1. Using heart rate profiles during sleep as a biomarker of depression
- Author
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Stuart Fogel, Louis Soucy, Rébecca Robillard, Zul Merali, Mysa Saad, Amir Parvaresh, Joseph De Koninck, Charles M. Morin, Laura B. Ray, B Bujaki, Elliott Kyung Lee, Iryna Palamarchuk, Alan B. Douglass, and Célyne H. Bastien
- Subjects
Adult ,Male ,Sleep Wake Disorders ,medicine.medical_specialty ,Adolescent ,lcsh:RC435-571 ,Polysomnography ,Disease ,Major depressive disorder ,Machine Learning ,03 medical and health sciences ,Electrocardiography ,Young Adult ,0302 clinical medicine ,Heart Rate ,Internal medicine ,lcsh:Psychiatry ,Heart rate ,medicine ,Insomnia ,Heart rate variability ,Humans ,Autonomic nervous system ,030212 general & internal medicine ,Medical diagnosis ,Aged ,Retrospective Studies ,Aged, 80 and over ,business.industry ,Depression ,Medical record ,Electroencephalography ,Middle Aged ,medicine.disease ,3. Good health ,030227 psychiatry ,Psychiatry and Mental health ,Female ,medicine.symptom ,business ,Sleep ,Research Article - Abstract
Background Abnormalities in heart rate during sleep linked to impaired neuro-cardiac modulation may provide new information about physiological sleep signatures of depression. This study assessed the validity of an algorithm using patterns of heart rate changes during sleep to discriminate between individuals with depression and healthy controls. Methods A heart rate profiling algorithm was modeled using machine-learning based on 1203 polysomnograms from individuals with depression referred to a sleep clinic for the assessment of sleep abnormalities, including insomnia, excessive daytime fatigue, and sleep-related breathing disturbances (n = 664) and mentally healthy controls (n = 529). The final algorithm was tested on a distinct sample (n = 174) to categorize each individual as depressed or not depressed. The resulting categorizations were compared to medical record diagnoses. Results The algorithm had an overall classification accuracy of 79.9% [sensitivity: 82.8, 95% CI (0.73–0.89), specificity: 77.0, 95% CI (0.67–0.85)]. The algorithm remained highly sensitive across subgroups stratified by age, sex, depression severity, comorbid psychiatric illness, cardiovascular disease, and smoking status. Conclusions Sleep-derived heart rate patterns could act as an objective biomarker of depression, at least when it co-occurs with sleep disturbances, and may serve as a complimentary objective diagnostic tool. These findings highlight the extent to which some autonomic functions are impaired in individuals with depression, which warrants further investigation about potential underlying mechanisms. Electronic supplementary material The online version of this article (10.1186/s12888-019-2152-1) contains supplementary material, which is available to authorized users.
- Published
- 2019