Efficient communication and EEG signal classification in wavelet domain for epilepsy patients

In this paper, we present an approach for the anticipation of electroencephalography (EEG) seizures using different families of wavelet transform. Different signal attributes are investigated to anticipate the seizure onset based on the wavelet transform. These attributes comprise amplitude, local mean, local median, local variance, derivative, and entropy of the wavelet-transformed signals. Different wavelet families are considered including Haar, Daubechies (db4, and db8), Symlets (Sym4), and Coiflets (Coif4) wavelets. The seizure prediction process is intended to be simple to be applied on a mobile application accompanying the patient to give him alerts of possible incoming seizures. The proposed approach is performed on long-term EEG recordings from the available CHB-MIT scalp dataset. It gives the best results in comparison with the other previous algorithms. It achieves a high sensitivity of 100% with Daubechies wavelet transform (db4) in addition to a low average False Prediction Rate (FPR) of 0.0818 h−1 and a high average Prediction Time (PT) of 38.1676 min. Therefore, it can help specialists for the prediction of epileptic seizures as early as possible.