Using heart rate profiles during sleep as a biomarker of depression

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.

Diurnal variation of heart rate variability as a physiological index of mood and emotion regulation processes in Major Depression and Borderline Personality Disorder

“…Conversely, some studies have reported that autonomic abnormalities in depression are more prominent during sleep than during awake [61][62][63], even relating this to an abnormal parasympathetic modulation [63]. However, those sleep-derived heart rate patterns were associated to sleep disturbances [64]. Therefore, the reason why we did not replicate this could be related to a different characteristic of the sample in terms of sleep disturbances and also to the reduced sample of the study.…”

The Different Facets of Heart Rate Variability in Obstructive Sleep Apnea

“…Reduced global HRV is consistently reported in depression and anxiety disorders. Specifically, depression is characterized by increased cardiac rhythmicity and reduced heart rate variability during both sleep and wakefulness (219). Moreover, changes in HRV parameters are associated with alternations in symptom severity of depression (220).…”

“…Moreover, changes in HRV parameters are associated with alternations in symptom severity of depression (220). Saad et al (219) showed that a sleep heart rate profiling algorithm detecting whether individuals with sleep complaints experience depression has an identification accuracy of 79.9%. Similarly, anxiety disorders displayed significantly lower HRV (221).…”

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

“…Gaetz et al ( 34 ) found a rather poor classification accuracy when using the diurnal physiological measures, but much improved accuracy (70.2%) when using nighttime data. Saad et al ( 17 ) were able to classify depressed patients with sleep abnormalities. The authors showed accurate distinction between patients and controls based on HR and EEG parameters (79.9%) ( 17 ).…”

“…Yet, in light of the established circadian rhythm differences in MDD, circadian rhythm variations may yield more insight and potentially more precise results as biomarkers. Indeed, Saad et al ( 17 ) have shown that night recordings in particular might be useful markers for depression ( 17 ). Recordings during sleep may constitute a more robust biomarker, given the absence of conscious neural modulation and external, potentially stressful stimuli or movement that could obscure HR measurements.…”