Talks：

Developing precision sleep medicine through artificial intelligence and IOT technology

Name：

劉文德(Wen-Te Liu)

Position：

1.Attending Physicians；2.Assistant Professor；3.Director of Research and Development

Affiliation：

1.Department of Pulmonary and Critical Care Medicine, Shuang-Ho Hospital 2.School of Respiratory Therapy,Taipei Medical University 3.Research Center of Sleep Medicine, College of Medicine, Taipei Medical University

Email：

lion5835@gmail.com

Photo：

Research Interests：

1.Artificial intelligence in medicine 2.Sleep medicine 3.Telecare 4.Pulmonary rehabilitation

Selected Publications：

◆ Liu WT, Lee KY, Lee HC, Chuang HC, Wu D, Juang JN, Chuang KJ. The association of annual air pollution exposure with blood pressure among patients with sleep-disordered breathing. Science of The Total Environment. 2016 Feb 1;543(Pt A):61-66 ◆ Tseng ST, Kao YH, Peng CC, Liu JY, Chu SC, Hong GF, Hsieh CH, Hsu KT, Liu WT, Huang YH, Huang SY, Chu TS. A 65nm CMOS low power impulse radar system for human respiratory feature extraction and diagnosis on respiratory diseases. (IEEE) Transactions on Microwave Theory and Techniques. 2016 April; 64(4):1029-1041 ◆ Liu WT, Wu HT, Juang JN, Wisniewski A, Lee HC, Wu D, Lo YL. Prediction of the severity of obstructive sleep apnea by anthropometric features via support vector machine. PLos One. 2017 May 4;12(5):e0176991 ◆ Shen YL, Liu WT, Lee KY, Chuang HC, Chen HW, Chuang KJ. Association of PM2.5 with sleep-disordered breathing from a population-based study in Northern Taiwan urban areas. Environmental Pollution. 2017 Oct 20;233:109-113. doi: 10.1016/j.envpol. 2017.10.052.

Abstract：

Sleep disorder is an important issue in modern society, that nearly one-third of the world’s population suffers from sleep problems. However, for the patients with sleep disorders, it is very complicated and difficult to solve because the inconvenience and complex of diagnostic methods as well as the lack of integration and following up of therapeutic strategies. Fortunately, in the recent years, by the rapid development of wearable technology and artificial intelligence (AI) we can quantify the patients’ bio-information to understand their sleep problems even leading to the treatment strategies. The massive information includes many kinds of biomarkers including inflammatory mediators caused by nocturnal intermittent hypoxemia, markers of neurodegenerative diseases by frequent arousal during sleep, physical activity of daily life, autonomic nerve status, even the environmental factors such as air pollution, allergen exposure, etc. Besides, the patient’s subjective feeling and sleep quality could also be analyzed by chatting program and structural questionnaire of a newly developed software in the mobile device by recording the patients’ voice and facial change before and after sleep through the AI technology. Therefore, we can integrate all the information from the home-based system into a prediction model of sleep problems, then develop an innovative therapeutic program to improve the patients with sleep disorders.