TY - GEN
T1 - Contactless Transfer Learning Based Apnea Detection System for Wi-Fi CSI Networks
AU - Chen, Chia Yu
AU - Hsiao, An Hung
AU - Chiu, Chun Jie
AU - Feng, Kai Ten
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Sleep apnea syndrome is a common sleep disorder that can lead to a variety of diseases. The traditional diagnostic method, polysomnography (PSG), is time-consuming, expensive, and inconvenient for patients. In this paper, we proposed the transfer learning based apnea detection (TLAD) system as a non-contact based method utilizing the channel state information (CSI) from commercial Wi-Fi devices. In order to reduce the overhead of collecting CSI data and improving efficiency during training process, the transfer learning technique is applied to establish pre-Trained model by utilizing open source contact-based thoracic movement data. Moreover, existing research works detect apnea based on breathing pauses and shallow breathing periods, which are not effective to identify complex apnea characteristics. This potential drawback is overcome in proposed TLAD system since both CSI amplitude and frequency features are extracted for apnea classification. Our experimental results showed that the TLAD system achieves an F1-score of 90.1, which is superior to other existing methods.
AB - Sleep apnea syndrome is a common sleep disorder that can lead to a variety of diseases. The traditional diagnostic method, polysomnography (PSG), is time-consuming, expensive, and inconvenient for patients. In this paper, we proposed the transfer learning based apnea detection (TLAD) system as a non-contact based method utilizing the channel state information (CSI) from commercial Wi-Fi devices. In order to reduce the overhead of collecting CSI data and improving efficiency during training process, the transfer learning technique is applied to establish pre-Trained model by utilizing open source contact-based thoracic movement data. Moreover, existing research works detect apnea based on breathing pauses and shallow breathing periods, which are not effective to identify complex apnea characteristics. This potential drawback is overcome in proposed TLAD system since both CSI amplitude and frequency features are extracted for apnea classification. Our experimental results showed that the TLAD system achieves an F1-score of 90.1, which is superior to other existing methods.
UR - http://www.scopus.com/inward/record.url?scp=85145649859&partnerID=8YFLogxK
U2 - 10.1109/PIMRC54779.2022.9977900
DO - 10.1109/PIMRC54779.2022.9977900
M3 - Conference contribution
AN - SCOPUS:85145649859
T3 - IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC
SP - 788
EP - 793
BT - 2022 IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 33rd IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2022
Y2 - 12 September 2022 through 15 September 2022
ER -