Saad M, et al. - Researchers evaluated if discrimination between individuals with depression and healthy controls could be made via an algorithm using patterns of heart rate changes during sleep. 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) were made to undergo a heart rate profiling algorithm modeling using machine-learning based on 1203 polysomnograms. Findings, for the first time, demonstrated the possible validity of changes in heart rate across sleep-wake states as physiological markers for the identification of depression in a sample of people with sleep complaints. Individuals were classified using the heart rate profiling algorithm with an accuracy of 79.9%. Specifically, the algorithm led to the detection of 82.8% of the depression cases and ruling out of 77.0% of healthy controls.