Wearable Multi-Biosignal Analysis Integrated Interface With Direct Sleep-Stage Classification

Abstract

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. INDEX TERMS Sleep-stage classification, multi-biosignal interface, rule-based decision tree, feature extraction stage, readout integrated circuit, wearable device.

A Systematic Review of Sensing Technologies for Wearable Sleep Staging

“…Another integrated circuit using EEG and EMG channels in [101] reported significantly lower consumption at 5 µW. Finally, an integrated system using EEG, EOG, and EMG channels in [150] reported 71 mW as the power consumption of their system. Apart from the latter system, which has a relatively higher power consumption, all are capable of delivering multi-night recordings using small coin cell batteries with a typical capacity of 200-300 mAh.…”

Design and Implementation of a Multifunction Wearable Device to Monitor Sleep Physiological Signals

“…There are many methods used to collect data for sleep physiological signal monitoring, including electrooculography (EOG) and electrocardiogram (ECG) methods, which need to be deployed in complex medical environments [5,25,26,28,30]. Because these methods are relatively expensive and time consuming for patients, an increasing amount of research is focused on the possibility of using motion detectors, such as triple-axis accelerometers, to replace electrical sensing technology [3,25,30].…”

“…The addition of the ambient light data allowed us to accurately determine sleep periods from the motion detection data. Although we were able to measure HR, we are still in the process of determining how to incorporate the data into assessing sleep quality [5,6,22,27].…”

“…An example of a healthcare application is the development of a sleep monitoring system device based on the Internet of Medical Things (IoMT) [2][3][4]. Wearable technologies for health monitoring have been maturing and expanding recently [5]. The primary use for wearable technology in a medical environment is to aid in patient or subject monitoring, which can range from simply providing a way to track the health status of patients to the remote viewing of multiple patients' vitals [6].…”

“…The primary use for wearable technology in a medical environment is to aid in patient or subject monitoring, which can range from simply providing a way to track the health status of patients to the remote viewing of multiple patients' vitals [6]. Sleep status is often an influential factor related to a patient s health status and is important to monitor [5,7]. In general, sleep is a dynamic process that varies from day to day; hence, it is important to assess multiple nights of sleep for medical, research, and wellness reasons [7,8].…”