R
Ram Bilas Pachori
Researcher at Indian Institute of Technology Indore
Publications - 222
Citations - 11302
Ram Bilas Pachori is an academic researcher from Indian Institute of Technology Indore. The author has contributed to research in topics: Wavelet transform & Computer science. The author has an hindex of 48, co-authored 182 publications receiving 8140 citations. Previous affiliations of Ram Bilas Pachori include Ngee Ann Polytechnic & Indian Institute of Technology Kanpur.
Papers
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Journal ArticleDOI
Classification of Seizure and Nonseizure EEG Signals Using Empirical Mode Decomposition
Varun Bajaj,Ram Bilas Pachori +1 more
TL;DR: The proposed method for classification of EEG signals based on the bandwidth features (BAM and BFM) and the LS-SVM has provided better classification accuracy than the method adopted by Liang and coworkers in their study published in 2010.
Journal ArticleDOI
Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions
Rajeev Sharma,Ram Bilas Pachori +1 more
TL;DR: New features based on the 2D and 3D PSRs of IMFs have been proposed for classification of epileptic seizure and seizure-free EEG signals.
Journal ArticleDOI
A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension
TL;DR: It appears that a system is in place to assist clinicians to diagnose seizures accurately in less time as the proposed model achieves perfect 100% classification sensitivity and is found to be outperforming all existing models in terms of classification sensitivity (CSE).
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A Multivariate Approach for Patient-Specific EEG Seizure Detection Using Empirical Wavelet Transform
TL;DR: Efficient detection of epileptic seizure is achieved when seizure events appear for long duration in hours long EEG recordings and the proposed method develops time–frequency plane for multivariate signals and builds patient-specific models for EEG seizure detection.
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Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals
TL;DR: The proposed method is able to differentiate the focal and non-focal EEG signals with an average classification accuracy of 87% correct and can be useful in assessing the nonlinear interrelation and complexity of focal and other EEG signals.