Autor:

Kunik, Mateusz M. ; Gramacki, Artur

Współtwórca:

Korbicz, Józef (1951- ) - red. ; Uciński, Dariusz - red.

Tytuł:

Deep learning epileptic seizure detection based on the matching pursuit algorithm and its time-frequency graphical representation

Tytuł publikacji grupowej:

AMCS, volume 35 (2025)

Temat i słowa kluczowe:

EEG signals ; epileptic seizure detection ; matching pursuit algorithm ; time-frequency representation ; deep learning

Abstract:

Electroencephalography (EEG) is the primary diagnostic and an important prognostic clinical tool for epilepsy. The detection of epileptic activity is usually performed by a human expert and is based on finding specific patterns in the multi-channel electroencephalogram. However, the manual inspection of EEG signals is a time-consuming procedure for neurologists. Therefore, various attempts are made to automate it using both conventional and deep learning techniques. ; In this article, (i) we investigate the possibility of using time-frequency maps of energy derived from the matching pursuit algorithm for accurate detection of epileptic seizures (to the best of our knowledge, such an approach has not been analyzed so far, making this a pilot study); (ii) we show how to build an effective deep convolutional neural network with the so-called (2+1)D convolution technique; (iii) using carefully selected 79 neonatal EEG recordings, we develop a complete framework for seizure detection employing a deep learning approach, (iv) we share a ready to use R and Python codes which allow reproducing all the results presented in the paper.

Wydawca:

Zielona Góra: Uniwersytet Zielonogórski

Data wydania:

2025

Typ zasobu:

artykuł

DOI:

10.61822/amcs-2025-0044

Strony:

617-630

Źródło:

AMCS, volume 35, number 4 (2025) ; kliknij tutaj, żeby przejść

Jezyk:

eng

Licencja CC BY 4.0:

kliknij tutaj, żeby przejść

Prawa do dysponowania publikacją:

Biblioteka Uniwersytetu Zielonogórskiego