Predicting depressed patients with suicidal ideation from ECG recordings

Screening major depressive disorder in patients with obstructive sleep apnea using single-lead ECG recording during sleep

Objective: A large number of people with obstructive sleep apnea (OSA) also suffer from major depressive disorder (MDD), leading to underdiagnosis due to overlapping symptoms. Polysomnography has been considered to identify MDD. However, limited access to sleep clinics makes this challenging. In this study, we propose a model to detect MDD in people with OSA using an electrocardiogram (ECG) during sleep. Methods: The single-lead ECG data of 32 people with OSA (OSAD-) and 23 with OSA and MDD (OSAD+) were investigated. The first 60 min of their recordings after sleep were segmented into 30-s segments and 13 parameters were extracted: PR, QT, ST, QRS, PP, and RR; mean heart rate; two time-domain HRV parameters: SDNN, RMSSD; and four frequency heart rate variability parameters: LF_power, HF_power, total power, and the ratio of LF_power/HF_power. The mean and standard deviation of these parameters were the input to a support vector machine which was trained to separate OSAD- and OSAD+. Results: The proposed model distinguished between OSAD+ and OSAD- groups with an accuracy of 78.18%, a sensitivity of 73.91%, a specificity of 81.25%, and a precision of 73.91%. Conclusion: This study shows the potential of using only ECG for detecting depression in OSA patients.

MDDBranchNet: A Deep Learning Model for Detecting Major Depressive Disorder Using ECG Signal

Major depressive disorder (MDD) is a chronic mental illness which affects people's well-being and is often detected at a later stage of depression with a likelihood of suicidal ideation. Early detection of MDD is thus necessary to reduce the impact, however, it requires monitoring vitals in daily living conditions. EEG is generally multi-channel and due to difficulty in signal acquisition, it is unsuitable for home-based monitoring, whereas, wearable sensors can collect single-channel ECG. Classical machine-learning based MDD detection studies commonly use various heart rate variability features. Feature generation, which requires domain knowledge, is often challenging, and requires computation power, often unsuitable for real time processing, MDDBranchNet is a proposed parallel-branch deep learning model for MDD binary classification from a single channel ECG which uses additional ECG-derived signals such as R-R signal and degree distribution time series of horizontal visibility graph. The use of derived branches was able to increase the model's accuracy by around 7%. An optimal 20-second overlapped segmentation of ECG recording was found to be beneficial with a 70% prediction threshold for maximum MDD detection with a minimum false positive rate. The proposed model evaluated MDD prediction from signal excerpts, irrespective of location (first, middle or last one-third of the recording), instead of considering the entire ECG signal with minimal performance variation stressing the idea that MDD phenomena are likely to manifest uniformly throughout the recording.

From Social Stress and Isolation to Autonomic Nervous System Dysregulation in Suicidal Behavior

PURPOSE OF REVIEW

In this narrative review we wanted to describe the relationship of autonomic nervous system activity with social environment and suicidal spectrum behaviors.

RECENT FINDINGS

Patients with suicidal ideation/suicide attempt have higher sympathetic nervous system (SNS) and lower parasympathetic nervous system (PNS) activity in resting conditions and during acute stress tasks compared with patients without suicidal ideation/suicide attempt. Death by suicide and violent suicide attempt also are related to SNS hyperactivation. Similarly, a SNS/PNS imbalance has been observed in people with childhood trauma, stressful life events or feelings of loneliness and isolation. Social support seems to increase PNS control and resilience. Due to the importance of the social context and stressful life events in suicidal behavior, SNS/PNS imbalance could act as a mediator in this relationship and be a source of relevant biomarkers. Childhood trauma and stressful life events may impair the autonomic nervous system response in suicidal patients. Loneliness, isolation and social support may act as moderators in acute stress situations.

Heart rate variability status at rest in adult depressed patients: a systematic review and meta-analysis

Purposes

A meta-analysis was conducted to examine the differences in heart rate variability (HRV) between depressed patients and healthy individuals, with the purpose of providing a theoretical basis for the diagnosis of depression and the prevention of cardiovascular diseases.

