TY - GEN
T1 - Sleep Stage Classification based on BCG using Improved Deep Convolutional Generative Adversarial Networks
AU - Wu, Longwen
AU - Ren, Pengcheng
AU - Zhao, Yaqin
AU - Lv, Ruchen
AU - Ding, Qinyu
AU - Zuo, Yirui
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Monitoring sleep quality and status is important to learn health condition for improvement and prevent sleep apnea. Sleep stage classification based on Ballistocardiography (BCG) has attracted more attention due to its simplicity in equipment usage and high positioning accuracy. Firstly, this paper tries to reconstruct Electrocardiogram (ECG) signals from BCG signals using an improved Deep Convolutional Generative Adversarial Networks (DCGAN) model. Then an optimal Support Vector Machine (SVM) model is exploited for sleep stage classification, in which a Particle Swarm Optimization (PSO) is combined with a Genetic Algorithm (GA) to train the classifier. Finally, the accuracy of four-stage and six-stage SVM models are analyzed and compared using Heart Rate Variability (HRV), incorporating HRV and Respiratory Variability (RV) features. The results show that the accuracy of the four-stage and six-stage SVM models using RV features for sleep stage classification reaches 76.38% and 71.11%, respectively.
AB - Monitoring sleep quality and status is important to learn health condition for improvement and prevent sleep apnea. Sleep stage classification based on Ballistocardiography (BCG) has attracted more attention due to its simplicity in equipment usage and high positioning accuracy. Firstly, this paper tries to reconstruct Electrocardiogram (ECG) signals from BCG signals using an improved Deep Convolutional Generative Adversarial Networks (DCGAN) model. Then an optimal Support Vector Machine (SVM) model is exploited for sleep stage classification, in which a Particle Swarm Optimization (PSO) is combined with a Genetic Algorithm (GA) to train the classifier. Finally, the accuracy of four-stage and six-stage SVM models are analyzed and compared using Heart Rate Variability (HRV), incorporating HRV and Respiratory Variability (RV) features. The results show that the accuracy of the four-stage and six-stage SVM models using RV features for sleep stage classification reaches 76.38% and 71.11%, respectively.
KW - Ballistocardiography
KW - Heart Rate Variability
KW - Improved DCGAN
KW - Non-contact Sleep Monitoring
KW - Support Vector Machine
UR - https://www.scopus.com/pages/publications/85183467459
U2 - 10.1109/ICISPC59567.2023.00020
DO - 10.1109/ICISPC59567.2023.00020
M3 - 会议稿件
AN - SCOPUS:85183467459
T3 - Proceedings - 2023 7th International Conference on Imaging, Signal Processing and Communications, ICISPC 2023
SP - 65
EP - 69
BT - Proceedings - 2023 7th International Conference on Imaging, Signal Processing and Communications, ICISPC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th International Conference on Imaging, Signal Processing and Communications, ICISPC 2023
Y2 - 21 July 2023 through 23 July 2023
ER -