
|
Recall | 0.7000 | 0.5667 |
|
AUROCc | 0.6980 | 0.5432 |
|
|||
|
Accuracyb (%) | 61.67 | 41.67 |
|
0.6171 | 0.3968 | |
|
Precision | 0.6273 | 0.4085 |
|
Recall | 0.6167 | 0.4167 |
|
AUROC | 0.6115 | 0.4067 |
|
|||
|
Accuracyb (%) | 63.33 | 30 |
|
0.6333 | 0.2576 | |
|
Precision | 0.6333 | 0.3004 |
|
Recall | 0.6333 | 0.3068 |
|
AUROC | 0.6333 | 0.3012 |
|
|||
|
Accuracyb (%) | 85 | 28.33 |
|
0.8492 | 0.2451 | |
|
Precision | 0.8774 | 0.2581 |
|
Recall | 0.8500 | 0.2833 |
|
AUROC | 0.8672 | 0.2856 |
aBD: bipolar disorder.
bAccuracy expected by chance for a 3-class classification task is 1/3=33%. Thus, accuracies above 33% suggest that the model can predict outcomes better than random guessing, and higher values for accuracy indicate better predictive capacity of the model. Note that the test set was designed to have the same number of samples in each class. This is reflected in the values of
cAUROC: area under the receiver operating characteristic.
dMDD: major depressive disorder.
Permutation importance analysis. The height of the bars shows the change in accuracy at test time upon scrambling a channel through a random permutation of its values. A positive (negative) permutation importance value means that scrambling that channel results in a drop (increase) in accuracy relatively to the baseline where original (nonpermuted) values were used across all channels, that is, the channel’s permutation deteriorates (improves) the performance. A “0” permutation importance value indicates that a random permutation of the channel’s values does not affect accuracy in either direction. For instance, electrodermal activity (EDA) shows a positive change in accuracy of 40% for the intra-individual depressed BD severity prediction model; this means that removing this channel from the model would result in a decrease of prediction accuracy of 40%—from 62% to 22%—thus EDA is highly relevant for that model. Different colors correspond to the different tasks being investigated. ACC: acceleration; BD: bipolar disorder; BVP: blood volume pulse; HC: healthy controls; HR: heart rate; MDD: major depressive disorder; TEMP: temperature; T0: current acute Diagnostic and Statistical Manual of Mental Disorders–5 affective episodes; T1: symptoms’ response; T2: symptomatic remission.
The 7-class classification task (accuracy expected by chance: 1/7=14%) to predict the polarity of affective episodes and euthymia showed an accuracy of 70%. The best classifications were depressed and euthymic MDD, followed by depressed BD, and the worst was manic BD, followed by HCs. The generalization model showed an accuracy of 15.7% (slightly above chance). The classification task for 7 HCs showed an accuracy of 50% (
The most relevant channels for predicting the polarity of affective episodes, euthymia, and HCs among different individuals (aim 2) were EDA, followed by ACC, HR, and TEMP (all channels showed >30% permutation importance). The BVP channel permutation importance was approximately 0%. These results were highly similar for the classification task of 7 HCs, but EDA showed only 4.9% permutation importance (
Prediction of the polarity of an acute affective episode and euthymia among different individuals: model and generalization on unseen patients.
Individuals with affective episodes and performance metric | Model | Generalization | |
|
|||
|
Accuracyb (%) | 70 | 15.7 |
|
0.6927 | 0.1516 | |
|
Precision | 0.6889 | 0.1513 |
|
Recall | 0.6934 | 0.1517 |
|
AUROCc | 0.6900 | 0.1510 |
|
|||
|
Accuracyb (%) | 50 | —d |
|
0.4923 | — | |
|
Precision | 0.4911 | — |
|
Recall | 0.4988 | — |
|
AUROC | 0.4998 | — |
aHC: healthy control.
bAccuracy expected by chance for a 3-class classification task is 1/3=33%. Thus, accuracies above 33% suggest that the model can predict outcomes better than random guessing, and higher values for accuracy indicate better predictive capacity of the model. Note that the test set was designed to have the same number of samples in each class. This is reflected in the values of
cAUROC: area under the receiver operating characteristic.
dAs we were interested in predicting affective psychopathology, we tested the degree to which a model can generalize to different individuals for each task except for the one about distinguishing members of a group of only HCs.
