Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций
Конференция: 13th IC-MSQUARE; Kalamata, Greece; Kalamata, Greece
Год издания: 2026
Идентификатор DOI: 10.1007/978-3-032-00914-2_48
Ключевые слова: recurrent neural network, multi-head attention, transformer, long short-term memory, lstm, epilepsy seizure detection, electroencephalogram, eeg, hybrid model
Аннотация: Epilepsy is a long-term neurological condition marked by uncontrollable seizure activity. Electroencephalography (EEG) is widely used to diagnose this disease. The creation of algorithms for automated processing and analysis of EEG data represents a crucial research domain within the fields of machine learning and artificial intellПоказать полностьюigence. In this study, we present a hybrid binary classification model that combines a Long Short-Term Memory (LSTM) network with a transformer for the automatic detection of epilepsy-related abnormalities. A hybrid approach, in which several heterogeneous models are combined into one, is a promising approach in the realm of contemporary machine learning, since it allows one model to take advantage of different algorithms. Nonlinear characteristics of the electroencephalogram signal, such as the Hurst exponent, Higuchi fractal dimension, detrended fluctuation analysis (DFA), sample entropy, the highest Lyapunov exponent, correlation dimension, autocorrelation, statistical characteristics of the signal, along with frequency characteristics obtained using the fast Fourier transform in five frequency ranges, most often used in EEG analysis, were employed as informative features for the suggested classification model. In the conducted computational experiments, the proposed model achieved high performance indicators on the test sample: 94.6% sensitivity, 99.8% specificity, 0.971 F1-score and 0.972 AUC.
Журнал: Mathematical modeling in physical sciences
Номера страниц: 717-728
Место издания: Kalamata, Greece