Epileptic Seizure Detection Contribution in Healthcare Sustainability

Healthcare department represented in the detection and prediction of epileptic seizures and other chronic diseases play a significant effect on the environment and make people well. Therefore, it is interesting to understand how healthcare contributes to sustainable development Epilepsy is one of the dangerous and devastating diseases that affect the human nervous system. This disease may affect anyone at any age, leading to a delayed reactivity and loss of consciousness. Epileptic seizure detection is an emerging approach in the neurological processing of brain signals. In this paper, an automated method for detection of abrupt changes of Electroencephalogram (EEG) signals is presented. The basic idea of this method depends on the utilization of Fast Walsh Hadamard Transform (FWHT). The FWHT analyzes the EEG signals in the frequency domain and decomposes it into the Hadamard coefficients. Different signal attributes are extracted from the decomposed EEG signals. These attributes comprise: Kurtosis, skewness, mean curve length, and Hjorth activity. Finally, classification is implemented using a thresholding strategy to discriminate between seizure and healthy epochs. This method is tested on long-term EEG recordings from the available Physio-Net EEG dataset. The proposed method demonstrates a high classification performance in comparison with other previous methods. An average sensitivity of 98.59%, an average specificity of 96.26% and an average accuracy of 96.26% are achieved from the mean curve length feature with FWHT.