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Invited Speakers

Assoc. Prof. Dakun Lai

University of Electronic Science and Technology of China, China

 

Dr. Lai is currently the director of the Biomedical Imaging and Electrophysiology Lab at the University of Electronic Science and Technology of China (UESTC). He received his Ph.D. in Medical Electronics from Fudan University in 2008. Then he completed a three-year Postdoctoral Associate in Biomedical Engineering at the University of Minnesota, USA. From 2012, he has been on the faculty of the School of Electronic Science and Technology, UESTC, China, where he was appointed as an Associate Professor of Electrical Science and Technology. Dr. Lai is members of IEEE and the Engineering in Medicine and Biology Society, and the member of American Heart Associate. He has served as a peer reviewer of IEEE Transction on Biomedical Egnineering, IEEE ACCESS, and related Chinese Journals. He has publised 30 peer-reviewed papers in Circulation, Physics in Medicine and Biology, IEEE Transcation on Information Technology in Biomedicine etc. and holds 20 Chinese Patents. His research interests and main contributions include computational medicine and deep learning, bioelelctromagnetics and medical applications, automated detection and prediction cardiac/neruo electrical disorder.

 

Speech Title: "Prospective Applications of Machine Learning Together with Wearable Flexible Dry Electrodes in Cardiac Arrhythmia Diagnosis"

 

Abstract: Artificial intelligence has transformed key features of human life. Machine learning is a subset of artificial intelligence in which machines autonomously acquire information by extracting patterns from large databases. It has been progressively used in the medical science and clinical diagnosis especially within the domain of cardiac electrical disorders, such as precise detection of cardiac electrical arrhythmias and further earlier prediction of such serious diseases as sudden cardiac death. Compared with manual analysis and diagnosis in past, it shows great superiority under such current mass clinical bio-signal data, which is promoted by the modern fast communication technology and advanced wearable long-term monitoring systems. Modern machine learning models can automatically identify different electrocardiograms (ECG) with high precision; moreover automatically extract all interested features and clinically significant parameters. Several deep learning models have been developed for the high fidelity detection of common rhythm disturbances as in case of atrial fibrillation and complex arrhythmias. Here, we have highlighted numerous applications of machine learning for prediction and early detection of cardiac electrical disorders. Machine learning algorithms try to develop the model by using all the available input. In future it will be used for more healthcare areas to improve the quality of diagnosis.