부산대학교 도서관

In this work, we investigate epilepsy seizure detection using machine learning. In the literature, a machine learning model is usually trained to help automate the epileptic detection process, eliminating the need for human intervention. Typically, the dataset is split into training and test sets in a way to maximize the detection accuracy. This requires the training set to include enough EEG samples for every possible patient in order to improve the accuracy numbers during the prediction. However, this might not be easy or practical in real life. A new patient might not have a previous record in the training set, and hence, the prediction for this particular patient might not meet the expected accuracy. The main contribution in this work is to study the impact of the training and test datasets selection from practical point of view on the accuracy and efficacy of the CNN prediction. In this work, a CNN model, namely AlexNet, is trained to detect epileptic states, namely preictal, interictal and ictal, in subjects using electroencephalogram (EEG) signals. The dataset includes the three epileptic zones of subjects taken from three medical centers, collected by the Fragility Multi-Center Retrospective Study. Furthermore, we propose a framework to utilize a feature extraction technique that exploits the available multiple channels of EEG signals to minimize information loss. As part of the main contribution, three different approaches are proposed to split the EEG sample dataset into the training and test sets. Thus, the prediction performance is evaluated based on the prior knowledge extracted from the particular samples picked for the training set. The results show that the proposed framework achieves an overall accuracy of 94.44% when the training contained samples from each patient. The accuracy is reduced to 92.98% when the training set contained a subset of the patient pool. A binary classification is also performed with up to 98% accuracy for both scenarios. [ABSTRACT FROM AUTHOR]