The tile plots for the NMI between physiological data and the YMRS and HDRS scale items for the former intra-individual (aim 1) and between-individuals (aim 2) classification tasks are shown in
Tile plots for the normalized mutual information analysis between physiological data and psychometric scales’ items: intra-individual level. For each scales’ item the mutual information (MI) with respect to each of the channels was measured and scaled to 0 to 1 dividing by the maximum MI value for that item. Values of zero indicate no associations, values of 1 indicate the maximum recorded MI across all channels for an individual item. ACC_X: x-axis acceleration; ACC_Y: y-axis acceleration; ACC_Z: z-axis acceleration; BD: bipolar disorder; BVP: blood volume pulse; EDA: electrodermal activity; HDRS: Hamilton Depression Rating Scale; HR: heart rate; MDD: major depressive disorder; TEMP: temperature; YMRS: Young Mania Rating Scale.
Tile plot for the normalized mutual information analysis between physiological data and psychometric scales’ items: between-individual level. For each scales’ item, the mutual information (MI) with respect to each of the channels was measured and scaled to 0 to 1 dividing by the maximum MI value for that item. Values of “0” indicate no associations; values of 1 indicate the maximum recorded MI across all channels for an individual item. ACC_X: x-axis acceleration; ACC_Y: y-axis acceleration; ACC_Z: z-axis acceleration; BVP: blood volume pulse; EDA: electrodermal activity; HC: healthy controls; HDRS: Hamilton Depression Rating Scale; HR: heart rate; TEMP: temperature; YMRS: Young Mania Rating Scale.
Motor activity (ACC) channels were highly associated with manic symptoms (NMI>0.6), and stress-related channels (EDA and HR) with depressive symptoms (NMI from 0.4 to 1.0), as shown in
“Increased motor activity” (YMRS item 2 [YMRS2]) was associated with ACC (NMI>0.55), “aggressive behavior” (YMRS9) with EDA (NMI=1.0), “insomnia” (HDRS4-6) with ACC (NMI∼0.6), “motor inhibition” (HDRS8) with ACC (NMI∼0.75), and “psychic anxiety” (HDRS10) with EDA (NMI=0.52), as shown in
Although other studies have used raw physiological data to predict mental health status, this is the first study to present a novel fully automated method for the analysis of raw physiological data from a research-grade wearable device, including a rules-based filter for invalid physiological data, whereas all other studies presented methods that required manual interventions at some point in the pipeline [
Note that both (1) intra- and (2) inter-individual analyses approach different research questions: the (1) intra-individual analytical approach looks at the course of an index episode within a single patient and examines whether different states (from the acute phase to response and remission) can be distinguished from each other; on the other hand, the (2) inter-individual analytical approach takes a cross-sectional view and studies the degree to which different mood disorder states (comprising the full spectrum from depression to mixed state, mania, and euthymia) can be separated. Both analyses try to identify digital biomarkers of illness activity using physiological data collected with a wristband. However, intra-individual analyses look for a fine-grained quantification of illness activity that may allow the identification of low-severity mood states (or prodromal phases) in comparison with moderate to severe ones. Conversely, inter-individual analyses could potentially distinguish between mood phases (mania vs depression) or cases from HCs but may not be suitable for assessing the severity of mood episodes, as represented in
Severity versus Mood-Phase Classification Models: visual grounds for both intra- and inter-individual analyses. On the left, a severity classification model for a patient with depression (acute-response-remission phases). On the right, a mood-phase classification model (depression, mania, and euthymia). Note that on the left model, the same individual is compared at 3 different states (corresponding to a reduction in depressive psychopathology). Thus, individual-level characteristics (age, sex, and gait) should go through little to no variation across; should remain the same on the 3 longitudinal registers; and therefore, the shift in the covariate distribution should be relatively contained and not influence the classification of the model (capturing mood-relevant signals). In contrast, on the right, 3 different individuals at 3 different mood states are compared. In this case, the model would potentially distinguish between mood phases (mania vs depression), or cases from healthy controls, but may not be able to distinguish longitudinal changes in disease severity over the course of an index episode. In addition, in the latter model, subject-specific characteristics may be overlapped with mood-relevant signals, thus acting as confounders for the model. T0: current acute Diagnostic and Statistical Manual of Mental Disorders–5 affective episodes; T1: symptoms’ response; T2: symptomatic remission.
