Wearable low-latency sleep stage classifier

Chemparathy, Aditi; Kassiri, Hossein; Salam, Muhammad Tariqus; Boyce, Richard D.; Bekmambetova, Fadime; Adamantidis, Antoine; Genov, Roman

doi:10.1109/biocas.2014.6981795

Cited by 15 publications

(2 citation statements)

References 10 publications

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“…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]. Recent research has revealed that motion detection can be used to monitor sleep status [9].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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We present a wearable device built on an Adafruit Circuit Playground Express (CPE) board and integrated with a photoplethysmographic (PPG) optical sensor for heart rate monitoring and multiple embedded sensors for medical applications—in particular, sleep physiological signal monitoring. Our device is portable and lightweight. Due to the microcontroller unit (MCU)-based architecture of the proposed device, it is scalable and flexible. Thus, with the addition of different plug-and-play sensors, it can be used in many applications in different fields. The innovation introduced in this study is that with additional sensors, we can determine whether there are intermediary variables that can be modified to improve our sleep monitoring algorithm. Additionally, although the proposed device has a relatively low cost, it achieves substantially improved performance compared to the commercially available Philips ActiWatch2 wearable device, which has been approved by the Food and Drug Administration (FDA). To assess the reliability of our device, we compared physiological sleep signals recorded simultaneously from volunteers using both our device and ActiWatch2. Motion and light detection data from our device were shown to be correlated to data simultaneously collected using the ActiWatch2, with correlation coefficients of 0.78 and 0.89, respectively. For 7 days of continuous data collection, there was only one instance of a false positive, in which our device detected a sleep interval, while the ActiWatch2 did not. The most important aspect of our research is the use of an open architecture. At the hardware level, general purpose input/output (GPIO), serial peripheral interface (SPI), integrated circuit (I2C), and universal asynchronous receiver-transmitter (UART) standards were used. At the software level, an object-oriented programming methodology was used to develop the system. Because the use of plug-and-play sensors is associated with the risk of adverse outcomes, such as system instability, this study heavily relied on object-oriented programming. Object-oriented programming improves system stability when hardware components are replaced or upgraded, allowing us to change the original system components at a low cost. Therefore, our device is easily scalable and has low commercialization costs. The proposed wearable device can facilitate the long-term tracking of physiological signals in sleep monitoring and related research. The open architecture of our device facilitates collaboration and allows other researchers to adapt our device for use in their own research, which is the main characteristic and contribution of this study.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…Electromyography (EMG), which is a tool to record and evaluate the electrical activity produced by our skeletal muscles, is a potentially useful technique for this application. EMG has been extensively used in polysomnography (PSG) studies to evaluate the quality of sleep [4,5] and can provide information about the level of stress by recording from facial expressions during sleep to detect certain patterns in specific movements such as clenching. In addition to sleep studies, personalized EMG sensors capable of real-time decision making can be used to take measurements during daily activities in order to warn the user when an alarm event occurs.…”

Section: Introductionmentioning

confidence: 99%

EMG-based Real Time Facial Gesture Recognition for Stress Monitoring

Orguc

Khurana

Stanković

et al. 2018

2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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An electromyogram (EMG) signal acquisition system capable of real time classification of several facial gestures is presented. The training data consist of the facial EMG collected from 10 individuals (5 female/5 male). A custom-designed sensor interface integrated circuit (IC) consisting of an amplifier and an ADC, implemented in 65nm CMOS technology, has been used for signal acquisition [1]. It consumes 3.8nW power from a 0.3V battery. Feature extraction and classification is performed in software every 300ms to give real-time feedback to the user. Discrete wavelet transforms (DWT) are used for feature extraction in the time-frequency domain. The dimensionality of the feature vector is reduced by selecting specific wavelet decomposition levels without compromising the accuracy, which reduces the computation cost of feature extraction in embedded implementations. A support vector machine (SVM) is used for the classification. Overall, the system is capable of identifying several jaw movements such as clenching, opening the jaw and resting in real-time from a single channel EMG data, which makes the system suitable for providing biofeedback during sleeping and awake states for stress monitoring, bruxism, and several orthodontic applications such as temporomandibular joint disorder (TMJD).

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A 0.21 μJ patient-specific REM/Non-REM sleep classifier for Alzheimer patients

Altaf

Saadeh

2017

2017 IEEE Biomedical Circuits and Systems Conference (BioCAS)

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Wearable low-latency sleep stage classifier

Cited by 15 publications

References 10 publications

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

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

EMG-based Real Time Facial Gesture Recognition for Stress Monitoring

A 0.21 μJ patient-specific REM/Non-REM sleep classifier for Alzheimer patients

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