Md Rashed-Al-Mahfuz1, Mohammad Ali Moni2, Shahadat Uddin3, Salem A Alyami4, Matthew A Summers5, Valsamma Eapen2. 1. Department of Computer Science and EngineeringUniversity of RajshahiRajshahi6205Bangladesh. 2. Faculty of Medicine, School of PsychiatryUniversity of New South WalesSydneyNSW2052Australia. 3. Complex Systems Research Group, Faculty of EngineeringThe University of SydneyDarlingtonNSW2008Australia. 4. Department of Mathematics and StatisticsImam Mohammad Ibn Saud Islamic UniversityRiyadh11432Saudi Arabia. 5. Garvan Institute of Medical ResearchDarlinghurstNSW2010Australia.
Abstract
BACKGROUND: Diagnosing epileptic seizures using electroencephalogram (EEG) in combination with deep learning computational methods has received much attention in recent years. However, to date, deep learning techniques in seizure detection have not been effectively harnessed due to sub-optimal classifier design and improper representation of the time-domain signal. METHODS: In this study, we focused on designing and evaluating deep convolutional neural network-based classifiers for seizure detection. Signal-to-image conversion methods are proposed to convert time-domain EEG signal to a time-frequency represented image to prepare the input data for classification. We proposed and evaluated three classification methods comprising of five classifiers to determine which is more accurate for seizure detection. Accuracy data were then compared to previous studies of the same dataset. RESULTS: We found our proposed model and signal-to-image conversion method outperformed all previous studies in the most cases. The proposed FT-VGG16 classifier achieved the highest average classification accuracy of 99.21%. In addition, the Shapley Additive exPlanations (SHAP) analysis approach was employed to uncover the feature frequencies in the EEG that contribute most to improved classification accuracy. To the best of our knowledge, this is the first study to compute the contribution of frequency components to target seizure classification; thus allowing the identification of distinct seizure-related EEG frequency components compared to normal EEG measures. CONCLUSION: Thus our developed deep convolutional neural network models are useful to detect seizures and characteristic frequencies using EEG data collected from the patients and this model could be clinically applicable for the automated seizures detection.
BACKGROUND: Diagnosing epileptic seizures using electroencephalogram (EEG) in combination with deep learning computational methods has received much attention in recent years. However, to date, deep learning techniques in seizure detection have not been effectively harnessed due to sub-optimal classifier design and improper representation of the time-domain signal. METHODS: In this study, we focused on designing and evaluating deep convolutional neural network-based classifiers for seizure detection. Signal-to-image conversion methods are proposed to convert time-domain EEG signal to a time-frequency represented image to prepare the input data for classification. We proposed and evaluated three classification methods comprising of five classifiers to determine which is more accurate for seizure detection. Accuracy data were then compared to previous studies of the same dataset. RESULTS: We found our proposed model and signal-to-image conversion method outperformed all previous studies in the most cases. The proposed FT-VGG16 classifier achieved the highest average classification accuracy of 99.21%. In addition, the Shapley Additive exPlanations (SHAP) analysis approach was employed to uncover the feature frequencies in the EEG that contribute most to improved classification accuracy. To the best of our knowledge, this is the first study to compute the contribution of frequency components to target seizure classification; thus allowing the identification of distinct seizure-related EEG frequency components compared to normal EEG measures. CONCLUSION: Thus our developed deep convolutional neural network models are useful to detect seizures and characteristic frequencies using EEG data collected from the patients and this model could be clinically applicable for the automated seizures detection.
Entities:
Keywords:
CWT; EEG; Epilepsy; STFT; deep learning; seizure
Authors: K V N Kavitha; Sharmila Ashok; Agbotiname Lucky Imoize; Stephen Ojo; K Senthamil Selvan; Tariq Ahamed Ahanger; Musah Alhassan Journal: J Healthc Eng Date: 2022-02-25 Impact factor: 2.682