Methods

To search China National Knowledge Infrastructure (CNKI), WanFang, VIP, PubMed, Web of Science, Science Direct, and Cochrane Library databases to collect case-control studies on HRV in depressed patients, the retrieval date is from the establishment of the database to December 2022. Effective Public Health Practice Project (EPHPP) scale was used to evaluate literature quality, and Stata14.0 software was used for meta-analysis.

Results

This study comprised of 43 papers, 22 written in Chinese and 21 in English, that included 2,359 subjects in the depression group and 3,547 in the healthy control group. Meta-analysis results showed that compared with the healthy control group, patients with depression had lower SDNN [Hedges' g = -0.87, 95% CI (-1.14, -0.60), Z = -6.254, p < 0.01], RMSSD [Hedges' g = -0.51, 95% CI (-0.69,-0.33), Z = -5.525, p < 0.01], PNN50 [Hedges' g = -0.43, 95% CI (-0.59, -0.27), Z = -5.245, p < 0.01], LF [Hedges' g = -0.34, 95% CI (-0.55, - 0.13), Z = -3.104, p < 0.01], and HF [Hedges' g = -0.51, 95% CI (-0.69, -0.33), Z = -5.669 p < 0.01], and LF/HF [Hedges' g = -0.05, 95% CI (-0.27, 0.18), Z = -0.410, p = 0.682] showed no significant difference.

Conclusion

This research revealed that HRV measures of depressed individuals were lower than those of the healthy population, except for LF/HF, suggesting that people with depression may be more at risk of cardiovascular diseases than the healthy population.

Identification of major depression patients using machine learning models based on heart rate variability during sleep stages for pre-hospital screening

With the COVID-19 pandemic causing challenges in hospital admissions globally, the role of home health monitoring in aiding the diagnosis of mental health disorders has become increasingly important. This paper proposes an interpretable machine learning solution to optimise initial screening for major depressive disorder (MDD) in both male and female patients. The data is from the Stanford Technical Analysis and Sleep Genome Study (STAGES). We analyzed 5-min short-term electrocardiogram (ECG) signals during nighttime sleep stages of 40 MDD patients and 40 healthy controls, with a 1:1 gender ratio. After preprocessing, we calculated the time-frequency parameters of heart rate variability (HRV) based on the ECG signals and used common machine learning algorithms for classification, along with feature importance analysis for global decision analysis. Ultimately, the Bayesian optimised extremely randomized trees classifier (BO-ERTC) showed the best performance on this dataset (accuracy 86.32%, specificity 86.49%, sensitivity 85.85%, F1-score 0.86). By using feature importance analysis on the cases confirmed by BO-ERTC, we found that gender is one of the most important factors affecting the prediction of the model, which should not be overlooked in our assisted diagnosis. This method can be embedded in portable ECG monitoring systems and is consistent with the literature results.

When Heart Beats Differently in Depression: Review of Nonlinear Heart Rate Variability Measures

BACKGROUND

Disturbed heart dynamics in depression seriously increases mortality risk. Heart rate variability (HRV) is a rich source of information for studying this dynamics. This paper is a meta-analytic review with methodological commentary of the application of nonlinear analysis of HRV and its possibility to address cardiovascular diseases in depression.

OBJECTIVE

This paper aimed to appeal for the introduction of cardiological screening to patients with depression, because it is still far from established practice. The other (main) objective of the paper was to show that nonlinear methods in HRV analysis give better results than standard ones.

METHODS

We systematically searched on the web for papers on nonlinear analyses of HRV in depression, in line with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 framework recommendations. We scrutinized the chosen publications and performed random-effects meta-analysis, using the esci module in jamovi software where standardized effect sizes (ESs) are corrected to yield the proof of the practical utility of their results.

