Smartphone-based and non-invasive sleep stage identification system with piezo-capacitive sensors

Abstract

A non-invasive, wireless, smartphone-based electronic measurement system for sleep stage identification is presented in this work. Ballistocardiograph signals are collected by two piezo-capacitive thin film strips located on the mattress base. Suitable analog conditioning circuits and digital pre-processing techniques are applied to obtain the heart and breathing rates (HR, BR), and an activity index (ACT) related to the body movements during the sleep. An initial calibration stage is proposed where analog signal amplification is fitted to each subject, from which activity index is derived. Features considered for machine learning classifications were the mentioned data and the time variabilities of HR and BR represented by the features R(k) and B(k), respectively. Support Vector Machine (SVM) and K-Nearest-Neighbour (KNN) classifiers are employed in both flat and hierarchical classifi- cation scenarios for Wake – Non Rapid Eye Movement – Rapid Eye Movement (WAKE/NREM/REM) sleep stage identification. Twelve healthy subjects were recorded with the developed system using a polysomnograph (PSG) as reference data. When compared with PSG, the presented system achieved an average accuracy of 69 % using only three features: R(k), B(k), and ACT, highlighting an 88.2 % recall for NREM stage identification. These findings suggest that accounting only for time variability features and activity, satisfactory results can be pro- vided as a complementary alternative for sleep stage identification, with a smartphone-based electronic system designed as an affordable, versatile, and simple tool for household applications.