Elements of the Theory of Computation
Elements of the Theory of Computation
Introduction to Automata Theory, Languages, and Computation (3rd Edition)
Introduction to Automata Theory, Languages, and Computation (3rd Edition)
EEG signal classification using wavelet feature extraction and a mixture of expert model
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Theory of Computer Science: Automata, Languages and Computation
Theory of Computer Science: Automata, Languages and Computation
Epileptic seizure detection: A nonlinear viewpoint
Computer Methods and Programs in Biomedicine
Epileptic seizure detection using dynamic wavelet network
Expert Systems with Applications: An International Journal
Weighted finite automata for video compression
IEEE Journal on Selected Areas in Communications
Parallel Algorithm to Analyze the Brain Signals: Application on Epileptic Spikes
Journal of Medical Systems
DFAspike: A new computational proposition for efficient recognition of epileptic spike in EEG
Computers in Biology and Medicine
Classifying Epilepsy Diseases Using Artificial Neural Networks and Genetic Algorithm
Journal of Medical Systems
Feature extraction and recognition of ictal EEG using EMD and SVM
Computers in Biology and Medicine
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This Paper presents an automated method of Epileptic Spike detection in Electroencephalogram (EEG) using Deterministic Finite Automata (DFA). It takes pre-recorded single channel EEG data file as input and finds the occurrences of Epileptic Spikes data in it. The EEG signal was recorded at 256 Hz in two minutes separate data files using the Visual Lab-M software (ADLink Technology Inc., Taiwan). It was preprocessed for removal of baseline shift and band pass filtered using an infinite impulse response (IIR) Butterworth filter. A system, whose functionality was modeled with DFA, was designed. The system was tested with 10 EEG signal data files. The recognition rate of Epileptic Spike as on average was 95.68%. This system does not require any human intrusion. Also it does not need any short of training. The result shows that the application of DFA can be useful in detection of different characteristics present in EEG signals. This approach could be extended to a continuous data processing system.