Noncontact Sleep Study by Multi-Modal Sensor Fusion.

Ku-Young Chung,Joon-Hyuk Chang,Kwangsub Song,Seok Cho,Kangsoo Shin,Jinho Sohn

doi:10.3390/s17071685

Abstract

Polysomnography (PSG) is considered as the gold standard for determining sleep stages, but due to the obtrusiveness of its sensor attachments, sleep stage classification algorithms using noninvasive sensors have been developed throughout the years. However, the previous studies have not yet been proven reliable. In addition, most of the products are designed for healthy customers rather than for patients with sleep disorder. We present a novel approach to classify sleep stages via low cost and noncontact multi-modal sensor fusion, which extracts sleep-related vital signals from radar signals and a sound-based context-awareness technique. This work is uniquely designed based on the PSG data of sleep disorder patients, which were received and certified by professionals at Hanyang University Hospital. The proposed algorithm further incorporates medical/statistical knowledge to determine personal-adjusted thresholds and devise post-processing. The efficiency of the proposed algorithm is highlighted by contrasting sleep stage classification performance between single sensor and sensor-fusion algorithms. To validate the possibility of commercializing this work, the classification results of this algorithm were compared with the commercialized sleep monitoring device, ResMed S+. The proposed algorithm was investigated with random patients following PSG examination, and results show a promising novel approach for determining sleep stages in a low cost and unobtrusive manner.

Highlights

High quality sleep is a profoundly important factor in an individual’s well-being because humans sleep nearly one third of their entire lives
Rahman et al [11], Eliran Dfana et al [15] and ResMed [16], we suggest that the fusion of noncontact microphone sensor and radar sensor data-based advanced signal processing algorithms would likely enhance the accuracy of sleep stage classification
We propose a novel approach for sleep stage classification of sleep disorder patients supported by a low cost, noncontact, multi-modal sensor fused signal processing technique

Summary

Introduction

High quality sleep is a profoundly important factor in an individual’s well-being because humans sleep nearly one third of their entire lives. Most people live with a lack of sleep and tend to ignore the importance of sleep quality and sleep patterns, which can eventually lead to sleep disorders. Throughout the years, a number of devices has been launched to provide users with knowledge of their sleep hygiene [1,2]. PSG is a long procedure that requires the use of various sensors and electrodes throughout the night [3,4]. Knowledge of sleep hygiene requires knowledge of sleep stages: awake, non-rapid eye movement (NREM) 1, NREM 2, NREM 3 and rapid eye movement (REM)

Results

Discussion

Conclusion