1,239 research outputs found
Development of an Epileptic Seizure Detection Application based on Parallel Computing
Abstract—Epileptic seizure detection in a large database of Electroencephalography (EEG) signals needs to be a time constrained process for real-time analysis. Epileptic seizure detection algorithms are designed to obtain and analyze a group of neural signals and recognize the presence of seizure occurrence. The computational cost of the algorithms should be minimized to reduce the processing time and memory consumption. Automated epileptic seizure detection using optimized feature selection improves the classification accuracy, but it occupies more processing time during the Artifact Removal (AR) stage. So, the execution time is greatly reduced by introducing task parallelism in the artifact removal stage. By harnessing parallel computing the computational overhead and processing time are decreased. An epileptic seizure detection application is developed and analyzed with respect to execution time, speedup, and parallel efficiency. The application was developed in Intel Pentium(R) Dual-core CPU with processor clock rate of 2.60 GHz, memory of 1.96 GB, and operating system of Windows X
A Novel Method for Epileptic Seizure Detection Using Coupled Hidden Markov Models
We propose a novel Coupled Hidden Markov Model to detect epileptic seizures
in multichannel electroencephalography (EEG) data. Our model defines a network
of seizure propagation paths to capture both the temporal and spatial evolution
of epileptic activity. To address the intractability introduced by the coupled
interactions, we derive a variational inference procedure to efficiently infer
the seizure evolution from spectral patterns in the EEG data. We validate our
model on EEG aquired under clinical conditions in the Epilepsy Monitoring Unit
of the Johns Hopkins Hospital. Using 5-fold cross validation, we demonstrate
that our model outperforms three baseline approaches which rely on a classical
detection framework. Our model also demonstrates the potential to localize
seizure onset zones in focal epilepsy.Comment: To appear in MICCAI 2018 Proceeding
Classification of Epileptic EEG Signals by Wavelet based CFC
Electroencephalogram, an influential equipment for analyzing humans
activities and recognition of seizure attacks can play a crucial role in
designing accurate systems which can distinguish ictal seizures from regular
brain alertness, since it is the first step towards accomplishing a high
accuracy computer aided diagnosis system (CAD). In this article a novel
approach for classification of ictal signals with wavelet based cross frequency
coupling (CFC) is suggested. After extracting features by wavelet based CFC,
optimal features have been selected by t-test and quadratic discriminant
analysis (QDA) have completed the Classification.Comment: Electroencephalogram; Wavelet Decomposition; Cross Frequency
Coupling;Quadratic Discriminant Analysis; T-test Feature Selectio
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