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Abhijit Bhattacharyya
Researcher at Indian Institute of Technology Indore
Publications - 19
Citations - 1315
Abhijit Bhattacharyya is an academic researcher from Indian Institute of Technology Indore. The author has contributed to research in topics: Wavelet transform & Wavelet. The author has an hindex of 12, co-authored 18 publications receiving 826 citations. Previous affiliations of Abhijit Bhattacharyya include National Institute of Technology, Hamirpur.
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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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Tunable-Q Wavelet Transform Based Multiscale Entropy Measure for Automated Classification of Epileptic EEG Signals
TL;DR: The performance measure of the proposed multi-scale entropy measure has been found to be comparable with the existing state of the art epileptic EEG signals classification methods studied using the same database.
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A novel approach for automated detection of focal EEG signals using empirical wavelet transform
Abhijit Bhattacharyya,Manish Sharma,Ram Bilas Pachori,Pradip Sircar,U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya +6 more
TL;DR: An automatic approach has been presented to detect electroencephalogram (EEG) signals of non-focal and focal groups to determine the area linked to the focal epilepsy and the developed prototype can be used for the epileptic patients and aid the clinicians to confirm diagnosis.
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Fourier–Bessel series expansion based empirical wavelet transform for analysis of non-stationary signals
TL;DR: The proposed method has provided better TF representation as compared to existing EWT method and Hilbert–Huang transform (HHT) method, especially when analyzed signal possesses closed frequency components and of short time duration.
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Tunable-Q Wavelet Transform Based Multivariate Sub-Band Fuzzy Entropy with Application to Focal EEG Signal Analysis
TL;DR: The complexity of multivariate electroencephalogram (EEG) signals in different frequency scales is analyzed for the analysis and classification of focal and non-focal EEG signals and the proposed multivariate sub-band entropy measure has been built based on tunable-Q wavelet transform (TQWT).