ABSTRACT :
Electroencephalogram (EEG) is one of the most powerful tools that offer valuable information related to different abnormalities in the human brain. One of these abnormalities is the epileptic seizure. A framework is proposed for detecting epileptic seizures from EEG signals recorded from normal and epileptic patients. The suggested approach is designed to classify the abnormal signal from the normal one automatically. This work aims to improve the accuracy of epileptic seizure detection and reduce computational costs. To address this, the proposed framework uses the 54-DWT mother wavelets analysis of EEG signals using the Genetic algorithm (GA) in combination with other four machine learning (ML) classifiers: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), and Naive Bayes (NB). The performance of 14 different combinations of two-class epilepsy detection is investigated using these four ML classifiers. The experimental results show that the four classifiers produce comparable results for the derived statistical features from the 54-DWT mother wavelets; however, the ANN classifier achieved the best accuracy in most datasets combinations, and it outperformed the other examined classifiers.
EXISTING SYSTEM :
Many researchers have paid attention to EEG signals classification for epilepsy detection. In this section, we review a set of recent related works to epileptic seizure detection from EEG signals. The authors in [17] proposed a new approach based on the 54- DWT mother wavelets divided into seven families to divide the EEG data into different sub-bands to extract the statistical features. Then, an SVM classifier is used to categorize the EEG signals based on the extracted features. The experimental results display that the accuracy is mainly sensitive to the level of decomposition, and 40% of the redundancies were removed from the resulting features.
DISADVANTAGES OF EXISTING SYSTEM :
1) Less accuracy
2) Low Efficiency
PROPOSED SYSTEM :
The proposed method uses 54-DWT mother wavelets, Genetic algorithm, and four classifiers to classify the EEG signals for epilepsy seizure detection. Figure 1 shows the flow of the proposed methodology.
We acquire publicly accessible EEG data from Bonn University, wherein the data include five sets (A, B, C, D, and E). Each set consists of 100 single EEG segments with a sampling rate of 173.6 HZ. The EEG signals were filtered using a Bandpass filter and smoothing method. The first two sets (A, B) represent healthy people, whose signals were takenwith open and closed eyes. The other three sets represent epileptic persons. Sets (C, D) were treated as non-seizure because the signals are captured in duration without seizures. For seizure detection, set (E) was only treated as an epileptic seizure.
ADVANTAGES OF PROPOSED SYSTEM :
1) High accuracy
2) High efficiency
SYSTEM REQUIREMENTS
SOFTWARE REQUIREMENTS:
• Programming Language : Python
• Font End Technologies : TKInter/Web(HTML,CSS,JS)
• IDE : Jupyter/Spyder/VS Code
• Operating System : Windows 08/10
HARDWARE REQUIREMENTS:
Processor : Core I3
RAM Capacity : 2 GB
Hard Disk : 250 GB
Monitor : 15″ Color
Mouse : 2 or 3 Button Mouse
Key Board : Windows 08/10
