الفهرس | Only 14 pages are availabe for public view |
Abstract Epilepsy is a central nervous system (neurological) disorder in which brain activity becomes abnormal, causing seizures or periods of unusual behavior, sensations, and sometimes loss of awareness. Epilepsy affects both males and females of all ages. Therefore, it is essential to notify the patient’s medication-resistant epileptic seizure to the caretaker and analyze the pattern of related signals before, during, and after the seizure onset. In the medical diagnosis of epileptic seizures, classification is a significant step that directly affects the results. However, the visual examination of the Electroencephalogram (EEG) is a comparatively common analytic procedure of epilepsy, although it is costly, time-consuming, and relies on the experiences of the doctor. In this thesis, we provide and propose an efficient and accurate epileptic seizure automatic diagnosis system suitable for clinical epileptic seizure diagnosis as an urgent task in recent times. Firstly, we propose and evaluate the performance of KNN classifier in classifying the epilepsy of epileptic patients from EEG signals. The preprocessing is performed to overcome the problem of imbalanced data using different sampling techniques. Finally, an optimization is performed by applying KNN classifier to obtain the optimal value for k on epileptic seizure recognition dataset. The result demonstrated that the optimized KNN with cuckoosearch and SMOTE resampling techniques give 98.5%, 99%, 99% for accuracy, precision, and recall respectively. Secondly, we propose a system that can select a channel by calculating the variance parameter for each channel. The highest three channels of variance will be selected. Then extracted the features that fed to the machine learning for classification in an efficient manner. In this model, ensemble technique achievied a sensitivity of 100%. Thirdly, an efficient framework is proposed by converting EEG signals into spectrogram images. Anew dataset was created by extracting the most important features of the EEG signals that detect spikes and after that converting it to spectrogram images. These images are fed to the pre-trained model to be classified as normal or seizure. The proposed system is evaluated through different experiment circumstances over the CHB-MIT dataset. For Tegear method, the highest accuracy of 93.06% was achieved using adam Abstract vi solver using squeezenet model. Also, this hybrid system provides a new approach for Early Epilepsy in Medical Internet of Things applications. Further, the results indicate the proposed system is suitable for IoT-based real- time seizure recognition from EEG recording along with providing an automated biomarker for normal and epileptic EEG signals in smart healthcare systems in the context of the smart city. Recently, data privacy is a major concern when accessing and processing sensitive medical data. Therefore, fourthly, an enhancement of the last proposed system by adding security using a combination of CNN and encrypted spectrogram images. An encrypted Spectrogram and CNN-based method are planned for the detection of Seizures from EEG signals. The accuracy of the suggested structure has been estimated up to 86.11 % and 84.72 % using googlenet with Arnold and chaotic methods respectively. The suggested system’s accuracy demonstrates that this system can be one effective system for seizure detection. |