Analyzing of EEG Using Discrete Wavelet Transform Method ?with (KNN & ANN) Algorithms for Detection of Epileptic ?Seizures

Abstract

An epileptic seizure is a type of seizure that occurs Because of the disruption of electrical signals in brain cells, it is a neurological ailment or problem that happens in brain cells. Epilepsy may be evaluated using electrical impulses from the brain (electroencephalogram), which are indicated by EEG, and the number of people with these disorders is roughly 1% worldwide [1]. Epilepsy can be studied using electrical impulses in the brain (electroencephalogram). Following the acquisition of EEG data, they are evaluated and divided into two categories: normal and abnormal (indicating an epileptic seizure). The EEG signals provided by the MIT BIH Dataset will be used in this work. The features will be extracted from the signals using the DWT method on the input EEG signals, and two separate algorithms (KNN and ANN) will be used to categorize the derived features into two different groups, depending on whether the input signal contains an epileptic seizure or not. Following the above method, two types of EEG are expected to be obtained using classification, either Normal (refers to normal brain activity) or Abnormal (refers to the active of brain is non-normal, maybe contain the epilepsy. The method will be evaluated using four matrices (precision, recall, and accuracy), as well as the implementation time. In this study, two methods were used: the first was DWT with KNN, and the second was DWT with ANN. Depending on the values of the three parameters and the time required for implementation. The second method proved to be superior that first method because the obtained results of second method were more accurate