Classification of normal and depressed EEG signals based on centered correntropy of rhythms in empirical wavelet transform domain

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Hesam Akbari ◽  
Muhammad Tariq Sadiq ◽  
Ateeq Ur Rehman
Keyword(s):  
Wavelet Transform ◽  
Transform Domain ◽  
Eeg Signals ◽  
Empirical Wavelet Transform

Classification of epileptic EEG signals using sparse spectrum based empirical wavelet transform

2020 ◽  
Vol 56 (25) ◽  
pp. 1370-1372
Author(s):  
A. Nishad ◽  
A. Upadhyay ◽  
G. Ravi Shankar Reddy ◽  
V. Bajaj
Keyword(s):  
Wavelet Transform ◽  
Eeg Signals ◽  
Empirical Wavelet Transform

Cross-wavelet transform as a new prototype for classification of EEG signals

2018 ◽  
Vol 7 (3) ◽  
pp. 348-358 ◽  
Author(s):  
Priyadarshiny Dhar ◽  
Saibal Dutta ◽  
V. Mukherjee ◽  
Abhijit Dhar ◽  
Prithwiraj Das
Keyword(s):  
Wavelet Transform ◽  
Eeg Signals ◽  
Cross Wavelet Transform ◽  
Cross Wavelet

scholarly journals Automatic Segmentation and Classification of Heart Sounds Using Modified Empirical Wavelet Transform and Power Features

2020 ◽  
Vol 10 (14) ◽  
pp. 4791 ◽  
Author(s):  
Pedro Narváez ◽  
Steven Gutierrez ◽  
Winston S. Percybrooks
Keyword(s):  
Wavelet Transform ◽  
Heart Sound ◽  
State Of The Art ◽  
Automatic Segmentation ◽  
Automatic Classification ◽  
The State ◽  
Heart Sounds ◽  
Empirical Wavelet Transform ◽  
Art Methods

A system for the automatic classification of cardiac sounds can be of great help for doctors in the diagnosis of cardiac diseases. Generally speaking, the main stages of such systems are (i) the pre-processing of the heart sound signal, (ii) the segmentation of the cardiac cycles, (iii) feature extraction and (iv) classification. In this paper, we propose methods for each of these stages. The modified empirical wavelet transform (EWT) and the normalized Shannon average energy are used in pre-processing and automatic segmentation to identify the systolic and diastolic intervals in a heart sound recording; then, six power characteristics are extracted (three for the systole and three for the diastole)—the motivation behind using power features is to achieve a low computational cost to facilitate eventual real-time implementations. Finally, different models of machine learning (support vector machine (SVM), k-nearest neighbor (KNN), random forest and multilayer perceptron) are used to determine the classifier with the best performance. The automatic segmentation method was tested with the heart sounds from the Pascal Challenge database. The results indicated an error (computed as the sum of the differences between manual segmentation labels from the database and the segmentation labels obtained by the proposed algorithm) of 843,440.8 for dataset A and 17,074.1 for dataset B, which are better values than those reported with the state-of-the-art methods. For automatic classification, 805 sample recordings from different databases were used. The best accuracy result was 99.26% using the KNN classifier, with a specificity of 100% and a sensitivity of 98.57%. These results compare favorably with similar works using the state-of-the-art methods.

scholarly journals Motor Imagery EEG Signals Decoding by Multivariate Empirical Wavelet Transform-Based Framework for Robust Brain–Computer Interfaces

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 171431-171451 ◽  
Author(s):  
Muhammad Tariq Sadiq ◽  
Xiaojun Yu ◽  
Zhaohui Yuan ◽  
Fan Zeming ◽  
Ateeq Ur Rehman ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Motor Imagery ◽  
Brain Computer Interfaces ◽  
Eeg Signals ◽  
Computer Interfaces ◽  
Empirical Wavelet Transform

Empirical Wavelet Transform Based Features for Classification of Parkinson’s Disease Severity

2017 ◽  
Vol 42 (2) ◽  
Author(s):  
Qi Wei Oung ◽  
Hariharan Muthusamy ◽  
Shafriza Nisha Basah ◽  
Hoileong Lee ◽  
Vikneswaran Vijean
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Wavelet Transform ◽  
Disease Severity ◽  
Empirical Wavelet Transform
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