Prof. Ram Bilas Pachori

FIEEE, FIET, FAAIA
Indian Institute of Technology Indore, India

Dr. Ram Bilas Pachori (FIEEE, FNAE, FIET, FAAIA, FIETE, FIE) is currently Institute Chair Professor and Professor (HAG) in the Department of Electrical Engineering at IIT Indore, India. He is also Adjunct Professor in the Department of Electronics & Communication Engineering at Dr. B.R. Ambedkar National Institute of Technology, Jalandhar, India. His research interests include signal and image processing, biomedical signal processing, non-stationary signal processing, speech signal processing, brain-computer interface, artificial intelligence and internet of things in healthcare, and machine learning for signal processing. He has 404 publications, which include journal articles (260), conference papers (102), books (12), and book chapters (30). He has also 10 patents, including 07 granted (01 Australian patent and 06 Indian patents) and 03 filed (Indian patents). His publications have been cited more than 21,500 times with h-index of 80 according to Google Scholar. He is IEEE EMBS Distinguished Lecturer and Academy Mentor for EURASIP Academy. Currently, he is Associate Editor of Electronics Letters, Computers and Electrical Engineering, and Biomedical Signal Processing and Control. He is also serving as Deputy Editor-in-Chief of IETE Journal of Research, Handling Editor of Signal Processing, and Editor of IETE Technical Review journal.

 

Speech Title: "Signal Processing methods for medical data analysis and classification"

 

Abstract: The electroencephalogram (EEG) and electrocardiogram (ECG) signals are commonly used for diagnosis of brain and heart diseases, respectively. The doctors generally perform diagnosis based on visual inspection of these physiological signals. This type of diagnosis procedure is very time-consuming specially for long-data recordings. Moreover, such a diagnosis approach is subjective in nature and requires expertise or training. Signal processing methods together with machine learning (ML) techniques can help in designing intelligent systems for the automated diagnosis of brain and heart diseases based on these physiological signals. Such ML-driven intelligent systems can help doctors in their diagnosis. Moreover, such intelligent systems can detect the diseases at an initial level so that timely treatment can be carried out. Our research group has developed various methods which are based on signal processing and ML for the brain and heart diseases. These developed methods together with experimental results will be presented in this talk.