A Novel Use of Discrete Wavelet Transform Features in the Prediction of Epileptic Seizures from EEG Data
This work addresses the challenge of accurate seizure prediction for epilepsy patients, but it is incremental as it focuses on improving feature extraction techniques rather than introducing a new paradigm.
This paper tackled the problem of predicting epileptic seizures from EEG data by demonstrating that discrete wavelet transform (DWT) features, particularly when combined with SVM or RF, achieve higher classification accuracy, specificity, and sensitivity compared to other methods like MFCC, with statistically significant results in both balanced and imbalanced datasets.
This paper demonstrates the predictive superiority of discrete wavelet transform (DWT) over previously used methods of feature extraction in the diagnosis of epileptic seizures from EEG data. Classification accuracy, specificity, and sensitivity are used as evaluation metrics. We specifically show the immense potential of 2 combinations (DWT-db4 combined with SVM and DWT-db2 combined with RF) as compared to others when it comes to diagnosing epileptic seizures either in the balanced or the imbalanced dataset. The results also highlight that MFCC performs less than all the DWT used in this study and that, The mean-differences are statistically significant respectively in the imbalanced and balanced dataset. Finally, either in the balanced or the imbalanced dataset, the feature extraction techniques, the models, and the interaction between them have a statistically significant effect on the classification accuracy.