Anurag Nishad

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.

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.

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.

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.

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.