Journal
ENTROPY
Volume 19, Issue 6, Pages -Publisher
MDPI
DOI: 10.3390/e19060222
Keywords
EEG; epileptic seizure detection; wavelet threshold denoising; wavelet feature extraction; nonlinear analysis; principal component analysis (PCA); analysis of variance (ANOVA)
Categories
Funding
- National Natural Science Foundation of China [61671042, 61403016]
- Beijing Natural Science Foundation [4172037]
- Fujian Provincial Key Laboratory in Minjiang University [MJUKF201702]
- Specialized Research Fund for the Doctoral Program of Higher Education [20131102120008]
- Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry
- Fundamental Research Funds for the Central Universities
Ask authors/readers for more resources
Epileptic seizure detection is commonly implemented by expert clinicians with visual observation of electroencephalography (EEG) signals, which tends to be time consuming and sensitive to bias. The epileptic detection in most previous research suffers from low power and unsuitability for processing large datasets. Therefore, a computerized epileptic seizure detection method is highly required to eradicate the aforementioned problems, expedite epilepsy research and aid medical professionals. In this work, we propose an automatic epilepsy diagnosis framework based on the combination of multi-domain feature extraction and nonlinear analysis of EEG signals. Firstly, EEG signals are pre-processed by using the wavelet threshold method to remove the artifacts. We then extract representative features in the time domain, frequency domain, time-frequency domain and nonlinear analysis features based on the information theory. These features are further extracted in five frequency sub-bands based on the clinical interest, and the dimension of the original feature space is then reduced by using both a principal component analysis and an analysis of variance. Furthermore, the optimal combination of the extracted features is identified and evaluated via different classifiers for the epileptic seizure detection of EEG signals. Finally, the performance of the proposed method is investigated by using a public EEG database at the University Hospital Bonn, Germany. Experimental results demonstrate that the proposed epileptic seizure detection method can achieve a high average accuracy of 99.25%, indicating a powerful method in the detection and classification of epileptic seizures. The proposed seizure detection scheme is thus hoped to eliminate the burden of expert clinicians when they are processing a large number of data by visual observation and to speed-up the epilepsy diagnosis.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available