Sleep-derived heart rate patterns could act as an objective biomarker of depression when it co-occurs with sleep disturbances, a new study found. Researchers assessed the validity of an algorithm using patterns of heart rate changes during sleep to discriminate between individuals with depression and healthy controls. A heart rate profiling algorithm was modeled using machine-learning based on 1,203 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. Among the findings:
- The algorithms had an overall classification accuracy of 79.9%
- The algorithm remained highly sensitive across subgroups stratified by age, sex, depression severity, comorbid psychiatric illness, cardiovascular disease, ad smoking status.
Saad M, et al. Using heart rate profiles during sleep as a biomarker of depression. [Published online ahead of print June 7, 2019]. BMC Psychiatry. doi: 10.1186/s12888-019-2152-1.