Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций
Конференция: Hybrid Methods of Modeling and Optimization in Complex Systems (HMMOCS-III 2024); Krasnoyarsk; Krasnoyarsk
Год издания: 2025
Идентификатор DOI: 10.1051/itmconf/20257204008
Аннотация: This paper presents a method for detecting epileptic seizures based on electroencephalogram (EEG) analysis using a deep learning model based on Bidirectional Long Short-Term Memory (BiLSTM). The proposed model architecture allows taking into account temporal dependencies and nonlinear dynamics of EEG signals, which makes it effectiПоказать полностьюve for recognizing patterns associated with epileptic seizures. The model uses frequency, dynamic, fractal, correlation and statistical characteristics of the EEG signal as informative features. The study includes the stages of data preprocessing, feature extraction and neural network training. To improve the accuracy of the model, data normalization and regularization methods were used. The experimental results obtained on the publicly available TUH EEG dataset demonstrate high performance of the model in detecting epileptic activity: Sensitivity 96.2, Specificity 99.8, F1-score 0.77, AUC 0.98.
Журнал: ITM Web of Conferences
Номера страниц: 4008
Место издания: Krasnoyarsk