Biomedical AI Signal Processing Systems

Sleep Apnea Syndrome Signal Processing and Artificial Intelligence Homecare System:

Sleep apnea syndrome (SAS, 睡眠呼吸中止症) is a popular but easily-ignored disease for modern people. Traditional polysomnography (PSG) examination is labor-intensive SAS diagnosis method and the wearing devices of the PSG are complicated and uncomfortable. This research project cooperates with Prof. Po-Chiun Huang and Prof. Hsi-Pin Ma of NTHU EE, Doctor Yu-Lun Lo of Linkou Chang Gung Memorial Hospital, and Prof. Hau-Tieng Wu of Duke University, USA. This research develops a wearable 3D accelerometer/SpO2/ECG device as IoT Sensors and designs sleep-apnea event detection/classification/analysis algorithms on the Cloud AI computing Server for Tele-homecare monitoring systems.

Research topics : Fused detection of sleep apnea by 3D acceleromenter and ECG signals, Sensor IoT and Cloud AI analysis for SAS homecar systems.

3D accelerometer sensing system
Sleep Apnea detection algorithm     
  1. Yin-Yan Lin, Hau-tieng Wu, Chi-An Hsu, Po-Chiun Huang, Yuan-Hao Huang, and Yu-Lun Lo, “Sleep Apnea Detection Based on Thoracic and Abdominal Movement Signals of Wearable Piezo-Electric Bands, “ IEEE Journal of Biomedical and Health Informatics, vol. 21, no. 6, pp.1533-1545, Dec. 2017.
  2. Hau-tieng Wu, Jhao-Chen Wu, Po-Chun Huang, Ting-Yu Lin, Tsai_Yu Wang, Yuan-Hao Huang, and Yu-Lun Lo, “Phenotype-Based and Self-Learning Inter-Individual Sleep Apnea Screening With a Level IV-Like Monitoring System,” Frontier Physiology, Computational Physiology and Medicine, https://doi.org/10.3389/fphys.2018.00723, 02 July, 2018.
  3. Hung-Chi Chang, Hau-Tieng Wu, Po-Chiun Huang, Hsi-Pin Ma, Yu-Lun Lo, and Yuan-Hao Huang, “Portable Sleep Apnea Syndrome Screening and Event Detection Using Long Short-Term Memory Recurrent Neural Network“, Sensors, Vol. 20, no. 21, http://doi/org/10.3390/s20216067, 25 Oct. 2020.