Use of Machine Learning Algorithms to Identify Sleep Phases Starting from ECG Signals

The absence of the rapid eye movement (REM) phase during sleep can have negative consequences, as, e.g., anxiety, increase in appetite, irritability, while, on the other hand, it can help in improving some kinds of depression. The goal of the research described in this chapter consists in the identification of the different sleep phases a subject is experiencing by using heart rate variability (HRV) values. These are computed starting from the signals gathered from electrocardiogram (ECG) sensors placed on the subject. To this aim, the publicly available Sleep Heart Health Study (SHHS) data set is taken into account, which contains both types of information. Several machine learning classification algorithms are tested on this data set, and their performance is compared in terms of F1-score value, as SHHS is highly unbalanced. Once the most suitable classification algorithm is found, it can be firstly trained offline on the problem and then used online in an IoT-based fully automated e-health system. In this latter, sensors gather, in real time, ECG signals from a sleeping subject, send them to a device where data is processed, HRV values are computed, sleep phase identification takes place, and medical personnel, close or not to the subject, are immediately informed of the subject's sleeping phases.