RESULTS

In all, 26 publications on the connection of nonlinear HRV measures and depression meeting our inclusion criteria were selected, examining a total of 1537 patients diagnosed with depression and 1041 healthy controls (N=2578). The overall ES (unbiased) was 1.03 (95% CI 0.703-1.35; diamond ratio 3.60). We performed 3 more meta-analytic comparisons, demonstrating the overall effectiveness of 3 groups of nonlinear analysis: detrended fluctuation analysis (overall ES 0.364, 95% CI 0.237-0.491), entropy-based measures (overall ES 1.05, 95% CI 0.572-1.52), and all other nonlinear measures (overall ES 0.702, 95% CI 0.422-0.982). The effectiveness of the applied methods of electrocardiogram analysis was compared and discussed in the light of detection and prevention of depression-related cardiovascular risk.

CONCLUSIONS

We compared the ESs of nonlinear and conventional time and spectral methods (found in the literature) and demonstrated that those of the former are larger, which recommends their use for the early screening of cardiovascular abnormalities in patients with depression to prevent possible deleterious events.

Automated recognition of major depressive disorder from cardiovascular and respiratory physiological signals

Major Depressive Disorder (MDD) is a neurohormonal disorder that causes persistent negative thoughts, mood and feelings, often accompanied with suicidal ideation (SI). Current clinical diagnostic approaches are solely based on psychiatric interview questionnaires. Thus, a computational intelligence tool for the automated detection of MDD with and without suicidal ideation is presented in this study. Since MDD is proven to affect cardiovascular and respiratory systems, the aim of the study is to automatically identify the disorder severity in MDD patients using corresponding multi-modal physiological signals, including electrocardiogram (ECG), finger photoplethysmography (PPG) and respiratory signals (RSP). Data from 88 subjects were used in this study, out of which 25 were MDD patients without SI (MDDSI-), 18 MDD patients with SI (MDDSI+), and 45 normal subjects. Multi-modal physiological signals were acquired from each subject, including ECG, RSP, and PPG signals, and then pre-processed. Discrete wavelet transform (DWT) was applied to the signals, which were decomposed up to six levels, and then eleven nonlinear features were extracted. The features were ranked according to the analysis of variance test and Marginal Fisher Analysis was employed to reduce the feature set, after which the reduced features were ranked again to select the most discriminatory features. Support vector machine with polynomial radial basis function (SVM-RBF) as well as k-nearest neighbor (KNN) classifiers were used to classify the significant features. The performance of the classifiers was evaluated in a 10-fold cross validation scheme. The best performance achieved for the classification of MDDSI+ patients was up to 85.2%, by using selected features from the obtained multi-modal signals with SVM-RBF, while it was up to 96.6% for the detection of MDD patients against healthy subjects. This work is a step toward the utilization of automated tools in diagnostics and monitoring of MDD patients in a personalized and wearable healthcare system.

Utilizing digital predictive biomarkers to identify Veteran suicide risk

Veteran suicide is one of the most complex and pressing health issues in the United States. According to the 2020 National Veteran Suicide Prevention Annual Report, since 2018 an average of 17.2 Veterans died by suicide each day. Veteran suicide risk screening is currently limited to suicide hotlines, patient reporting, patient visits, and family or friend reporting. As a result of these limitations, innovative approaches in suicide screening are increasingly garnering attention. An essential feature of these innovative methods includes better incorporation of risk factors that might indicate higher risk for tracking suicidal ideation based on personal behavior. Digital technologies create a means through which measuring these risk factors more reliably, with higher fidelity, and more frequently throughout daily life is possible, with the capacity to identify potentially telling behavior patterns. In this review, digital predictive biomarkers are discussed as they pertain to suicide risk, such as sleep vital signs, sleep disturbance, sleep quality, and speech pattern recognition. Various digital predictive biomarkers are reviewed and evaluated as well as their potential utility in predicting and diagnosing Veteran suicidal ideation in real time. In the future, these digital biomarkers could be combined to generate further suicide screening for diagnosis and severity assessments, allowing healthcare providers and healthcare teams to intervene more optimally.

