Detection of Epileptic Seizure Using Pretrained Deep Convolutional Neural Network and Transfer Learning

Introduction: The diagnosis of epilepsy takes a certain process, depending entirely on the attending physician. However, the human factor may cause erroneous diagnosis in the analysis of the EEG signal. In the past 2 decades, many advanced signal processing and machine learning methods have been developed for the detection of epileptic seizures. However, many of these methods require large data sets and complex operations. Methods: In this study, an end-to-end machine learning model is presented for detection of epileptic seizure using the pretrained deep two-dimensional convolutional neural network (CNN) and the concept of transfer learning. The EEG signal is converted directly into visual data with a spectrogram and used directly as input data. Results: The authors analyzed the results of the training of the proposed pretrained AlexNet CNN model. Both binary and ternary classifications were performed without any extra procedure such as feature extraction. By performing data set creation from short-term spectrogram graphic images, the authors were able to achieve 100% accuracy for binary classification for epileptic seizure detection and 100% for ternary classification. Discussion/Conclusion: The proposed automatic identification and classification model can help in the early diagnosis of epilepsy, thus providing the opportunity for effective early treatment.

Automated summary

The study utilized deep learning and transfer learning methods to detect epileptic seizures using EEG signals, achieving 100% accuracy without requiring additional feature extraction steps. This automatic identification and classification model can aid in early diagnosis of epilepsy, providing effective early treatment opportunities.