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Recognition of epileptic seizures is an important issue and in certain circumstances it is desirable to have portable equipment implementing the algorithm in order to better monitor the patients. This work considers a widely used EEG database from University of Bonn as reference for comparing our recognition method with other previously reported. In order to perform epileptic seizures we combine a...
Fast and automatic identification and analysis of different bio-medical signals is of growing importance nowadays. This necessitates the application of different computer aided diagnosis methods to interpret, distinguish and analyze various signals and images. In this paper, we have proposed a novel method to identify the Epilepsy from EEG signals. RBF Kernel based Support Vector Machine (SVM) is...
Classification of EEG under wearable environment faces many challenges including motion artifact, electrode DC offset, noise and limited available energy source. This paper describes the design consideration of a multi-channel machine-learning based EEG classification and recording processors for wearable form-factor sensors. The goal is to optimize the detection performance while balancing the analog...
EEG is the most economical and effective tool for understanding the complex dynamic behavior of the brain and studying its physiological states. In the present work, hierarchical computer aided diagnostic system (HCAD) for classification of normal, ictal and inter-ictal of EEG signals is proposed. In the present work, three different HCAD systems comprising of SVM, KNN and PNN classifiers are proposed...
A new low complexity seizure prediction algorithm is proposed. The algorithm achieves high sensitivity and low false positive rates in 10 out of 18 epileptic patients from the Freiburg database. Its primary achievement is two orders of magnitude computational complexity reduction. The reduced complexity makes an implantable medical device application realizable. In the subset of ten highly predictable...
Algorithms for Epileptic seizure prediction using various features extracted from the multichannel Electroencephalo-graphic (EEG) signals, need to work in high dimensional spaces, leading to increased difficulties in computational time and convergence conditions. Multidimensional Scaling (MDS) is a technique to surpass this curse of dimensionality in classification problems. In this work we investigate...
There is an urgent need for a quick screening process that could help neurologists diagnose and determine whether a patient is epileptic versus simply demonstrating symptoms linked to epilepsy but actually stemming from a different illness. An inaccurate diagnosis could have fatal consequences, particularly in operating rooms and intensive care units. Electroencephalogram (EEG) has been traditionally...
A patient-specific seizure prediction algorithm is proposed using a classifier to differentiate preictal from interictal ECoG signals. Spectral power of ECoG processed in four different fashions are used as features: raw, time-differential, space-differential, and time/space-differential ECoG. The features are classified using cost-sensitive support vector machines by the double cross-validation methodology...
Routine electroencephalogram (EEG) is an important test in aiding the diagnosis of patients with suspected epilepsy. These recordings typically last 20-40 minutes, during which signs of abnormal activity (spikes, sharp waves) are looked for in the EEG trace. It is essential that events of short duration are detected during the routine EEG test. The work presented in this paper examines the effect...
Several different algorithms have been proposed for automatic detection of epileptic seizure based on both scalp and intracranial electroencephalography (sEEG and iEEG). Which modality that renders the best result is hard to assess though. From 16 patients with focal epilepsy, at least 24 hours of ictal and non-ictal iEEG were obtained. Characteristics of the seizures are represented by use of wavelet...
Accurate modeling of Electroencephalography (EEG) signals is an important problem in clinical diagnosis of brain diseases. The method using support vectors machine (SVM) based on the structure risk minimization provides us an effective way of learning machine. But solving the quadratic programming problem for training SVM becomes a bottle-neck of using SVM because of the long time of SVM training...
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