Journal ArticleDOI
Noncontact Sleep Stage Estimation Using a CW Doppler Radar
TLDR
Comparative analysis of the classification performance under different sleep stage patterns with prior works has been carried out to show the significant improvements over state-of-the-art solutions, and suggest that the proposed scheme is suitable for long-term sleep monitoring.Abstract:
Sleep stage estimation is crucial to the evaluation of sleep quality and is a proven biometric in diagnosing cardiovascular diseases. In this paper, we design a continuous wave (CW) Doppler radar to accurately measure sleep-related signals, including respiration, heartbeat, and body movement. Body movement index, respiration per minute (RPM), variance of RPM, amplitude difference accumulation (ADA) of respiration, rapid eye movement parameter, sample entropy, heartbeat per minute (HPM), variance of HPM, ADA of heartbeat, deep parameter, and time feature have been extracted and fed into different machine learning classifiers. A total of 11 all night polysomnography recordings from 13 healthy examinees were used to validate the proposed CW Doppler radar system and the ability to detect sleep stage information from it. Comparative studies and statistical results have shown that the subspace K-nearest neighbor algorithm outperforms the other classifiers with the highest accuracy of up to 86.6%. With the Relief F algorithm, features have been ranked, and the selected feature subsets have been preliminary tested to identify the optimal feature subset. Meanwhile, comparative analysis of our classification performance under different sleep stage patterns with prior works has been carried out to show the significant improvements over state-of-the-art solutions. These results suggest that the proposed scheme is suitable for long-term sleep monitoring.read more
Citations
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Journal ArticleDOI
Radar Signal Processing for Sensing in Assisted Living: The challenges associated with real-time implementation of emerging algorithms
Julien Le Kernec,Francesco Fioranelli,Chuanwei Ding,Heng Zhao,Sun Li,Hong Hong,Jordane Lorandel,Olivier Romain +7 more
TL;DR: This article covers radar signal processing for sensing in the context of assisted living through three example applications: human activity recognition for activities of daily living, respiratory disorders, and sleep stages classification.
Journal ArticleDOI
Sleep stage classification from heart-rate variability using long short-term memory neural networks
Mustafa Radha,Pedro Fonseca,Pedro Fonseca,Arnaud Moreau,Marco Ross,Andreas Cerny,Peter Anderer,Xi Long,Xi Long,Ronald M. Aarts,Ronald M. Aarts +10 more
TL;DR: A long short-term memory (LSTM) network is proposed as a solution to model long-term cardiac sleep architecture information and validated on a comprehensive data set, demonstrating the merit of deep temporal modelling using a diverse data set and advancing the state-of-the-art for HRV-based sleep stage classification.
Journal ArticleDOI
A Novel Vital-Sign Sensing Algorithm for Multiple Subjects Based on 24-GHz FMCW Doppler Radar
TL;DR: A novel non-contact vital-sign sensing algorithm for use in cases of multiple subjects is proposed, using a 24 GHz frequency-modulated continuous-wave Doppler radar with the parametric spectral estimation method to identify multiple targets.
Journal ArticleDOI
Differential Enhancement Method for Robust and Accurate Heart Rate Monitoring via Microwave Vital Sign Sensing
TL;DR: A novel method, called differential enhancement (DE), was proposed, which can effectively eliminate the effects of the respiration harmonic interference on HR estimation, including the likely adjacent harmonic interference.
Journal ArticleDOI
Microwave Sensing and Sleep: Noncontact Sleep-Monitoring Technology With Microwave Biomedical Radar
TL;DR: A good night's sleep is an important part of a healthy life, and poor sleep quality can contribute to weight gain, depression, heart disease, inflammation, emotional problems, energy imbalance, and social interaction disorders.
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