Predicting Healthy and Pathological EEG Patterns with Machine Learning Algorithms /
Ghulam Abbas
- 85p. Soft Copy 30cm
Neurological disorders pose major global health challenge, affecting an estimated one billion individuals worldwide. According to the World Health Organization (WHO), these neurological disorders contribute to approximately six million deaths annually, representing a significant burden. Early and accurate identification of brain pathological features in electroencephalogram (EEG) recordings is important for the diagnosis and management of these disorders. However, manual interpretation of EEG recordings is not only time-consuming but also requires expertise. This problem is compounded by the scarcity of trained neurologists in the healthcare sector, especially in low- and middleincome countries. These limitations emphasize the necessity for automated diagnostic processes. With the advancement of machine learning algorithms, have sparked significant interest in automating the process of early diagnoses using EEGs. Therefore, this paper presents a novel deep learning model consisting of distinct path, Hybrid-CNNTransformer, for the automatic detection of abnormal raw EEG data. Through multiple ablation studies, we demonstrated the effectiveness of all parts of proposed model. The performance of our proposed model was evaluated using NMT Scalp EEG Dataset and achieved a high classification accuracy of 87.77%, which outperforms the original baseline model and other research studies. Moreover, we demonstrated the generalization of our proposed model by evaluating it on another independent dataset, TUH abnormal EEG Corpus V.2.0.0. (TUAB), without any hyperparameter tuning or adjustment. Furthermore, a Explainable AI (XAI) analysis confirmed that the model's decision-making process is not only transparent but also neurologically plausible.