Intelligent Methodology Using Machine Learning for Epileptic Seizure Identification Based on Time-Frequency Analysis of EEG Signals

Autores

  • Diego Dutra Sampaio UFMA
  • Priscila Lima Rocha Instituto Federal de Educação, Ciência e Tecnologia do Maranhão
  • Washigton Luis Santos Silva Instituto Federal de Educação, Ciência e Tecnologia do Maranhão
  • Allan Kardec Duailibe Barros Filho Universidade Federal do Maranhão

DOI:

https://doi.org/10.55592/cilamce.v6i06.8164

Palavras-chave:

Epileptic Seizures, Time-Frequency Feature Extraction, Convolutional Neural Network (CNN)

Resumo

The diagnosis of epilepsy is conducted through visual inspection of electroencephalogram (EEG) signal recordings. However, due to the variations in convulsive disorders, it can be challenging for clinicians to constantly monitor the patient for seizure type, especially because EEG records contain hours of signal. Nevertheless, these patterns present in EEG signals can also be identified through signal classification methods based on signal processing and machine learning approaches. In light of this, this study proposes the development of a methodology for epileptic seizure type classification based on analysis of time-frequency characteristics of EEG signals, using Continuous Wavelet Transform (CWT) and joint moments of time-frequency distribution. Epileptic seizure classification was performed using a convolutional neural network (CNN), employing k-fold cross-validation methods. Accuracy, sensitivity, specificity, and area under the curve (AUC) metrics were obtained to validate this algorithm. The achieved results for the CNN classifier were 96.54% accuracy, 96.54% sensitivity, 96.54% specificity, and AUC = 0.90%.

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Publicado

2024-12-02

Edição

Seção

Applications of Biomechanics and Biochemistry in Computational and Experimental Engineering