Studies comparing intra- and inter-individual models show that although intra-individual (cross-subject or patient-specific) models are trained on the data of a single subject, they perform better than intersubject (within-subject or generalized) models [
Notably, the out-of-sample generalizations of both models differ vastly. Whereas the intra-individual model requires multiple seizures recorded per subject and will produce individualized models tailored to a single patient, the inter-individual model requires seizures recorded from multiple participants and will provide intersubject models to be used over wider populations. For this purpose, intersubject variability plays a key role: focal seizures have a multitude of possible clinical manifestations that can occur in sequence or in parallel and can be repeated or not occur at all, in a single seizure. For instance, preictal tachycardia appears to be a phenomenon that is not generalizable to patient cohorts. Furthermore, although there may oftentimes be little change in the semiology of seizures for a single patient, they can be very heterogeneous across populations. Intra-individual models optimized for each patient can robustly detect seizures in some patients with epilepsy, but they may fail, especially when the seizures have differing semiologies that are not represented in the training data for the model. Intersubject models perform worse than if trained in an individualized manner, at least in terms of either sensitivity or false-alarm rates [
Clinically, the end goal is to have a model inferring mood states at the individual level, regardless of whether such a model is shared across subjects or if each subject has a tailored model. Although most digital biomarker research has focused on diagnosis classification, few studies have aimed to detect longitudinal symptom change. Developing methods to detect changes in mood symptoms has the potential to prompt just-in-time interventions to prevent full-blown affective relapses and clinical deterioration and evaluate the response to pharmacological treatments with objective measures [
In our sample, both intra- and inter-individual models for respectively assessing differences in severity of acute affective episodes over time (
Motor activity (from ACC) was the most relevant digital signal for predicting the severity of mania and mixed mania (but not for unipolar or bipolar depression) and also for predicting the polarity of acute affective episodes between individuals (
In contrast, “stress-related” digital signals (EDA and HR) were the most relevant for predicting the severity of both unipolar and bipolar depression (but not mania or mixed mania) and were also prominent for predicting the polarity of acute affective episodes between individuals (
Generalizations of the former models on unseen patients were of overall low accuracy, which may be due to high psychopathological and individual heterogeneity, as well as external factors. Although mood episodes share many psychopathological aspects, they can present with multiple combinations of symptoms [
When exploring the association between affective symptoms and physiological data, skin TEMP showed the highest association with psychometric scales (NMI approximately 1.0;
Regarding the most relevant inputs for the previous models, physiological data related to specific symptom dimensions (eg, ACC with motor activity and EDA and HR variation with stress response or anxiety) seemed to be more relevant signals for predicting mood episode severity and polarity rather than more raw data, such as BVP with nearly 0% permutation importance in all models (
We acknowledge several limitations in this study. First, the limited sample size for model development does not allow us to make strong claims about generalization performance [
Future works will further explore the capabilities of advanced automated machine learning models for identifying affective illness activity and the role of confounders in this association. Of particular interest are the application of clustering algorithms [
Physiological wearable data may have the potential to identify and predict the severity of mania and depression in mood disorders as well as specific symptoms quantitatively. Motor activity appears to be the most relevant digital biomarker for predicting mania, whereas stress-related digital biomarkers (EDA and HR) appear to be the most relevant for predicting both bipolar and unipolar depression. In the context of biomarkers in mood disorders, these findings represent a promising pathway toward personalized psychiatry, in which clinical decisions and treatments could be supported by passive continuous and objective digital data.
Empatica E4.
Validation set performance (accuracy) as a function of time alignment (Hz) and window length (w).
