Wearable Multi-Biosignal Analysis Integrated Interface With Direct Sleep-Stage Classification
| Autor: | Sung-Woo Kim, Don-Han Kim, Tae Hoon Lee, Junyeong Yeom, Jae Joon Kim, Kwangmuk Lee |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
General Computer Science
Computer science Interface (computing) Wearable computer wearable device 02 engineering and technology Polysomnography Electromyography Electroencephalography Sleep-stage classification 03 medical and health sciences readout integrated circuit 0302 clinical medicine 0202 electrical engineering electronic engineering information engineering medicine General Materials Science Sleep Stages medicine.diagnostic_test business.industry feature extraction stage 020208 electrical & electronic engineering General Engineering Wireless network interface controller lcsh:Electrical engineering. Electronics. Nuclear engineering business OpenBCI rule-based decision tree multi-biosignal interface lcsh:TK1-9971 030217 neurology & neurosurgery Computer hardware |
| Zdroj: | IEEE Access, Vol 8, Pp 46131-46140 (2020) |
| ISSN: | 2169-3536 |
| Popis: | This paper presents a wearable multi-biosignal wireless interface for sleep analysis. It enables comfortable sleep monitoring with direct sleep-stage classification capability while conventional analytic interfaces including the Polysomnography (PSG) require complex post-processing analyses based on heavy raw data, need expert supervision for measurements, or do not provide comfortable fit for long-time wearing. The proposed multi-biosignal interface consists of electroencephalography (EEG), electromyography (EMG), and electrooculography (EOG). A readout integrated circuit (ROIC) is designed to collect three kinds of bio-potential signals through four internal readout channels, where their analog feature extraction circuits are included together to provide sleep-stage classification directly. The designed multi-biosignal sensing ROIC is fabricated in a 180-nm complementary metal-oxide-semiconductor (CMOS) process. For system-level verification, its low-power headband-style analytic device is implemented for wearable sleep monitoring, where the direct sleep-stage classification is performed based on a decision tree algorithm. It is functionally verified by comparison experiments with post-processing analysis results from the OpenBCI module, whose sleep-stage detection shows reasonable correlation of 74% for four sleep stages. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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