Epileptic Seizure Detection on EEG Data based on CHB-MIT database using Discrete Wavelet Transform with wavelet family 'coif3', 7 level decomposition. Training is done by SVM and Random Forest.
36 Features are extracted from each subband and 23 channels. After DWT decomposition, I calculated Max, Min, Mean, Energy, Standard deviation and Skewness features for 6 subbands. 6 x 6 = 36 features are extracted for just 1 channel. Since we have 23 channel, every feature vector has 23x36 dimension.
Data source for .mat files: https://archive.physionet.org/cgi-bin/atm/ATM
RESULTS:
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Linear SVM
Sensitivity: % 92.0042643923241
Specificity: % 79.98338870431894
Positive Predictive Val: % 78.17028985507247
Negative Predictive Val: % 92.77456647398844
False Positive Rate: % 20.016611295681063
False Negative Rate: % 7.995735607675907
False Discovery Rate: % 21.829710144927535
False Omission Rate: % 7.225433526011561
Accuracy: % 85.24743230625583
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SVM with RBF (gamma=0.1)
Sensitivity: % 89.69276511397423
Specificity: % 82.4360105913504
Positive Predictive Val: % 81.97463768115942
Negative Predictive Val: % 89.98073217726397
False Positive Rate: % 17.5639894086496
False Negative Rate: % 10.307234886025768
False Discovery Rate: % 18.02536231884058
False Omission Rate: % 10.01926782273603
Accuracy: % 85.85434173669468
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Random Forest (n_estimators=20, random_state=0)
Sensitivity: % 97.73584905660377
Specificity: % 93.71534195933457
Positive Predictive Val: % 93.84057971014492
Negative Predictive Val: % 97.6878612716763
False Positive Rate: % 6.284658040665435
False Negative Rate: % 2.2641509433962264
False Discovery Rate: % 6.159420289855073
False Omission Rate: % 2.312138728323699
Accuracy: % 95.70494864612512