[Detection of epileptic spike wave in EEG signals based on morphological component analysis].
This paper proposed a morphological component analysis (MCA) method, which is based on sparse representation, to detect the spike wave in electroencephalogram (EEG) signals. It takes the advantage of MCA being able to extract the background waves and the spike waves from the EEG signals, respectively,as the dictionaries and chooses the discrete cosine transform (DCT) and the daubechies order 4 wavelet (db4) transformation as the dictionaries of MCA to detect the spike waves from the epileptic EEG. The experiment results showed that the MCA could detect epileptic spike waves in EEG signals very effectively, and it yielded high selectivity of 89.01% and sensitivity of 90.71%. As a feature extraction/decomposition algorithm, MCA can be used to extract the spike waves from EEG signals.
College of Biomedical Engineering, Tianjin Medical University, Tianjin 300070, China.
Donghua
D
Ma
Xuyuan
X
Zheng
Zhen
Z
Wang
D000465:Algorithms; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000145:classification* / Q000175:diagnosis* / Q000503:physiopathology; D006801:Humans; D025341:Principal Component Analysis / Q000379:methods; D012815:Signal Processing, Computer-Assisted; D058067:Wavelet Analysis