Confusion matrix for the prediction of the severity of an acute affective episode: models and generalization. BD: bipolar disorder; MDD: major depressive disorder.
Confusion matrix for the prediction of the polarity of affective episodes, euthymia, and healthy controls: models and generalization. BD: bipolar disorder; HC: healthy controls; MDD: major depressive disorder; T0: current acute Diagnostic and Statistical Manual of Mental Disorders–5 affective episodes.
acceleration
area under the receiver operating characteristic
bipolar disorder
Bidirectional Long Short-Term Memory
blood volume pulse
Diagnostic and Statistical Manual of Mental Disorders–5
electrodermal activity
healthy control
Hamilton Depression Rating Scale
heart rate
heart rate variability
interbeat interval
long short-term memory
major depressive disorder
normalized mutual information
recurrent neural network
receiver operating characteristic
current acute Diagnostic and Statistical Manual of Mental Disorders–5 affective episodes
symptoms' response
symptomatic remission
temperature
Young Mania Rating Scale
The authors acknowledge the contribution of all the participants of the study.
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 cofinanced by Fondo Social Europeo Plus (FSE+). FC and BML are supported by the United Kingdom Research and Innovation (grant EP/S02431X/1), UK Research and Innovation (UKRI) Centre for Doctoral Training in Biomedical AI at the University of Edinburgh, School of Informatics. A Mas is supported by an Agència de Gestió d’Ajudes Universitàries i de Investigació (AGAUR)—PANDÈMIES 2020 grant (PI047003) from the Generalitat de Catalunya. MS is supported by a grant from the Baszucki Brain Research Fund. IG thanks the support of the Spanish Ministry of Science and Innovation (PI19/00954) integrated into the Plan Nacional de I+D+I and cofinanced by the ISCIII-Subdirección General de Evaluación y el Fondos Europeos de la Unión Europea (FEDER, FSE, Next Generation EU/Plan de Recuperación Transformación y Resiliencia_PRTR); the ISCIII; the CIBER of Mental Health (CIBERSAM); and the Secretaria d’Universitats i Recerca del Departament d’Economia i Coneixement (2017 SGR 1365), Centres de Recerca de Catalunya (CERCA) Programme or Generalitat de Catalunya as well as the Fundació Clínic per la Recerca Biomèdica (Pons Bartran 2022-FRCB_PB1_2022). AG-P is supported by a Rio Hortega 2021 grant (CM21/00094) from the Spanish Ministry of Health financed by ISCIII and cofinanced by Fondo Social Europeo Plus (FSE+). MB thanks the Spanish Ministry of Health and ISCIII (PI20/01066). NV thanks the Biomedicine International Training Research Programme for Excellent Clinician-Scientists (BITRECS) project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 754550 and from “La Caixa” Foundation (ID 100010434), under the agreement LCF/PR/GN18/50310006. SM is supported by the grant “Contracte de Recerca Emili Letang-Josep Font” provided by Hospital Clínic de Barcelona. A Murru thanks the support of the Spanish Ministry of Science and Innovation (PI19/00672) integrated into the Plan Nacional de I+D+I and cofinanced by the ISCIII-Subdirección General de Evaluación and the Fondo Europeo de Desarrollo Regional (FEDER). SA has been supported by a Sara Borrell contract (CD20/00177), funded by ISCIII and cofunded by the European Social Fund “Investing in your future.” AM-A thanks the support of the Spanish Ministry of Science and Innovation (PI18/00789, PI21/00787) integrated into the Plan Nacional de I+D+I and cofinanced by ISCIII-Subdirección General de Evaluación and the FEDER; the ISCIII; the CIBERSAM; the Secretaria d’Universitats i Recerca del Departament d’Economia i Coneixement (2017 SGR 1365), the CERCA Programme, and the Departament de Salut de la Generalitat de Catalunya for the PERIS grant SLT006/17/00177. GF is supported by a fellowship from “La Caixa” Foundation (ID 100010434)—fellowship code—LCF/BQ/DR21/11880019. JR is supported by a Miguel Servet II contract (CPII19/00009), funded by ISCIII and cofunded by the European Social Fund “Investing in your future.” EV thanks the support of the Spanish Ministry of Science, Innovation and Universities (PI15/00283, PI18/00805, PI19/00394, CPII19/00009) integrated into the Plan Nacional de I+D+I and cofinanced by the ISCIII-Subdirección General de Evaluación and the FEDER; the ISCIII; the CIBERSAM; the Secretaria d’Universitats i Recerca del Departament d’Economia i Coneixement (2017 SGR 1365), and the CERCA Programme or Generalitat de Catalunya. The authors would like to thank the Departament de Salut de la Generalitat de Catalunya for the PERIS grant SLT006/17/00357. DH-M is supported by a Juan Rodés JR18/00021 granted by the ISCIII.
