Automatic Identification of Interictal Epileptiform Discharges in Secondary Generalized Epilepsy

Ictal epileptiform discharges (EDs) are characteristic signal patterns of scalp electroencephalogram (EEG) or intracranial EEG (iEEG) recorded from patients with epilepsy, which assist with the diagnosis and characterization of various types of epilepsy. The EEG signal, however, is often recorded from patients with epilepsy for a long period of time, and thus detection and identification of EDs have been a burden on medical doctors. This paper proposes a new method for automatic identification of two types of EDs, repeated sharp-waves (sharps), and runs of sharp-and-slow-waves (SSWs), which helps to pinpoint epileptogenic foci in secondary generalized epilepsy such as Lennox-Gastaut syndrome (LGS). In the experiments with iEEG data acquired from a patient with LGS, our proposed method detected EDs with an accuracy of 93.76% and classified three different signal patterns with a mean classification accuracy of 87.69%, which was significantly higher than that of a conventional wavelet-based method. Our study shows that it is possible to successfully detect and discriminate sharps and SSWs from background EEG activity using our proposed method.ope

Feature selection and classifier parameters estimation for EEG signals peak detection using particle swarm optimization

Electroencephalogram (EEG) signal peak detection is widely used in clinical applications. The peak point can be detected using several approaches, including time, frequency, time-frequency, and nonlinear domains depending on various peak features from several models. However, there is no study that provides the importance of every peak feature in contributing to a good and generalized model. In this study, feature selection and classifier parameters estimation based on particle swarm optimization (PSO) are proposed as a framework for peak detection on EEG signals in time domain analysis. Two versions of PSO are used in the study: (1) standard PSO and (2) random asynchronous particle swarm optimization (RA-PSO). The proposed framework tries to find the best combination of all the available features that offers good peak detection and a high classification rate from the results in the conducted experiments. The evaluation results indicate that the accuracy of the peak detection can be improved up to 99.90% and 98.59% for training and testing, respectively, as compared to the framework without feature selection adaptation. Additionally, the proposed framework based on RA-PSO offers a better and reliable classification rate as compared to standard PSO as it produces low variance model

Studying the Use of Hidden Markov Models in the Detection and Classification of EEG Epileptiform Transients using LPC features

The process of identifying the presence of an AEP (Abnormal Epileptiform Paroxysmal) in a subject\u27s EEG, normally done by neurologist experts, is a particularly long one and involves considerable financial expenses. This research aims to pave an automatic method of detecting and classifying streams of EEGs as to whether or not it has any AEPs present in it. This is a two step process, where step 1 is the classification problem and step 2 is the detection problem. There are many different activities on the EEGs, and the classification task helps to identify which of these activities are AEPs. So, this task involves training 2 HMMs to classify all given artifacts into 2 classes, AEP or NonAEP. LPC features extracted from the spike have been used to train the HMMs. The detection task is to find out the presence of ETs (Epileptiform Transients) from a patient\u27s EEG. For detection, two HMMs have been trained on examples taken from two classes, the ETs and the Non-ETs. The ETs class is all the Yellow Boxed annotations provided to us by the experts. The Non-ET class data has been formed by taking into consideration all the data which has not been marked as an ET. In this task, LPC features extracted from the spike and the contextual information has seen to provide good results. For validation of the system, a cascaded structure of four HMMs is formed. The first two HMMs are for detection and the next two classify the detected ETs. Test EEG signals, having both AEPs and NonAEPs are passed through this system, and the AEPs are marked and identied. The results have been compared to the annotations marked by experts

Automatic detection of epileptiform events in EEG by a three-stage procedure based on artificial neural networks

PubMed ID: 15651562This paper introduces a three-stage procedure based on artificial neural networks for the automatic detection of epileptiform events (EVs) in a multichannel electroencephalogram (EEG) signal. In the first stage, two discrete perceptrons fed by six features are used to classify EEG peaks into three subgroups: 1) definite epileptiform transients (ETs); 2) definite non-ETs; and 3) possible ETs and possible non-ETs. The pre-classification done in the first stage not only reduces the computation time but also increases the overall detection performance off the procedure. In the second stage, the peaks falling into the third group are aimed to be separated from each other by a nonlinear artificial neural network that would function as a postclassifier whose input is a vector of 41 consecutive sample values obtained from each peak. Different networks, i.e., a backpropagation multilayer perceptron and two radial basis function networks trained by a hybrid method and a support vector method, respectively, are constructed as the postclassifier and then compared in terms of their classification performances. In the third stage, multichannel information is integrated into the system for contributing to the process of identifying an EV by the electroencephalographers (EEGers). After the integration of multichannel information, the overall performance of the system is determined with respect to EVs. Visual evaluation, by two EEGers, of 19 channel EEG records of 10 epileptic patients showed that the best performance is obtained with a radial basis support vector machine providing an average sensitivity of 89.1%, an average selectivity of 85.9%, and a false detection rate (per hour) of 7.5.Manuscript received December 18, 2002; revised May 13, 2004. The work of N. Acır was supported in part by the Turkish Scientific and Technical Research Council (TÜBİTAK) through the Münir Birsel Fund. Asterisk indicates corresponding author. *N. Acır is with Neuro-Sensory Engineering Laboratory, University of Miami, Miami, FL 33124 USA, on leave from the Dokuz Eylül University, Electrical and Electronics Engineering Department, 35160, Buca, 'zmir, Turkey. (e-mail: [email protected]). '. Öztura and B. Baklan are with Dokuz Eylül University, Medical Faculty, Neurology Department, 'zmir, Turkey. -

