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Sensors (Basel). 2017 Jul 21;17(7). pii: E1685. doi: 10.3390/s17071685.

Noncontact Sleep Study by Multi-Modal Sensor Fusion.

Author information

1
Department of Electronics and Computer Engineering, Hanyang University, Seoul 04763, Korea. kuyoungninezero@hanyang.ac.kr.
2
Department of Electronics and Computer Engineering, Hanyang University, Seoul 04763, Korea. sentel103@naver.com.
3
Intelligence Lab, LG Electronics Woomyon Research and Development Campus, Seoul 06763, Korea. ksoo.shin@lge.com.
4
Intelligence Lab, LG Electronics Woomyon Research and Development Campus, Seoul 06763, Korea. jinho.sohn@lge.com.
5
Department of Otorhinolaryngology-Head and Neck Surgery, College of Medicine, Hanyang University, Seoul 04763, Korea. shcho@hanyang.ac.kr.
6
Department of Electronics and Computer Engineering, Hanyang University, Seoul 04763, Korea. jchang@hanyang.ac.kr.

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.

KEYWORDS:

medical device; microphone; radar; sensor fusion; sleep stage; vital signal

PMID:
28753994
PMCID:
PMC5539697
DOI:
10.3390/s17071685
[Indexed for MEDLINE]
Free PMC Article

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