A
Abhay Upadhyay
Researcher at Bundelkhand University
Publications - 18
Citations - 760
Abhay Upadhyay is an academic researcher from Bundelkhand University. The author has contributed to research in topics: Wavelet transform & Computer science. The author has an hindex of 9, co-authored 15 publications receiving 518 citations. Previous affiliations of Abhay Upadhyay include Indian Institute of Technology Indore & National Institute of Technology Goa.
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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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Automatic sleep stages classification based on iterative filtering of electroencephalogram signals
TL;DR: The proposed method for automated classification of sleep stages based on iterative filtering of EEG signals has provided better tenfold cross- validation classification accuracy than other existing methods.
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Instantaneous voiced/non-voiced detection in speech signals based on variational mode decomposition
Abhay Upadhyay,Ram Bilas Pachori +1 more
TL;DR: Experimental results at various signal to noise ratios (SNRs) are included in order to show the effectiveness of the proposed method compared to the other existing methods for V/NV detection in speech signals.
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An integrated alcoholic index using tunable-Q wavelet transform based features extracted from EEG signals for diagnosis of alcoholism
TL;DR: A new way for diagnosis of alcoholism using Tunable-Q Wavelet Transform (TQWT) based features derived from EEG signals and establishing a novel Alcoholism Risk Index using three clinically significant features to discriminate the given classes by means of a single number is presented.
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Accurate tunable-Q wavelet transform based method for QRS complex detection
Ashish Sharma,Shivnarayan Patidar,Abhay Upadhyay,U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya +5 more
TL;DR: A high performance QRS complex detection scheme based on the tunable-Q wavelet transform (TQWT) is presented in this paper, which has yielded an average detection accuracy, sensitivity and positive productivity on the MIT-BIH arrhythmia database.