A
Anurag Nishad
Researcher at Indian Institute of Technology Indore
Publications - 8
Citations - 262
Anurag Nishad is an academic researcher from Indian Institute of Technology Indore. The author has contributed to research in topics: Wavelet transform & Fundamental frequency. The author has an hindex of 5, co-authored 8 publications receiving 173 citations.
Papers
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Journal ArticleDOI
Cross-terms reduction in the Wigner-Ville distribution using tunable-Q wavelet transform
Ram Bilas Pachori,Anurag Nishad +1 more
TL;DR: A new method to reduce cross-terms in the Wigner-Ville distribution (WVD) using tunable-Q wavelet transform (TQWT), which exploits the advantages of sub-band filtering of filter-bank and also retaining the time-resolution property of the wavelet decomposition to achieve signal decomposition.
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Application of TQWT based filter-bank for sleep apnea screening using ECG signals
TL;DR: This paper uses single-lead electrocardiogram (ECG) signal to detect apneic and non-apneic events using tunable-Q wavelet transform based filter-bank instead of TQWT to decompose the segment of ECG signal into several constant bandwidth sub-band signals.
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Automated classification of hand movements using tunable-Q wavelet transform based filter-bank with surface electromyogram signals
Anurag Nishad,Abhay Upadhyay,Ram Bilas Pachori,U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya +5 more
TL;DR: T tunable- Q wavelet transform based filter-bank is applied for decomposition of cross-covariance of sEMG (csEMG) signals for basic hand movements classification using Kraskov entropy features.
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Classification of epileptic electroencephalogram signals using tunable-Q wavelet transform based filter-bank
Anurag Nishad,Ram Bilas Pachori +1 more
TL;DR: Acc of 99% is obtained in the classification of normal, seizure-free, and seizure EEG signals using the proposed method, which is ready to be tested using huge database and can be employed to aid the epileptologists to screen the seizure- free and seizure patients accurately.
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Classification of emotions from EEG signals using time-order representation based on the S-transform and convolutional neural network
TL;DR: In this article, a novel time-order representation based on the S-transform and convolutional neural network (CNN) is proposed for the identification of human emotions, which helps in the development of affective computing, braincomputer interface, medical diagnosis system, etc.