Partha Sarati Das received the Ph.D. degree in electronic engineering from the Kwangwoon University, South Korea, in 2019, where he was engaged in development of flexible dry biopotential electrodes and sensors for wearable electrophysiological signals monitoring system applications. He is currently working as Post-Doctoral Fellow at Biomedical Microsystems Laboratory (BML) in the Department of Electrical and Computer Engineering at Université Laval. His research interests include biopotential signal monitoring, chemical and physical sensors, wearable health care applications, energy harvesting devices, and flexible electronics. He received the M.S. degree in electrical and information engineering from Seoul National University of Science and Technology, South Korea, in 2015. He received the B.S. and M. Eng. degree in electrical and electronic engineering from the American International University – Bangladesh in 2008 and Dhaka University of Engineering and Technology, Bangladesh in 2012. From 2011 to 2013, he was an Officer in the Information Technology Division with EXIM Bank Ltd.
Project : A multimodal seizure detection artificial intelligence-based smart wear
Epilepsy is a chronic neurological condition that affects as many as 1 in every 100 Canadians. With the ageing of the population, the prevalence and incidence of epilepsy among the elderly is greater than any other age group due to the increasing number brain insults. The first line of treatment consists of long-term drug therapy but more than a third of patients are said to be drug-resistant and continue to suffer from disabling seizures. Due to their unpredictable nature, uncontrolled seizures represent a major personal handicap and source of worriment for patients (risks of injury and death). In addition, persistent seizures constitute a considerable public health burden due to high use of health care resources, high number of disability days or unemployment, and low annual income. For these patients, accurate and rapid detection of seizures could significantly improve their care. Indeed, a system capable of detecting seizures could alert family members, caretakers or medical personnel to intervene in a timely fashion to limit the risk of injuries and death. Hence, the main objective of this project is to develop a user-friendly artificial intelligence-based seizure detection system based on non-invasive multimodal physiological signals obtained through smart wearable devices.