Heart rate variability and cordance in rapid eye movement sleep as biomarkers of depression and treatment response

Pawlowski, Marcel; Gazea, Mary; Wollweber, Bastian; Dresler, Martin; Holsboer, Florian; Keck, Martin E.; Steiger, Axel; Adamczyk, Marek; Mikoteit, Thorsten

doi:10.1016/j.jpsychires.2017.03.026

Cited by 27 publications

(19 citation statements)

References 53 publications

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“…It showed a negative correlation with REM density in healthy subjects and in patients at week four. HRV derived from REM sleep appears to categorize healthy subjects and patients with depression [108].…”

Section: Heart Rate Variability Derived From Rem Sleep In Depressmentioning

confidence: 99%

Depression and Sleep

Steiger

Pawlowski

2019

IJMS

Self Cite

193

101

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Impaired sleep is both a risk factor and a symptom of depression. Objective sleep is assessed using the sleep electroencephalogram (EEG). Characteristic sleep-EEG changes in patients with depression include disinhibition of rapid eye movement (REM) sleep, changes of sleep continuity, and impaired non-REM sleep. Most antidepressants suppress REM sleep both in healthy volunteers and depressed patients. Various sleep-EEG variables may be suitable as biomarkers for diagnosis, prognosis, and prediction of therapy response in depression. In family studies of depression, enhanced REM density, a measure for frequency of rapid eye movements, is characteristic for an endophenotype. Cordance is an EEG measure distinctly correlated with regional brain perfusion. Prefrontal theta cordance, derived from REM sleep, appears to be a biomarker of antidepressant treatment response. Some predictive sleep-EEG markers of depression appear to be related to hypothalamo-pituitary-adrenocortical system activity.

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Section: Heart Rate Variability Derived From Rem Sleep In Depressmentioning

confidence: 99%

Depression and Sleep

Steiger

Pawlowski

2019

IJMS

Self Cite

193

101

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“…attempted to differentiate treatment response group from nonresponse group using EEG and HRV (53). However, the researchers did not predict treatment responsivity in depression using HRV parameters, while they could predict treatment outcome in MDD only using the EEG parameter (53). Despite focusing on PTSD diagnosis other than MDD, Minassian et al showed that high LF/HF ratio (>6.7) before deployment was significantly associated with post-deployment post-traumatic stress disorder (PTSD) in active-duty marines (54).…”

Section: Baseline Hrv Parameters For the Prediction Of Treatment Respmentioning

confidence: 99%

“…attempted to differentiate treatment response group from non-response group using EEG and HRV ( 53 ). However, the researchers did not predict treatment responsivity in depression using HRV parameters, while they could predict treatment outcome in MDD only using the EEG parameter ( 53 ). Despite focusing on PTSD diagnosis other than MDD, Minassian et al.…”

Section: Baseline Hrv Parameters For the Prediction Of Treatment Respmentioning

confidence: 99%

Heart Rate Variability for the Prediction of Treatment Response in Major Depressive Disorder

Choi

Jeon

2020

Front. Psychiatry

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Major depressive disorder (MDD) is one of the disabling diseases in the worldwide , and known to increase cardiac morbidity and mortality. Therefore, previous studies related heart rate variability (HRV) have been conducted to evaluate and diagnose MDD, and to predict treatment outcomes in patient with MDD. We reviewed extensively on the previous peer-reviewed publications associated with this issue, using Pub-Med. In this review article, we introduce the basic concept of HRV and HRV measures, and present several important findings associated with diagnosis and treatment prediction in MDD with using HRV parameters. Furthermore, we discuss the possible underlying mechanism of this phenomenon, and suggest several considerations for the future research.

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“…The basic function of the intelligent alarm clock is to monitor the ANR during the nap and awaken the user after good ANR has been detected. HR and HRV are effective markers of the autonomic nervous activity [15][16][17][18], and they can be derived from the ECG signal conveniently acquired through wearable ECG devices. The conventional indexes of HR and HRV are the mean of the RR intervals, standard deviation of the RR intervals, square root of the mean of the squares of differences between successive RR intervals (RMSSD), low frequency power (0.04 Hz-0.15 Hz), and high frequency power (0.15 Hz-0.4 Hz) [18].…”

Section: Introductionmentioning

confidence: 99%

“…HR and HRV are effective markers of the autonomic nervous activity [15][16][17][18], and they can be derived from the ECG signal conveniently acquired through wearable ECG devices. The conventional indexes of HR and HRV are the mean of the RR intervals, standard deviation of the RR intervals, square root of the mean of the squares of differences between successive RR intervals (RMSSD), low frequency power (0.04 Hz-0.15 Hz), and high frequency power (0.15 Hz-0.4 Hz) [18]. The intelligent alarm clock in practice will process the real-time ECG data, calculating the RR intervals and Pdf_Folio:1 activation [11], and we call this the autonomic nervous recovery inferring the ANR with as short delay as possible.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Biological Alarm Clock for Monitoring Autonomic Nervous Recovery During Nap

Xie¹,

Wang²,

Liu³

et al. 2019

IJCIS

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Intelligent biological alarm clock ECG Heart rate Heart rate variability Autonomic nervous system A B S T R A C TNap is an effective way to reduce daily-level fatigue after several hours of work. However, no alarm clock, which intelligently manages the nap duration with good autonomic nervous recovery (ANR) from fatigue, has been reported in literature. In this work, an intelligent biological alarm clock algorithm was designed on the basis of electrocardiogram (ECG) and electroencephalogram (EEG) data acquisition and analysis. ECG data samples were collected from 31 subjects in 278 times of nap experiments and categorized into good, moderate, and poor ANR datasets according to the degree of sympathetic withdrawal and parasympathetic activation during the nap. In practice, the alarm clock automatically classified the new-coming ECG data as good, moderate, or poor ANR with a classifier trained by the abovementioned ANR datasets. A prototype system of the intelligent alarm clock algorithm was implemented and validated in real-scene naps. The prototype system detected falling asleep during the closed-eye naps with a true positive rate of 93.55% and a true negative rate of 100%.

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Heart rate variability and cordance in rapid eye movement sleep as biomarkers of depression and treatment response

Cited by 27 publications

References 53 publications

Depression and Sleep

Depression and Sleep

Heart Rate Variability for the Prediction of Treatment Response in Major Depressive Disorder

Intelligent Biological Alarm Clock for Monitoring Autonomic Nervous Recovery During Nap

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