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This book presents an innovative method of EEG-based feature extraction and classification of seizures using EEG signals. It describes the methodology required for EEG analysis, seizure detection, seizure prediction and seizure classification.
This book presents an innovative method of EEG-based feature extraction and classification of seizures using EEG signals. It describes the methodology required for EEG analysis, seizure detection, seizure prediction and seizure classification.
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Ratnaprabha Ravindra Borhade is an academician at the Cummins College of Engineering for Women, Pune Maharashtra, India. She is an assistant professor of electronics and telecommunication engineering. She completed her PhD in biomedical signal processing. She has 15 years of teaching experience. She has published research papers in the SCI Indexing list and has authored more than four book chapters. Her area of interest is biomedical signal processing and brain imaging.
Manoj S. Nagmode is a professor and head of department at the Government College of Engineering and Research, Avasari, Pune, India, in the Department of Electronics and Telecommunication Engineering. He has more than 22 years of teaching experience. He has published more than 45 research papers in various reputed journals and conferences. He is the author of a Taylor & Francis series book on research methodology. He has worked on different committees for the evaluation of research papers. His areas of interest include image processing, signal processing, and embedded systems. He was awarded a PhD by the University of Pune, India, in 2009 in electronics and telecommunication engineering in the domain of video processing.
Ravindra Honaji Borhade has published more than 20 research papers in various reputed journals and conferences. He is the author of two books regarding mobile computing and the basics of computer networks. He has guided more than 38 undergraduate projects and 18 postgraduate projects. He has worked at the institute level in charge of a "Techtonic" national-level technical event for more than ten years. He was in charge of the engineering club of the Sinhgad Student Council (SSC) for more than ten years and led the institute website for more than seven years. His areas of interest include mobile communication, networking, security, Internet of Things (IoT), image processing, signal processing, and cloud computing.
Vinayak. K. Bairagi earned an ME (electronic) from Sinhgad College of Engineering, Pune, in 2007. Savitribai Phule Pune University awarded him a PhD in engineering. He has 15 years of teaching experience and 8 years of research experience. He has filed 12 patents and 5 copyrights in the technical field. He has published more than 60 papers, of which 26 papers are in international journals and of which 15 papers are in the SCI Indexing list. He has authored/edited more than eight books/book chapters. He is a reviewer for nine scientific journals. He has received grants from DST SERB, UoP-BUCD, and GYTI. He has also received more than 14 awards, including the national-level Young Engineer Award (2014), the ISTE national level Young Researcher Award (2015) for his excellence in the field of engineering, and the IETE M N SAHA Memorial Award (2018). He is a member of INENG (UK), IETE (India), ISTE (India), IEI and BMS (India). He worked on image compression at the College of Engineering, Pune, under Pune University. His main research interests include medical imaging, machine learning, computer-aided diagnosis, and medical signal processing. Currently, he is associated with the AISSMS Institute of Information Technology, Pune, as a professor in electronics and telecommunication engineering. He is a recognized PhD guide in electronics engineering at Savitribai Phule Pune University. Presently, he is guiding seven PhD students.
Inhaltsangabe
1: Introduction 2: Electroencephalography 3: Epilepsy Detection 4: Existing Methods for Epileptic Seizure Prediction using Electroencphalegram Signals 5: Epileptic Seizure Prediction with EEG Signal Using Deep Recurrent Neural Network 6: Epileptic Seizure Prediction using Squirrle Atom Search Optimization Algorithm based Deep RNN 7: Contribution of Research and Conclusion
1: Introduction 2: Electroencephalography 3: Epilepsy Detection 4: Existing Methods for Epileptic Seizure Prediction using Electroencphalegram Signals 5: Epileptic Seizure Prediction with EEG Signal Using Deep Recurrent Neural Network 6: Epileptic Seizure Prediction using Squirrle Atom Search Optimization Algorithm based Deep RNN 7: Contribution of Research and Conclusion
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