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Article

Wavelet Power Spectral Domain Functional Principal Component Analysis for Feature Extraction of Epileptic EEGs

Global Management Studies, Ted Rogers School of Management, Ryerson University, Toronto, ON M5B 2K3, Canada
Academic Editor: Frank Emmert-Streib
Received: 30 May 2021 / Revised: 3 July 2021 / Accepted: 4 July 2021 / Published: 7 July 2021
Feature extraction plays an important role in machine learning for signal processing, particularly for low-dimensional data visualization and predictive analytics. Data from real-world complex systems are often high-dimensional, multi-scale, and non-stationary. Extracting key features of this type of data is challenging. This work proposes a novel approach to analyze Epileptic EEG signals using both wavelet power spectra and functional principal component analysis. We focus on how the feature extraction method can help improve the separation of signals in a low-dimensional feature subspace. By transforming EEG signals into wavelet power spectra, the functionality of signals is significantly enhanced. Furthermore, the power spectra transformation makes functional principal component analysis suitable for extracting key signal features. Therefore, we refer to this approach as a double feature extraction method since both wavelet transform and functional PCA are feature extractors. To demonstrate the applicability of the proposed method, we have tested it using a set of publicly available epileptic EEGs and patient-specific, multi-channel EEG signals, for both ictal signals and pre-ictal signals. The obtained results demonstrate that combining wavelet power spectra and functional principal component analysis is promising for feature extraction of epileptic EEGs. Therefore, they can be useful in computer-based medical systems for epilepsy diagnosis and epileptic seizure detection problems. View Full-Text
Keywords: wavelet power spectra; feature extraction; functional PCA; medical informatics; data visualization wavelet power spectra; feature extraction; functional PCA; medical informatics; data visualization
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MDPI and ACS Style

Xie, S. Wavelet Power Spectral Domain Functional Principal Component Analysis for Feature Extraction of Epileptic EEGs. Computation 2021, 9, 78. https://0-doi-org.brum.beds.ac.uk/10.3390/computation9070078

AMA Style

Xie S. Wavelet Power Spectral Domain Functional Principal Component Analysis for Feature Extraction of Epileptic EEGs. Computation. 2021; 9(7):78. https://0-doi-org.brum.beds.ac.uk/10.3390/computation9070078

Chicago/Turabian Style

Xie, Shengkun. 2021. "Wavelet Power Spectral Domain Functional Principal Component Analysis for Feature Extraction of Epileptic EEGs" Computation 9, no. 7: 78. https://0-doi-org.brum.beds.ac.uk/10.3390/computation9070078

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