Ram Bilas Pachori

Ram Bilas Pachori

Ram Bilas Pachori received the B.E. degree with honours in ECE from RGTU, Bhopal, India in 2001, the M.Tech. and Ph.D. degrees in EE from Indian Institute of Technology (IIT) Kanpur, India in 2003 and 2008, respectively. He worked as a Postdoctoral Fellow at Charles Delaunay Institute, University of Technology of Troyes, Troyes, France during 2007-2008. He is presently working as a Professor at IIT Indore. He worked as a Visiting Scholar at Intelligent Systems Research Center, Ulster University, Northern Ireland, UK during December 2014. He is an Associate Editor of Electronics Letters, Biomedical Signal Processing and Control journal and an Editor of IETE Technical Review journal. He is a senior member of IEEE and a Fellow of IETE and IET. He has supervised 12 Ph.D., 20 M.Tech., and 37 B.Tech. students for their theses and projects.

Publications

AI-Enabled Smart Healthcare Using Biomedical Signals
AI-Enabled Smart Healthcare Using Biomedical Signals
Rahul Kumar Chaurasiya, Dheeraj Agrawal, Ram Bilas Pachori. © 2022. 322 pages.
Technological advancements have enhanced all functions of society and revolutionized the healthcare field. Smart healthcare applications and practices have grown within the past...
Emotion Identification From TQWT-Based EEG Rhythms
Emotion Identification From TQWT-Based EEG Rhythms
Aditya Nalwaya, Kritiprasanna Das, Ram Bilas Pachori. © 2022. 22 pages.
Electroencephalogram (EEG) signals are the recording of brain electrical activity, commonly used for emotion recognition. Different EEG rhythms carry different neural dynamics....
Empirical Wavelet Transform-Based Framework for Diagnosis of Epilepsy Using EEG Signals
Empirical Wavelet Transform-Based Framework for Diagnosis of Epilepsy Using EEG Signals
Sibghatullah I. Khan, Ram Bilas Pachori. © 2022. 23 pages.
In the chapter, a novel yet simple method for classifying EEG signals associated with normal and epileptic seizure categories has been proposed. The proposed method is based on...
Handbook of Research on Advancements of Artificial Intelligence in Healthcare Engineering
Handbook of Research on Advancements of Artificial Intelligence in Healthcare Engineering
Dilip Singh Sisodia, Ram Bilas Pachori, Lalit Garg. © 2020. 420 pages.
Artificial intelligence (AI) is revolutionizing every aspect of human life including human healthcare and wellbeing management. Various types of intelligent healthcare...
Classification of EMG Signals Using Eigenvalue Decomposition-Based Time-Frequency Representation
Classification of EMG Signals Using Eigenvalue Decomposition-Based Time-Frequency Representation
Rishi Raj Sharma, Mohit Kumar, Ram Bilas Pachori. © 2020. 23 pages.
Electromyogram (EMG) signals are commonly used by doctors to diagnose abnormality of muscles. Manual analysis of EMG signals is a time-consuming and cumbersome task. Hence, this...
Automated Seizure Classification Using Deep Neural Network Based on Autoencoder
Automated Seizure Classification Using Deep Neural Network Based on Autoencoder
Rahul Sharma, Pradip Sircar, Ram Bilas Pachori. © 2020. 19 pages.
A neurological abnormality in the brain that manifests as a seizure is the prime risk of epilepsy. The earlier and accurate detection of the epileptic seizure is the foremost...
Three Channel Wavelet Filter Banks With Minimal Time Frequency Spread for Classification of Seizure-Free and Seizure EEG Signals
Three Channel Wavelet Filter Banks With Minimal Time Frequency Spread for Classification of Seizure-Free and Seizure EEG Signals
Dinesh Bhati, Akruti Raikwar, Ram Bilas Pachori, Vikram M. Gadre. © 2020. 17 pages.
The authors compute the classification accuracy of minimal time-frequency spread wavelet filter bank with three channels in discriminating seizure-free and seizure...
Automated Classification of Focal and Non-Focal EEG Signals Based on Bivariate Empirical Mode Decomposition
Automated Classification of Focal and Non-Focal EEG Signals Based on Bivariate Empirical Mode Decomposition
Rajeev Sharma, Ram Bilas Pachori. © 2018. 21 pages.
The chapter presents a new approach of computer aided diagnosis of focal electroencephalogram (EEG) signals by applying bivariate empirical mode decomposition (BEMD). Firstly...