A comprehensive comparison of handcrafted features and convolutional autoencoders for epileptic seizures detection in EEG signals

Epilepsy, a brain disease generally associated with seizures, has tremendous effects on people's quality of life. Diagnosis of epileptic seizures is commonly performed on electroencephalography (EEG) signals, and by using computer-aided diagnosis systems (CADS), neurologists can diagnose epileptic seizure stages more accurately. In these systems, a mandatory stage is feature extraction, performed by handcrafting features or learning them, ordinarily by a deep neural net. While researches in this field commonly show the value of a group of limited features, yet an accurate comparison between different suggested features is essential. In this article, first, a comparison between the importance of 50 different handcrafted features for seizure detection is presented. Additionally, the computational complexity of features is investigated as well. Then the best features based on Fisher scores are picked to classify signals on a benchmark dataset for evaluation. Additionally, a convolutional autoencoder with five layers is applied to learn features in order to have a complete comparison among feature extraction approaches. Finally, a hybrid method is employed, which combines handcrafted features and encoding of autoencoder to reach high performance in seizure detection in EEG signals.

Automated summary

This article compares the importance of 50 different handcrafted features for seizure detection and investigates the computational complexity of features. The best features based on Fisher scores are then selected to classify signals on a benchmark dataset. Additionally, a convolutional autoencoder with five layers is applied for feature learning, and a hybrid method combining handcrafted features and autoencoder encoding is employed for high performance in seizure detection in EEG signals.