Enhanced Epilepsy Seizure Detection and Smart Phone APP for Monitoring Seizures Based on EEG Classification

Automated epilepsy seizure detection is the solution to the limitation and time consuming of manual epilepsy monitoring and detection using EEG signals. We developed a technique for epilepsy seizure detection using EEG signals. The signal will be pre-processed and filtered using multiple filters. Then, the filtered signal will be decomposed into sub-bands. Furthermore, feature extraction is applied; we developed a combined feature consists of combining three features into one. Finally, we used well-known classifiers such as Support Vector Machine (SVM), Artificial Neural Network (ANN), and K-Nears Neighbor (KNN) to differentiate between epileptic and no epileptic signals, and we achieved an accuracy of 98%. Furthermore, we developed an Android-based smartphone application for monitoring epilepsy detection based on the classification results of the EEG signal. A notification will be sent to the patient, doctors, and family members when an epilepsy seizure occurs. Once the EEG signal is classified as epileptic, the App will display a visual notification indicating that Epileptic Seizure has been detected. Moreover, it will trigger an alarm and send a message notification to all associated phone numbers. Although we are using an EEG signal from a dataset, we have generated both normal and epileptic EEG signals using a waveform generator, and we have displayed those signals on the spectrum analyzer for future real time detection using our Android App

An Efficient Automated Technique and Smartphone Application for Epilepsy Seizure Detection Using EEG signals

Epilepsy is a neurological disorder disease that affects the central nervous system of the human brain that can disrupt the activity of the nervous cells in the brain which will result in unusual behavior that can lead to loss of consciousness called epileptic seizure that can endanger the life of the patient. Therefore, automated epilepsy seizure detection is the solution to the limitation and time consuming of manual epilepsy monitoring and detection using EEG signals. Thus, using MATLAB R2014b, we developed a technique for epilepsy seizure detection using EEG signals, and we achieved an accuracy of 97%. For our main contribution, we developed an Android-based smartphone application for monitoring epilepsy detection based on the classification results of the EEG signal. A notification will be sent to the patient, doctors, and family members when an epilepsy seizure occurs. Once the EEG signal is classified as epileptic, the App will display a visual notification indicating that Epileptic Seizure has been detected. Moreover, it will trigger an alarm and send a message notification to all associated phone numbers. The main goal of our research is to develop an APP that will read the signal from the brain through a Bluetooth device, and process the signal on the APP to determine if it is normal or abnormal

Epilepsy seizure detection using EEG signals

EEG signal processing involves multiple algorithms in which epileptic data is received in the MATLAB environment and needs to be processed in order to obtain a perfectly filtered waveform and process it in both the time and frequency domain. In our work we have shown the EEG signal in the frequency domain using Fast Fourier Transform and its absolute value. Using Wavelet decomposition technique we divide the EEG signal into different sub-level bands then the lowest frequency sub-band was selected to perform feature extraction. Discrete Wavelet Transform (DWT) was applied and Vector Analysis was used for feature extraction and then we have used Inverse Discrete Fourier Transform to transform from frequency to time domain so that frequency analysis of the feature extracted EEG signal could fetch the best results. We have used the lowest frequency band possible between 1 and 3.45 Hz which could be the smallest possible in order to either classify a signal or to apply threshold and compare the results. In order to verify our work, we are comparing our results with some of the mostly used classifiers results even though classifiers do not show frequency analysis

Detection of Epileptic Seizure Using EEG Sensor

The epileptic seizure is a disease of central nervous system. Its detection by the physical analysis of the person?s body is very difficult. So, the appropriate detection of the seizure is very crucial in diagnosis of the person with seizure. The person with epileptic seizure which affects the brain signal can be detected by analyzing the brain signals using EEG sensor. The electroencephalogram (EEG) signal is very essential in the diagnosis of epilepsy. Long-term EEG recordings of an epileptic patient contain a huge amount of EEG data. The detection of epileptic activity is, therefore, a very demanding process that needs a detailed analysis of the entire length of the EEG data, usually performed by an expert. This paper describes an automated classification of EEG signals for detecting epileptic seizures using wavelet transform and statistical analysis. The decision making process is comprised of three different stages: (1) filtering of EEG signals given as input (2) feature extraction based on wavelet transform, and (3) classification by SVM classifier. The signal from brain given as an input to EEG sensor is analyzed using MATLAB by signal processing technique

Localization of the epileptogenic foci using Support Vector Machine

Epileptic foci localization is a crucial step in planning surgical treatment of medically intractable epilepsy. The solution to this problem can be determined by the detection of the earliest time of seizure onset in electroencephalographic (EEG) recordings. This study presents the application of support vector machine (SVM) for localization of the focus region at the epileptic seizure on the basis of EEG signals. We used intracranial EEG recordings from patients suffering from pharmacoresistant focal-onset epilepsy. We have been investigating a localization of the focus region at the epileptic seizure based on SVM to detect the onset of seizure activity in EEG data. The SVM is trained on sets of intracranial EEG recordings from patients suffering from pharmacoresistant focal-onset epilepsy. The performance of SVM is measured by using accuracy obtained from a fit between the target value and network output. Our EEG based localization of the focus region at the epileptic seizure approach achieves 97.4% accuracy with using 10-fold cross validation. Therefore, our method can be successfully applied to localization of the epileptogenic foci

