Classification of EEG Signals for Prediction of Epileptic Seizures

• Published in: Applied sciences 2022-07, Vol.12 (14), p.7251
• Main Authors: Aslam, Muhammad Haseeb, Usman, Syed Muhammad, Khalid, Shehzad, Anwar, Aamir, Alroobaea, Roobaea, Hussain, Saddam, Almotiri, Jasem, Ullah, Syed Sajid, Yasin, Amanullah
• Format: Article
• Language: English
• Subjects: Accident prediction; Automation; Classification; Convulsions & seizures; Datasets; Decision trees; Deep learning; EEG; Electrodes; electroencephalogram; Electroencephalography; Epilepsy; epilepsy prediction; Fourier transforms; Machine learning; Methods; Neural networks; Patients; postictal state; preictal state; Seizures; Sensitivity; Statistical analysis; Support vector machines; Wavelet transforms
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app12147251

Epilepsy is a common brain disorder that causes patients to face multiple seizures in a single day. Around 65 million people are affected by epilepsy worldwide. Patients with focal epilepsy can be treated with surgery, whereas generalized epileptic seizures can be managed with medications. It has been noted that in more than 30% of cases, these medications fail to control epileptic seizures, resulting in accidents and limiting the patient’s life. Predicting epileptic seizures in such patients prior to the commencement of an oncoming seizure is critical so that the seizure can be treated with preventive medicines before it occurs. Electroencephalogram (EEG) signals of patients recorded to observe brain electrical activity during a seizure can be quite helpful in predicting seizures. Researchers have proposed methods that use machine and/or deep learning techniques to predict epileptic seizures using scalp EEG signals; however, prediction of seizures with increased accuracy is still a challenge. Therefore, we propose a three-step approach. It includes preprocessing of scalp EEG signals with PREP pipeline, which is a more sophisticated alternative to basic notch filtering. This method uses a regression-based technique to further enhance the SNR, with a combination of handcrafted, i.e., statistical features such as temporal mean, variance, and skewness, and automated features using CNN, followed by classification of interictal state and preictal state segments using LSTM to predict seizures. We train and validate our proposed technique on the CHB-MIT scalp EEG dataset and achieve accuracy of 94%, sensitivity of 93.8%, and 91.2% specificity. The proposed technique achieves better sensitivity and specificity than existing methods.