Detection of major depressive disorder from linear and nonlinear heart rate variability features during mental task protocol

Byun, Sung Su; Kim, Ah Young; Jang, Eun Hye; Kim, Seunghwan; Choi, Kee‐Joon; Yu, Han Young; Jeon, Hong Jin

doi:10.1016/j.compbiomed.2019.103381

Cited by 71 publications

(47 citation statements)

References 86 publications

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“…In [ 16 ], the author has introduced a machine learning-based model and automated prediction of depression model using linear and nonlinear HRV measures and using a categorization and characteristic assortment method. A support vector machine-recursive feature elimination (SVM-RFE) and a statistical filter were utilized as classification algorithms.…”

Section: Related Workmentioning

confidence: 99%

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[Retracted] Predicting the Risk of Depression Based on ECG Using RNN

Noor

Asad

Khan

et al. 2021

Computational Intelligence and Neuroscience

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This paper presents a model to predict the risk of depression based on electrocardiogram (ECG). This proposed model uses a Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) autoencoder to predict normal, abnormal, and PVC heartbeats. The RNN model is a deep learning-based model to classify normal, abnormal, and PVC heartbeats. We used the model as a classifier. The model uses a heart rates dataset to predict abnormal and PVC heartbeats. As for the dataset, we have used 5000 ECG samples. The model was trained on a training dataset and validation dataset. After that, it was tested on a test dataset. The model is trained on normal heartbeat rates, so the model can predict any heartbeat rates other than normal. Our contribution here is to build a model that can differentiate between “normal,” “abnormal,” and “risky” heartbeats. Our model predicts “normal” heartbeats with 97.24% accuracy and can predict “PVC” heartbeats with 100% accuracy. Other than the accuracy, we evaluated our model on the training loss graphs. These two types of training loss graphs were evaluated as “normal” versus “risky” and “abnormal” versus “risky.” We have seen great results there as well. The best losses for “normal,” “abnormal,” and “risky” are 5.71, 33.36, and 34.78. However, these results may improve if a larger dataset is used. In studies, it was found that patients suffering from depression may have a different kind of heartbeat than the normal ones. In most cases, it is PVC (Premature Ventricular Contraction) heartbeats. Therefore, the target is to predict abnormal heartbeats and PVC heartbeats.

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Section: Related Workmentioning

confidence: 99%

“…ough the "PVC" class is quite similar to "R-on-T," "SP," and "UB"; [15] Cross validation 90-97% In [16] Machine learning model 74.4%…”

Section: Time-series Graph For Eachmentioning

confidence: 99%

[Retracted] Predicting the Risk of Depression Based on ECG Using RNN

Noor

Asad

Khan

et al. 2021

Computational Intelligence and Neuroscience

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“…This confirmed these indexes as accurate trait markers for depression and identified a particular time window that can be used. Attempts of classification based on short-term physiological recordings have been made before ( 2 , 36 ), achieving a moderate-to-good accuracy, but classifications based on long-term or night data are limited. Few studies attempted to classify patients based on longitudinal physiological data.…”

Section: Discussionmentioning

confidence: 99%

“…Significantly increased heart rate (HR) and low levels of heart rate variability (HRV) as surrogate markers for ANS functioning seem to support this position. The HR/HRV changes in individuals with major depressive disorder (MDD) have been described for both short recordings and psychological exposures, demonstrating robustness across several conditions ( 2 ). While these changes are usually tested in relatively short time intervals (i.e., minutes), pronounced alterations in patients with MDD have also been described for cardiac circadian rhythm variations.…”

