Abstract
Epilepsy is a chronic noncommunicable brain disease. Manual inspection of long-term Electroencephalogram (EEG) records for detecting epileptic seizures or other diseases that lasted several days or weeks is a time-consuming task. Therefore, this research proposes a novel epileptic seizure classification architecture called the Deep Batch Normalization Neural Network (Deep BN3), a BN3 architecture with a deeper layer to classify big epileptic seizure data accurately. The raw EEG signals are first to cut into pieces and passed through the bandpass filter. The dataset is very imbalanced, so an undersampling technique was used to produce a balanced sample of data for the training and testing dataset. Furthermore, the balanced data is used to train the Deep BN3 architecture. The resulting model classifies the EEG signal as an epileptic seizure or non-seizure. The classification of epileptic seizures using Deep BN3 obtained pretty good results compared to other architectures used in this research, with an accuracy of 53.61%.
Highlights
Epilepsy is a chronic noncommunicable brain disease
The classification of epileptic seizures using Deep BN3 obtained pretty good results compared to other architectures used in this research, with an accuracy of 53.61%
The Deep BN3 architecture is a BN3 architecture with a deeper layer inspired by deep CNN architecture to classify big epileptic seizures data accurately
Summary
Epilepsy is a chronic noncommunicable brain disease. The number of people who have epilepsy worldwide is approximately 50 million. Recent research by Tjandrasa et al classified the EEG signals using a combination of intrinsic mode function, and power spectrum feature extractor gave a maximum of 78.6% accuracy for five classes [3]. Tjandrasa et al classified EEG signals using single channel-independent component analysis, power spectrum, and linear discriminant analysis They obtained a maximum accuracy of 94% for three classes [4]. Since epilepsy EEG data is a big dataset, a deeper architecture may be better suited to classify big data. This research proposes a novel epileptic seizure classification architecture called the Deep Batch Normalization Neural Network (Deep BN3). The Deep BN3 architecture is a BN3 architecture with a deeper layer inspired by deep CNN architecture to classify big epileptic seizures data accurately. Deep BN3 will be concluded as a good architecture if it can compete with another architecture
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