scholarly journals Electroencephalogram Signal Processing with Independent Component Analysis and Cognitive Stress Classification Using Convolutional Neural Networks

2022 ◽  
pp. 275-292
Author(s):  
Venkatakrishnan Sutharsan ◽  
Alagappan Swaminathan ◽  
Saisrinivasan Ramachandran ◽  
Madan Kumar Lakshmanan ◽  
Balaji Mahadevan
Keyword(s):  
Neural Networks ◽  
Signal Processing ◽  
Independent Component Analysis ◽  
Convolutional Neural Networks ◽  
Component Analysis ◽  
Independent Component ◽  
Cognitive Stress ◽  
Stress Classification

scholarly journals Novel feature extraction method for signal analysis based on independent component analysis and wavelet transform

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260764
Author(s):  
Mariusz Topolski ◽  
Jędrzej Kozal
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Wavelet Transform ◽  
Independent Component Analysis ◽  
Convolutional Neural Networks ◽  
Extraction Method ◽  
Component Analysis ◽  
Independent Component ◽  
Feature Extraction Method ◽  
Ectopic Beats

Feature extraction is an important part of data processing that provides a basis for more complicated tasks such as classification or clustering. Recently many approaches for signal feature extraction were created. However, plenty of proposed methods are based on convolutional neural networks. This class of models requires a high amount of computational power to train and deploy and large dataset. Our work introduces a novel feature extraction method that uses wavelet transform to provide additional information in the Independent Component Analysis mixing matrix. The goal of our work is to combine good performance with a low inference cost. We used the task of Electrocardiography (ECG) heartbeat classification to evaluate the usefulness of the proposed approach. Experiments were carried out with an MIT-BIH database with four target classes (Normal, Vestibular ectopic beats, Ventricular ectopic beats, and Fusion strikes). Several base wavelet functions with different classifiers were used in experiments. Best was selected with 5-fold cross-validation and Wilcoxon test with significance level 0.05. With the proposed method for feature extraction and multi-layer perceptron classifier, we obtained 95.81% BAC-score. Compared to other literature methods, our approach was better than most feature extraction methods except for convolutional neural networks. Further analysis indicates that our method performance is close to convolutional neural networks for classes with a limited number of learning examples. We also analyze the number of required operations at test time and argue that our method enables easy deployment in environments with limited computing power.

Exploring Independent Component Analysis for GPR Signal Processing

PIERS Online ◽  
2005 ◽  
Vol 1 (6) ◽  
pp. 750-753 ◽  
Author(s):  
Anxing Zhao ◽  
Yansheng Jiang ◽  
Wenbing Wang
Keyword(s):  
Signal Processing ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component

An Approach on MCSA-Based Fault Detection Using Independent Component Analysis and Neural Networks

2019 ◽  
Vol 68 (5) ◽  
pp. 1353-1361 ◽  
Author(s):  
Juan Enrique Garcia-Bracamonte ◽  
Juan Manuel Ramirez-Cortes ◽  
Jose de Jesus Rangel-Magdaleno ◽  
Pilar Gomez-Gil ◽  
Hayde Peregrina-Barreto ◽  
...  
Keyword(s):  
Neural Networks ◽  
Fault Detection ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component
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