Section: Introductionmentioning

confidence: 99%

Twenty-Four-Hour Heart Rate Is a Trait but Not State Marker for Depression in a Pilot Randomized Controlled Trial With a Single Infusion of Ketamine

et al. 2021

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Background: Abnormalities of heart rate (HR) and its variability are characteristic of major depressive disorder (MDD). However, circadian rhythm is rarely taken into account when statistically exploring state or trait markers for depression.Methods: A 4-day electrocardiogram was recorded for 16 treatment-resistant patients with MDD and 16 age- and sex-matched controls before, and for the patient group only, after a single treatment with the rapid-acting antidepressant ketamine or placebo (clinical trial registration available on https://www.clinicaltrialsregister.eu/ with EUDRACT number 2016-001715-21). Circadian rhythm differences of HR and the root mean square of successive differences (RMSSD) were compared between groups and were explored for classification purposes. Baseline HR/RMSSD were tested as predictors for treatment response, and physiological measures were assessed as state markers.Results: Patients showed higher HR and lower RMSSD alongside marked reductions in HR amplitude and RMSSD variation throughout the day. Excellent classification accuracy was achieved using HR during the night, particularly between 2 and 3 a.m. (90.6%). A positive association between baseline HR and treatment response (r = 0.55, p = 0.046) pointed toward better treatment outcome in patients with higher HR. Heart rate also decreased significantly following treatment but was not associated with improved mood after a single infusion of ketamine.Limitations: Our study had a limited sample size, and patients were treated with concomitant antidepressant medication.Conclusion: Patients with depression show a markedly reduced amplitude for HR and dysregulated RMSSD fluctuation. Higher HR and lower RMSSD in depression remain intact throughout a 24-h day, with the highest classification accuracy during the night. Baseline HR levels show potential for treatment response prediction but did not show potential as state markers in this study.Clinical trial registration: EUDRACT number 2016-001715-21.

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“…Logo, é lícita a reflexão que os estressores contemporâneos apresentem a capacidade de estimular reações neuroendócrinas, que, em última instância, podem contribuir para o surgimento de fenótipos ansiosos e/ou depressivos. Alterações emocionais podem, deste modo, provocar respostas autonômicas, e dentre elas está o estudo da modulação autonômica cardíaca, em que os dados oriundos dos batimentos cardíacos consecutivos podem ser analisados por meio de modelos lineares e/ou não lineares (Byun et al, 2019;Hoshi et al, 2019).…”

Section: Controle Cardiovascular Pelo Sistema Nervoso Autônomounclassified

Ansiedade, Depressão E Análise Não Linear Da Variabilidade Da Frequência Cardíaca Em Ingressantes No Ensino Superior

et al. 2020

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The non-linear analysis of the heart rate variability (HRV) is a potential correlation tool between physiological and emotional states. The aim of this study was correlate changes in non-linear parameters of the HRV with anxiety and depression indices in higher education entrants from Federal University of Goiás (UFG). To record HRV, the Polar Frequency Metermodel Polar® RS800cx (Electro Oi, Finland) was used. To analyze the levels of depression, the Beck Depression Inventory-I (BDI) was used, and for analysis of anxiety, the Inventories of State and Trace (IDATE T and E). Altogether, 50 (19.4±2.48 years) volunteers participated in the study. The scores of the depression and anxiety inventories indicated that the public had low and moderate indices of both conditions, respectively. Statistically significant correlations were found between the SD2 parameter (from the Poincaré plot in the HRV analysis), entropy data (ApEn and SampEn) and fractal property (DFA: α1) and trait and state anxiety levels. Although ApEn e SampEn still do not have well-defined physiological correspondences, DFA: α1 has been associated with parasympathetic modulation of heart rate. Therefore, it is concluded that chronic negative emotional states, such as anxiety and humor disorders, can be accompanied by parasympathetic increase in HRV, so the organism would be able to not wear out against long-term stressors. New studies are necessary to understand the physiological and evolutional bases of depression and anxiety.

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Detection of major depressive disorder from linear and nonlinear heart rate variability features during mental task protocol

Cited by 71 publications

References 86 publications

[Retracted] Predicting the Risk of Depression Based on ECG Using RNN

[Retracted] Predicting the Risk of Depression Based on ECG Using RNN

Twenty-Four-Hour Heart Rate Is a Trait but Not State Marker for Depression in a Pilot Randomized Controlled Trial With a Single Infusion of Ketamine

Ansiedade, Depressão E Análise Não Linear Da Variabilidade Da Frequência Cardíaca Em Ingressantes No Ensino Superior

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