A multi-dimensional hybrid CNN-BiLSTM framework for epileptic seizure detection using electroencephalogram signal scrutiny

• Published in: Systems and soft computing 2023-12, Vol.5, p.200062, Article 200062
• Main Authors: K．R, Aravind Britto, Srinivasan, Saravanan, Mathivanan, Sandeep Kumar, Venkatesan, Muthukumaran, M．B, Benjula Anbu Malar, Mallik, Saurav, Qin, Hong
• Format: Article
• Language: English
• Subjects: Bi-long-short term memory; Electroencephalogram; Epileptic seizure detection; Multi-dimensional convolutional network; Signal scrutiny
• ISSN: 2772-9419; 2772-9419
• DOI: 10.1016/j.sasc.2023.200062

The proposed hybrid CNN-BiLSTM architecture aims to address the challenge of detecting epileptic seizures systematically from EEG signal analysis. The system consists of several stages, including preprocessing, feature extraction using multi-dimensional CNN, temporal feature processing using BiLSTM, and classification using fully connected layers. The first stage involves preprocessing and normalization of the raw EEG signal to prepare it for further analysis. This step helps in removing noise and standardizing the input for subsequent processing. Next, a multi-dimensional CNN is employed to effectively extract features from the preprocessed EEG sequence data. CNNs are known for their ability to capture spatial features, and in this case, they are utilized to extract relevant features from the EEG data. After the feature extraction stage, the BiLSTM component of the architecture is utilized to process the extracted features and capture temporal dependencies. BiLSTMs are well-suited for sequence modeling tasks and can effectively capture long-range dependencies in the data. By incorporating BiLSTM, the architecture aims to capture important temporal patterns related to epileptic seizures. Finally, the temporal feature values are fed into fully connected layers for classification. The system is designed to detect epileptic seizures and classify them into specific types using a 10-class classification approach. The proposed system reports high detection accuracy with an overall accuracy of 99.53% and an accuracy of 82.95% on the binary classification task. These results suggest that the system performs well in accurately identifying epileptic seizures from EEG signals. Furthermore, the proposed system demonstrates superior performance compared to other existing techniques such as K-nearest neighbor (KNN), support vector machine (SVM), and decision tree (DT) in terms of accuracy. It is important to note that the evaluation and comparison of the proposed system were performed on a publicly available epileptic seizures image database. Overall, the proposed hybrid CNN-BiLSTM architecture shows potential in enhancing the detection and classification of epileptic seizures from EEG signals, potentially improving the efficiency and accuracy of diagnosis and treatment in the early stages of these disorders.