The Proposition for Bipolar Depression Forecasting Based on Wearable Data Collection

Bipolar depression is treated wrongly as unipolar depression, on average, for 8 years. It is shown that this mismedication affects the occurrence of a manic episode and aggravates the overall condition of patients with bipolar depression. Significant effort was invested in early detection of depression and forecasting of responses to certain therapeutic approaches using a combination of features extracted from standard and online testing, wearables monitoring, and machine learning. In the case of unipolar depression, this approach yielded evidence that this data-based computational psychiatry approach would be helpful in clinical practice. Following a similar pipeline, we examined the usefulness of this approach to foresee a manic episode in bipolar depression, so that clinicians and family of the patient can help patient navigate through the time of crisis. Our projects combined the results from self-reported daily questionnaires, the data obtained from smart watches, and the data from regular reports from standard psychiatric interviews to feed various machine learning models to predict a crisis in bipolar depression. Contrary to satisfactory predictions in unipolar depression, we found that bipolar depression, having more complex dynamics, requires personalized approach. A previous work on physiological complexity (complex variability) suggests that an inclusion of electrophysiological data, properly quantified, might lead to better solutions, as shown in other projects of our group concerning unipolar depression. Here, we make a comparison of previously performed research in a methodological sense, revisiting and additionally interpreting our own results showing that the methodological approach to mania forecasting may be modified to provide an accurate prediction in bipolar depression.

Development of Autonomic Nervous System Assays as Point-of-Care Tests to Supplement Clinical Judgment in Risk Assessment for Suicidal Behavior: A Review

PURPOSE OF REVIEW

A biomarker point-of-care (POC) test that supplements the psychiatric interview and improves detection of patients at risk for suicide would be of value, and assays of autonomic nervous system (ANS) activity would satisfy the logistical requirements for a POC test. We performed a selective review of the available literature of ANS assays related to risk for suicide.

RECENT FINDINGS

We searched PubMed and Web of Science with the strategy: "suicide OR suicidal" AND "electrodermal OR heart rate variability OR pupillometry OR pupillography." The search produced 119 items, 21 of which provided original data regarding ANS methods and suicide. These 21 studies included 6 for electrodermal activity, 14 for heart rate variability, and 1 for the pupillary light reflex. The 21 papers showed associations between ANS assays and suicide risk in a direction suggesting underlying hyperarousal in patients at risk for suicide. ANS assays show promise for future development as POC tests to supplement clinical decision making in estimating risk for suicide.

Reduction of depressive symptoms during inpatient treatment is not associated with changes in heart rate variability

Vagally mediated heart rate variability (HRV) is a psychophysiological indicator of mental and physical health. Limited research suggests there is reduced vagal activity and resulting lower HRV in patients with Major Depressive Disorder (MDD); however little is actually known about the association between HRV and symptoms of depression and whether the association mirrors symptom improvement following psychotherapy. The aim of this study was to investigate the association between antidepressant therapy, symptom change and HRV in 50 inpatients (68% females; 17–68 years) with a diagnosis of MDD. Severity of depressive symptoms was assessed by self-report (Beck Depression Inventory II) and the Hamilton Rating Scale of Depression. Measures of vagally mediated HRV (root mean square of successive differences and high-frequency) were assessed at multiple measurement points before and after inpatient psychotherapeutic and psychiatric treatment. Results showed an expected negative correlation between HRV and depressive symptoms at intake. Depressive symptoms improved (d = 0.84) without corresponding change in HRV, demonstrating a de-coupling between this psychophysiological indicator and symptom severity. To our knowledge, this study is the first to examine an association between HRV and depressive symptoms before and after psychotherapy. The observed de-coupling of depression and HRV, and its methodological implications for future research are discussed.

Objective measurement of sleep, heart rate, heart rate variability, and physical activity in suicidality: A systematic review

BACKGROUND

Emerging literature suggests that the arousal and regulatory systems as measured by sleep-wakefulness, heart rate (HR) and heart rate variability (HRV) may be powerful objective warning signs of suicidality. However, there is no systematic literature review examining the association between objective measurements of these variables with suicide and suicidal behavior.

METHODS

A web-based, systematic literature search using PubMed and EMBASE was conducted for articles that measured sleep-wakefulness and HR/HRV quantitatively in association with suicide. Search results were limited to human subjects and articles published in peer-reviewed journals in English. There were no restrictions for age, sex, settings and durations of measurements, types of mental illnesses, or comorbidity.

