Motion Detection in Bed-Based Ballistocardiogram to Quantify Sleep Quality

Abstract

Daily assessment of sleep patterns with traditional sensor systems such as polysomnography (PSG) and electrocardiograms (ECG) is not feasible for certain populations such as severely disabled autistic children. Nocturnal movement/disturbance analysis is an important diagnostic tool for assessing sleep issues. The objective of this paper is to describe a motion detection algorithm to quantify restlessness during sleep via an unobtrusive contactless electromechanical film based ballistocardiogram (BCG) sensor integrated into a smart bed system. Two methods are proposed for motion artifact detection. One involves formulating Neyman-Pearson detection test based on signal variance in time domain and the second approach is based on a sequential detection algorithm. Test results demonstrate that Neyman- Pearson and sequential detection algorithms are very effective in identifying movements with 95% and 96% probability of detection and 94% and 95.2% accuracy in sleep and restlessness state identification, respectively.