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A forecasting tool for prediction of epileptic seizures using a machine learning approach
Author(s) -
Asharindavida Fayas,
Shamim Hossain M.,
Thacham Azeemsha,
Khammari Hedi,
Ahmed Irfan,
Alraddady Fahad,
Masud Mehedi
Publication year - 2018
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.5111
Subject(s) - electroencephalography , hilbert–huang transform , epilepsy , matlab , epileptic seizure , pattern recognition (psychology) , spectral analysis , computer science , artificial intelligence , signal (programming language) , speech recognition , psychology , neuroscience , physics , quantum mechanics , spectroscopy , programming language , operating system , filter (signal processing) , computer vision
Summary ECG and EEG signals are very helpful in the early diagnosis of epileptic seizures. The research focuses on analysis of ECG and EEG signals applying a deep learning technique to study early prediction of epileptic seizure. Signal processing methods like Empirical Mode Decomposition, spectral analysis, and statistical methods were used. The algorithms were implemented in MATLAB, and the EEG and ECG data were collected from Physiobank and EPILEPSIAE databases. In the window‐based analysis of low‐frequency spectral area of EEG signals, 78.5% of the cases displayed a significant change as the windows progressed and the onset of seizure was approached. The spectral area of IMF components indicated a possible seizure prediction in 68.9% of the analyzed cases. Considering signals from individual EEG electrodes, the least percentage of seizure prediction was indicated by signals from T4 and F4 electrodes (52.3% and 40.7%, respectively, for spectral peaks and 23.8% and 29.6%, respectively, for spectral area). The results of regression analysis show that prediction of seizures can be possible around 20‐30 minutes prior to the actual occurrence of seizures.