RESULTS

Twenty-three studies were included in the current systematic review. Across the studies, consistent patterns of disturbed sleep-wakefulness such as greater sleep onset latency and lower sleep efficiency were related to suicide. In addition, higher HR and lower variance of R-R intervals was an indicator of risk of suicide.

LIMITATIONS

Studies that used different equipment for sleep studies (i.e., polysomnography, electroencephalogram, actigraphy) were combined, and potential differences in their findings due to the different equipment were not considered.

CONCLUSIONS

Findings provide initial evidence for consistent patterns of sleep-wakefulness and HR/HRV possibly associated with suicidality; however, more studies are needed in order to identify the precise objective variables (e.g., sleep onset latency, high-frequency HRV), as well as time-varying patterns in these variables, that are related to acute suicide risk.

Feature of Heart Rate Variability and Metabolic Mechanism in Female College Students with Depression

Purpose

To explore the effects of depression on cardiac autonomic nerve function and related metabolic pathways, the heart rate variability (HRV) and urinary differential metabolites were detected on the college students with depression.

Methods

12 female freshmen with depression were filtered by the Beck Depression Inventory (BDI-II) and Self-rating Depression Scale (SDS). By wearing an HRV monitoring system, time domain indexes and frequency domain indexes were measured over 24 hours. Liquid chromatography–mass spectrometry (LC-MS) was used to detect their urinary differential metabolites. Differential metabolites were identified by principal component analysis (PCA) and orthogonal projections to latent structures discriminant analysis (OPLS-DA). The metabolic pathways related to these differential metabolites were analyzed by the MetPA database.

Results

Stress time was significantly increased, and recovery time was markedly decreased in the depression group compared with the control group (p < 0.001). Standard deviation of the normal-to-normal R interval (SDNN), root mean square of the beat-to-beat differences (RMSSD), high frequency (HF), and low frequency (LF) were decreased significantly (p < 0.001). Standard deviation of the normal-to-normal R interval (SDNN), root mean square of the beat-to-beat differences (RMSSD), high frequency (HF), and low frequency (LF) were decreased significantly (

Conclusion

Some autonomic nervous system disruption, high stress, and poor fatigue recovery were confirmed in college students with depression. The metabolic mechanism involved the disruption of coenzyme Q biosynthesis, glycine-serine-threonine metabolism, tyrosine metabolism, pyrimidine metabolism, and steroid metabolism under daily stress.

A meta-analysis of heart rate variability in major depression

BACKGROUND

Major depression (MD) is a risk factor for cardiovascular disease. Reduced heart rate variability (HRV) has been observed in MD. Given the predictive value of HRV for cardiovascular health, reduced HRV might be one physiological factor that mediates this association.

METHODS

The purpose of this study was to provide up-to-date random-effects meta-analyses of studies which compare resting-state measures of HRV between unmedicated adults with MD and controls. Database search considered English and German literature to July 2018.

RESULTS

A total of 21 studies including 2250 patients and 1982 controls were extracted. Significant differences between patients and controls were found for (i) frequency domains such as HF-HRV [Hedges' g = -0.318; 95% CI (-0.388 to -0.247)], LF-HRV (Hedges' g = -0.195; 95% CI (-0.332 to -0.059)], LF/HF-HRV (Hedges' g = 0.195; 95% CI (0.086-0.303)] and VLF-HRV (Hedges' g = -0.096; 95% CI (-0.179 to -0.013)), and for (ii) time-domains such as IBI (Hedges' g = -0.163; 95% CI (-0.304 to -0.022)], RMSSD (Hedges' g = -0.462; 95% CI (-0.612 to -0.312)] and SDNN (Hedges' g = -0.266; 95% CI (-0.431 to -0.100)].

CONCLUSIONS

Our findings demonstrate that all HRV-measures were lower in MD than in healthy controls and thus strengthens evidence for lower HRV as a potential cardiovascular risk factor in these patients.