Description
Technological tools and computational techniques have enhanced the healthcare industry. These advancements have led to significant progress in the diagnosis of heart disorders.
Electrocardiogram Signal Classification and Machine Learning: Emerging Research and Opportunities is a critical scholarly resource that examines the importance of automatic normalization and classification of electrocardiogram (ECG) signals of heart disorders. Featuring a wide range of topics such as common heart disorders, particle swarm optimization, and benchmarks functions, this publication is geared toward medical professionals, researchers, professionals, and students seeking current and relevant research on the categorization of ECG signals.
Author's/Editor's Biography
Sara Moein
Sara Moein, PhD, is currently a researcher in computational center at Mount Sinai School of Medicine, United States. Her interests are machine learning, algorithm designing, optimization and computational biology. She has received her PhD from Multimedia University, Malaysia in computer science. Her Master and Bachelor degrees are in software engineering. She is author of book Medical Diagnosis Using Artificial Neural Networks. Dr. Sara Moein is the member of editorial boards of some of the international journals such as Journal of Experimental & Theoretical Artificial Intelligence and Journal of Intelligent Automation & Soft Computing and others. In addition, she is reviewer of many journals and conferences papers such as Journal of Computing, Journal of Supercomputing and conferences such as ICINCO (2012-2016), WORLDCOMP (2009-2013) and 7th IEEE BIBE. She has a number of publications in book chapters, journals, and conference proceedings.