This project was funded by the ISCIII (FIS PI21/00340, TIMEBASE Study), cofunded by the Euopean Union, as well as a Baszucki Brain Research Fund grant (PI046998) from the Milken Foundation. The ISCIII or the Milken Foundation had no further role in study design; in the collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the paper for publication.
The data supporting the findings of this study are available upon request from the corresponding author.
GA and DH-M were responsible for study planning, project conception, and coordination. A Mas, MS, IP, MV, IG, A Benabarre, AG-P, MG, IA, A Bastidas, MC, TF-P, NA, MB, CG-R, NV, SM, SA, AM-A, and VR were responsible for recruitment. FC, BML, AV, MDP, VO, AS, and JR were responsible for data analysis. GA, FC, BML, and DH-M were responsible for manuscript preparation. All authors revised the final manuscript.
GA has received continuing medical education (CME)–related honoraria or consulting fees from Janssen-Cilag, Lundbeck, Lundbeck and Otsuka, and Angelini. IP has received CME-related honoraria, or consulting fees from ADAMED, Janssen-Cilag, and Lundbeck. IG has received grants and served as consultant, advisor or CME speaker for the following identities: Angelini, Casen Recordati, Ferrer, Janssen Cilag, and Lundbeck, Lundbeck-Otsuka, Luye, SEI Healthcare. AG-P has received CME-related honoraria, or consulting fees from Janssen-Cilag, Lundbeck, Casen Recordati and Angelini. MC has received grants and served as consultant, advisor or CME speaker for the following entities: Lundbeck, Esteve, Pfizer. NA has received CME-related financing from Janssen-Cilag, Lundbeck, Adamed, Pfizer, Angelini and Boston Scientific. MB has been a consultant for, received grant/research support and honoraria from, and been on the speakers/advisory board of has received honoraria from talks and/or consultancy of Adamed, Angelini, Casen-Recordati, Exeltis, Ferrer, Janssen, Lundbeck, Neuraxpharm, Otsuka, Pfizer and Sanofi. NV has received financial support for CME activities and travel funds from the following entities: Angelini, Janssen-Cilag, Lundbeck, Otsuka. SM has received CME-related honoraria, or consulting fees from Janssen-Cilag, Lundbeck, Lundbeck/Otsuka, and Angelini. A Murru has received grants and served as consultant, advisor or CME speaker for the following entities: Angelini, Idorsia, Lundbeck, Pfizer, Takeda. LS has received CME-related honoraria, or consulting fees from Boehringer -Ingelheim, Janssen, Lundbeck/Otsuka, Sanofi-Aventis. AHY has received honoraria for lectures and advisory boards for all major pharmaceutical companies with drugs used in affective and related disorders. EV has received research support from or served as consultant, adviser or speaker for AB-Biotics, Abbott, Abbvie, Adamed, Angelini, Biogen, Celon, Dainippon Sumitomo Pharma, Ferrer, Gedeon Richter, GH Research, Glaxo SmithKline, Janssen, Lundbeck, Organon, Otsuka, Rovi, Sage pharmaceuticals, Sanofi-Aventis, Shire, Sunovion, Takeda, and Viatris. DH-M has received CME-related honoraria and served as consultant for Abbott, Angelini, Ethypharm Digital Therapy and Janssen-Cilag. All authors report no financial or other relationship relevant to the subject of this article.