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kernel-principal-component-analyses--kpca
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Stockwell transform for epileptic seizure detection from EEG signals
, Article Biomedical Signal Processing and Control ; Volume 38 , 2017 , Pages 108-118 ; 17468094 (ISSN) ; Shayesteh, M. G ; Sharif University of Technology
Abstract
Epilepsy is the most common disorder of human brain. The goal of this paper is to present a new method for classification of epileptic phases based on the sub-bands of electroencephalogram (EEG) signals obtained from the Stockwell transform (ST). ST is a time-frequency analysis that not only covers the advantages of both short-time Fourier transform (FT) and wavelet transform (WT), but also overcomes their shortcomings. In the proposed method, at first, EEG signal is transformed into time-frequency domain using ST and all operations are performed in the new domain. Then, the amplitudes of ST in five sub-bands, namely delta (δ), theta (θ), alpha (α), beta (β), and gamma (γ), are computed. In...
Significant pathological voice discrimination by computing posterior distribution of balanced accuracy
, Article Biomedical Signal Processing and Control ; Volume 73 , 2022 ; 17468094 (ISSN) ; Jahed, M ; Sharif University of Technology
Elsevier Ltd
2022
Abstract
The ability to speak lucidly plays a key role in social relations. Consequently, the role of the larynx is quite important, and timely diagnosis of laryngeal diseases has proved to be crucial. In this study, a simple computational model for inverse of speech production model is employed to extract the glottal waveform using speech signal. This waveform has useful information about vocal folds performance in terms of providing evidence for distinguishing pathological disorders. Furthermore, obtaining the significance of classification results is important, because it leads to reliable inferences. This study utilizes the sustained vowel sound /a/ and a well-referenced database, namely MEEI. In...