TY - JOUR
T1 - A Noncontact Breathing Disorder Recognition System Using 2.4-GHz Digital-IF Doppler Radar
AU - Zhao, Heng
AU - Hong, Hong
AU - Miao, Dongyu
AU - Li, Yusheng
AU - Zhang, Haitao
AU - Zhang, Yingming
AU - Li, Changzhi
AU - Zhu, Xiaohua
N1 - Publisher Copyright:
© 2013 IEEE.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2019/1
Y1 - 2019/1
N2 - In this paper, a noncontact breathing disorder recognition system has been proposed for identifying irregular breathing patterns. The proposed system consists of a Doppler radar-based sensor module and a machine-learning-based breathing disorder recognition module. A custom-designed 2.4-GHz continuous wave digital-IF Doppler radar is utilized as the radar sensor module to accurately capture the time-domain breathing waveform. Then, a recognition module is designed with selected features and optimized classifiers. Four sets of experiments have been carried out to evaluate the proposed system comprehensively. For the laboratorial experiments, the proposed system achieves 94.7% classification accuracy using the linear support vector machine classifier with seven selected features. Results of clinical experiments demonstrate the feasibility of long-Term breathing disorder recognition with good accuracy and robustness, and illustrate the potential of the proposed solution for the auxiliary diagnosis of diseases.
AB - In this paper, a noncontact breathing disorder recognition system has been proposed for identifying irregular breathing patterns. The proposed system consists of a Doppler radar-based sensor module and a machine-learning-based breathing disorder recognition module. A custom-designed 2.4-GHz continuous wave digital-IF Doppler radar is utilized as the radar sensor module to accurately capture the time-domain breathing waveform. Then, a recognition module is designed with selected features and optimized classifiers. Four sets of experiments have been carried out to evaluate the proposed system comprehensively. For the laboratorial experiments, the proposed system achieves 94.7% classification accuracy using the linear support vector machine classifier with seven selected features. Results of clinical experiments demonstrate the feasibility of long-Term breathing disorder recognition with good accuracy and robustness, and illustrate the potential of the proposed solution for the auxiliary diagnosis of diseases.
KW - Doppler radar
KW - breathing disorder
KW - noncontact vital sign detection
KW - support vector machine (SVM)
UR - http://www.scopus.com/inward/record.url?scp=85044337115&partnerID=8YFLogxK
U2 - 10.1109/JBHI.2018.2817258
DO - 10.1109/JBHI.2018.2817258
M3 - Article
C2 - 29993789
AN - SCOPUS:85044337115
VL - 23
SP - 208
EP - 217
JO - IEEE Journal of Biomedical and Health Informatics
JF - IEEE Journal of Biomedical and Health Informatics
SN - 2168-2194
IS - 1
M1 - 8322142
ER -