Automatic detection of epileptiform events in EEG by a three-stage procedure based on artificial neural networks

This paper introduces a three-stage procedure based on artificial neural networks for the automatic detection of epileptiform events (EVs) in a multichannel electroencephalogram (EEG) signal. In the first stage, two discrete perceptrons fed by six features are used to classify EEG peaks into three subgroups: 1) definite epileptiform transients (ETs); 2) definite non-ETs; and 3) possible ETs and possible non-ETs. The pre-classification done in the first stage not only reduces the computation time but also increases the overall detection performance of the procedure. In the second stage, the peaks failing into the third group are aimed to be separated from each other by a nonlinear artificial neural network that would function as a postclassifier whose input is a vector of 41 consecutive sample values obtained from each peak. Different networks, i.e., a backpropagation multilayer perceptron and two radial basis function networks trained by a hybrid method and a support vector method, respectively, are constructed as the postclassifier and then compared in terms of their classification performances. In the third stage, multichannel information is integrated into the system for contributing to the process of identifying an EV by the electroencephalographers (EEGers). After the integration of multichannel information, the overall performance of the system is determined with respect to EVs. Visual evaluation, by two EEGers, of 19 channel EEG records of 10 epileptic patients showed that the best performance is obtained with a radial basis support vector machine providing an average sensitivity of 89.1%, an average selectivity of 85.9%, and a false detection rate (per hour) of 7.5

Analisis gelombang otak low alpha high alpha low beta high beta dan theta menggunakan alat Neurosky Mindwave Mobile Headset untuk mengidentifikasi kelelahan supervisor pabrik menggunakan metode Means Comparison Test (MCT)

Brain-Computer Interface (BCI) adalah jalur komunikasi langsung antara otak dan perangkat eksternal (Tan dan Nijholt, 2010). Dengan kata lain, BCI adalah bidang kajian untuk memanfaatkan gelombang listrik yang dipancarkan oleh otak dan ditangkap oleh sensor seperti Electroencephalography (EEG), ke dalam aplikasi komputer. Kelelahan merupakan suatu keadaan yang sulit dipisahkan dalam kehidupan manusia, sehingga sedapat mungkin kelelahan tersebut dihindarkan ketika seseorang hendak melakukan pekerjaanya agar performa kerja yang diharapkan dapat terpenuhi. Dengan mendeteksi secara dini kelelahan tersebut maka setidaknya kelelahan dapat dihindarkan, salah satunya kelelahan mental. Belum adanya penelitian ilmiah yang menyebutkan bahwa kelelahan mental dapat diketahui melalui gelombang otak menjadikan penelitian ini sebagai penelitian dasar untuk mendeteksi kelelahan. Penelitian dilakukan dengan menganalisis dan mengidentifikasi gelombang otak low alpha, high alpha, low beta, high beta dan theta dari supervisor pabrik. Keluaran dari penelitian ini didapati standar kelelahan dari seorang supervisor pabrik. =============================================================================================== Brain Computer Interface (BCI) is a direct communication route between the brain and an external device (Tan and Nijholt, 2010). in other words, BCI is a subject which analyze the waves effused by the brain and captured by sensor such as Electroencephalography (EEG) into a computer software. fatigue is an inseperable part of human life so that it is important to be avoided in order to go for the desired work performance. By early detection, it is expexted that fatigue , especially mental fatigue, can be avoided. the fact that it is not scientifically proven yet that mental fatigue can be detected by the wave effused by the brain make this research the base of fatigue detector development. this reserach is done by analyzing and identifying low alpha, high alpha, low beta, high beta and tetha brainwave of a manufacturing supervisor. the result of the research will indicate the fatigue level of a manufacturing supervisor