Epileptic seizure detection from EEG signals using logistic model trees

Reliable analysis of electroencephalogram (EEG) signals is crucial that could lead the way to correct diagnostic and therapeutic methods for the treatment of patients with neurological abnormalities, especially epilepsy. This paper presents a novel analysis system for detecting epileptic seizure from EEG signals, which uses statistical features based on optimum allocation technique (OAT) with logistic model trees (LMT). The analysis involves applying the OAT to select representative EEG signals that reflect the entire database. Then, some statistical features are extracted from these EEG signals and the obtained feature set is fed into the LMT classification model to detect epileptic seizure. To test the consistency of the proposed method, all experiments are carried out on a benchmark EEG dataset and repeated twenty times with the same parameters in the detection process, and the average values of the performance parameters are reported. The results show very high detection performances for each class, and also confirm the consistency of the proposed method in the repeating process. The proposed method outperforms some state-of-the-art methods of epileptic EEG signal detection using the same EEG dataset

Seizure detection using EEG and ECG signals for computer-based monitoring, analysis and management of epileptic patients

This is the accepted manuscript version of the following article: Iosif Mporas, “Seizure detection using EEG and ECG signals for computer-based monitoring, analysis and management of epileptic patients”, Expert Systems with Applications, Vol. 42(6), December 2014. The final published version is available at: http://www.sciencedirect.com/science/article/pii/S0957417414007763?via%3Dihub © 2014 Elsevier Ltd. All rights reserved.In this paper a seizure detector using EEG and ECG signals, as a module of a healthcare system, is presented. Specifically, the module is based on short-time analysis with time-domain and frequency-domain features and classification using support vector machines. The seizure detection module was evaluated on three subjects with diagnosed idiopathic generalized epilepsy manifested with absences. The achieved seizure detection accuracy was approximately 90% for all evaluated subjects. Feature ranking investigation and evaluation of the seizure detection module using subsets of features showed that the feature vector composed of approximately the 65%-best ranked parameters provides a good trade-off between computational demands and accuracy. This configurable architecture allows the seizure detection module to operate as part of a healthcare system in offline mode as well as in online mode, where real-time performance is needed.Peer reviewe

An Automated System for Epilepsy Detection using EEG Brain Signals based on Deep Learning Approach

Epilepsy is a neurological disorder and for its detection, encephalography (EEG) is a commonly used clinical approach. Manual inspection of EEG brain signals is a time-consuming and laborious process, which puts heavy burden on neurologists and affects their performance. Several automatic techniques have been proposed using traditional approaches to assist neurologists in detecting binary epilepsy scenarios e.g. seizure vs. non-seizure or normal vs. ictal. These methods do not perform well when classifying ternary case e.g. ictal vs. normal vs. inter-ictal; the maximum accuracy for this case by the state-of-the-art-methods is 97+-1%. To overcome this problem, we propose a system based on deep learning, which is an ensemble of pyramidal one-dimensional convolutional neural network (P-1D-CNN) models. In a CNN model, the bottleneck is the large number of learnable parameters. P-1D-CNN works on the concept of refinement approach and it results in 60% fewer parameters compared to traditional CNN models. Further to overcome the limitations of small amount of data, we proposed augmentation schemes for learning P-1D-CNN model. In almost all the cases concerning epilepsy detection, the proposed system gives an accuracy of 99.1+-0.9% on the University of Bonn dataset.Comment: 18 page

A Hidden Markov Factor Analysis Framework for Seizure Detection in Epilepsy Patients

Approximately 1% of the world population suffers from epilepsy. Continuous long-term electroencephalographic (EEG) monitoring is the gold-standard for recording epileptic seizures and assisting in the diagnosis and treatment of patients with epilepsy. Detection of seizure from the recorded EEG is a laborious, time consuming and expensive task. In this study, we propose an automated seizure detection framework to assist electroencephalographers and physicians with identification of seizures in recorded EEG signals. In addition, an automated seizure detection algorithm can be used for treatment through automatic intervention during the seizure activity and on time triggering of the injection of a radiotracer to localize the seizure activity. In this study, we developed and tested a hidden Markov factor analysis (HMFA) framework for automated seizure detection based on different features such as total effective inflow which is calculated based on connectivity measures between different sites of the brain. The algorithm was tested on long-term (2.4-7.66 days) continuous sEEG recordings from three patients and a total of 16 seizures, producing a mean sensitivity of 96.3% across all seizures, a mean specificity of 3.47 false positives per hour, and a mean latency of 3.7 seconds form the actual seizure onset. The latency was negative for a few of the seizures which implies the proposed method detects the seizure prior to its onset. This is an indication that with some extension the proposed method is capable of seizure prediction