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

Real-Time Localization of Epileptogenic Foci EEG Signals: An FPGA-Based Implementation

The epileptogenic focus is a brain area that may be surgically removed to control of epileptic seizures. Locating it is an essential and crucial step prior to the surgical treatment. However, given the difficulty of determining the localization of this brain region responsible of the initial seizure discharge, many works have proposed machine learning methods for the automatic classification of focal and non-focal electroencephalographic (EEG) signals. These works use automatic classification as an analysis tool for helping neurosurgeons to identify focal areas off-line, out of surgery, during the processing of the huge amount of information collected during several days of patient monitoring. In turn, this paper proposes an automatic classification procedure capable of assisting neurosurgeons online, during the resective epilepsy surgery, to refine the localization of the epileptogenic area to be resected, if they have doubts. This goal requires a real-time implementation with as low a computational cost as possible. For that reason, this work proposes both a feature set and a classifier model that minimizes the computational load while preserving the classification accuracy at 95.5%, a level similar to previous works. In addition, the classification procedure has been implemented on a FPGA device to determine its resource needs and throughput. Thus, it can be concluded that such a device can embed the whole classification process, from accepting raw signals to the delivery of the classification results in a cost-effective Xilinx Spartan-6 FPGA device. This real-time implementation begins providing results after a 5 s latency, and later, can deliver floating-point classification results at 3.5 Hz rate, using overlapped time-windows

Automated Classification of EEG Signals Using Component Analysis and Support Vector Machines

Epileptic seizures are characterized by abnormal electrical activity occurring in the brain. EEG records the seizures demonstrating changes in signal morphology. These signal characteristics, however, differ between patients as well as between different seizures in the same patient. Epilepsy is managed with anti-epileptic medications but in some extreme cases surgery might be necessary. Non-invasive surface electrode EEG measurement gives an estimate of the seizure onset but more invasive intra-cranial electrocorticogram (ECoG) are required at times for precise localization of the epileptogenic zone. The epileptogenic zone can be described as the cortical area targeted for resection to render the patient symptom free. Epileptologists use the “evolution” of aberrant signals for identifying epileptic seizures and the epileptogenic zone is identified by concentrating on the area contributing to the onset of seizure. This process is done by visually analyzing hours of ECoG data. The signal morphology during an epileptic seizure is not very different from abnormal discharges noticed in ECoG data thereby complicating signal analysis for the epileptologists. This thesis aims to classify the ECoG channel data as epileptic or non-epileptic using an automated machine learning algorithm called support vector machines (SVM). The data will be decomposed into various frequency bands identified by wavelet transform and will span the range of 0-30Hz. Statistical measures will be applied to these frequency bands to identify features that will subsequently be used to train SVM. This thesis will further investigate feature reduction using multivariate analysis methods to train the SVM and compare it to the performance of classification when all the features were used to train SVM. Results show that channel data classification using trained SVM that did not undergo feature reduction performed better with 98% sensitivity but needed more runtime than the SVM algorithms that was trained using reduced features. For high frequency analysis of frequencies between 60-500Hz, the results show the same sensitivity yet less specificity when compared to the classification using lower frequency range of 0-30Hz. The results seen in this thesis show that support vector machines classifiers can be trained to classify the data as epileptic or non-epileptic with good accuracy. Even though training the classifiers took almost two hours, it was still noticeably less than other machine learning algorithms such as artificial neural networks. The accuracy of this algorithm can be improved with changes to the data segment length, size of training matrix, accuracy of epileptic and nonepileptic data, and amount of data used for training

Ensemble approach on enhanced compressed noise EEG data signal in wireless body area sensor network

The Wireless Body Area Sensor Network (WBASN) is used for communication among sensor nodes operating on or inside the human body in order to monitor vital body parameters and movements. One of the important applications of WBASN is patients’ healthcare monitoring of chronic diseases such as epileptic seizure. Normally, epileptic seizure data of the electroencephalograph (EEG) is captured and compressed in order to reduce its transmission time. However, at the same time, this contaminates the overall data and lowers classification accuracy. The current work also did not take into consideration that large size of collected EEG data. Consequently, EEG data is a bandwidth intensive. Hence, the main goal of this work is to design a unified compression and classification framework for delivery of EEG data in order to address its large size issue. EEG data is compressed in order to reduce its transmission time. However, at the same time, noise at the receiver side contaminates the overall data and lowers classification accuracy. Another goal is to reconstruct the compressed data and then recognize it. Therefore, a Noise Signal Combination (NSC) technique is proposed for the compression of the transmitted EEG data and enhancement of its classification accuracy at the receiving side in the presence of noise and incomplete data. The proposed framework combines compressive sensing and discrete cosine transform (DCT) in order to reduce the size of transmission data. Moreover, Gaussian noise model of the transmission channel is practically implemented to the framework. At the receiving side, the proposed NSC is designed based on weighted voting using four classification techniques. The accuracy of these techniques namely Artificial Neural Network, Naïve Bayes, k-Nearest Neighbour, and Support Victor Machine classifiers is fed to the proposed NSC. The experimental results showed that the proposed technique exceeds the conventional techniques by achieving the highest accuracy for noiseless and noisy data. Furthermore, the framework performs a significant role in reducing the size of data and classifying both noisy and noiseless data. The key contributions are the unified framework and proposed NSC, which improved accuracy of the noiseless and noisy EGG large data. The results have demonstrated the effectiveness of the proposed framework and provided several credible benefits including simplicity, and accuracy enhancement. Finally, the research improves clinical information about patients who not only suffer from epilepsy, but also neurological disorders, mental or physiological problems