Year-end Sale % OFF 
Abstract
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.
Highlights
We discuss the wavelet power spectrum based on the discrete wavelet transform (DWT) and explain its superiority compared to continuous wavelet transform (CWT)
Our study shows that DWT is a better choice for feature extraction using functional principal component analysis (PCA)
EEG signals to wavelet power spectra enhances the functionality of input signals so that functional PCA becomes useful in feature extraction
Summary
In [6], EEG signals are first decomposed using DWT, extracted wavelet features were used as an input to a mixture expert network for classification. In [7], a robust feature extraction method based on PCA was designed to classify multi-class EEG signals. In [9], a novel patient-specific seizure detection approach using wavelet decomposition of multi-channel EEG recordings was proposed, and the features extracted from different frequency bands were used to classify the seizure and non-seizure signals. In [10], an empirical mode decomposition (EMD)-based dictionary approach was proposed for epilepsy seizure detection, and the high accuracy of the obtained results suggests that the proposed method may be promising for classifying long-term multi-channel
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
