Talks：

Sensor Fusion and AI Analysis for SDB Healthcare

Name：

黃柏鈞(Po-Chiun Huang)

Position：

Professor and Associate Chair

Affiliation：

Dept. of Electrical Engineering, National Tsing Hua Univ.

Email：

pchuang@ee.nthu.edu.tw

Photo：

Research Interests：

Cognitive System for Healthcares and Sports System Prototyping and Signal Processing Mixed-Signal Integrated Circuit Realization

Selected Publications：

1. H.-T. Wu, J.-C. Wu, P.-C. Huang, T.-Y. Lin, T.-Y. Wang, Y.-H. Huang, and Y.-L. Lo, “Phenotype-Based and Self-Learning Inter-Individual Sleep Apnea Screening with a Level IV-Like Monitoring System," Frontiers in Physiology, vol. 9, article 723, Jul. 2018. 2. J.-C. Wu, C.-W. Wang, Y.-H. Huang, H.-T. Wu, P.-C. Huang, and Y.-L. Lo, “A Portable Monitoring System with Automatic Event Detection for Sleep Apnea Level-IV Evaluation," IEEE Int. Symp. on Circuits and Systems, (ISCAS), Italy, 2018. 3. A. Sinha, P. Gopinathan, Y.-D. Chung, H.-Y. Lin, K.-H. Li, H.-P. Ma, P.-C. Huang, S.-C. Shiesh, and G.-B. Lee, “An Integrated Microfluidic Platform to Perform Uninterrupted SELEX Cycles to Screen Affinity Reagents Specific to Cardiovascular Biomarkers," Biosensors and Bioelectronics, 122, pp. 104-112, Sep. 2018.

Abstract：

The population with sleep-disordered breathing (SDB) is continuously increasing with the obesity, aging, and stress. Although the untreated SDB will increase the risk of health problems, most patients are not aware of it. In this talk a sensing system that is easy to install at home, cheap, and not interfere sleep, is introduced. Artificial intelligence (AI) techniques are proposed to achieve automatic annotation for the collected signal. Clinical results show high accuracy for the screening purpose.