Performance Comparison of classification algorithms for EEG-based remote epileptic seizure detection in Wireless Sensor Networks
| Autor: | Khalid Abualsaud, Amr Mohamed, Mohammad Saleh, Massudi Mahmuddin |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: |
Classification accuracy
Performance comparison Computer science Speech recognition Feature extraction Neurophysiology Electroencephalography Classification algorithm Discrete cosine transforms Statistical features C4.5 algorithm Wavelet medicine Discrete cosine transform Data mining medicine.diagnostic_test Classifiers Classification (of information) business.industry Neurodegenerative diseases Wide-ranging applications Pattern recognition Wavelet coefficients Epileptic seizures Wireless sensor networks Electrophysiology Statistical classification ComputingMethodologies_PATTERNRECOGNITION Epileptic seizure detection Artificial intelligence Epileptic seizure medicine.symptom business Wireless sensor network Algorithms |
| Zdroj: | AICCSA |
| Popis: | Identification of epileptic seizure remotely by analyzing the electroencephalography (EEG) signal is very important for scalable sensor-based health systems. Classification is the most important technique for wide-ranging applications to categorize the items according to its features with respect to predefined set of classes. In this paper, we conduct a performance evaluation based on the noiseless and noisy EEG-based epileptic seizure data using various classification algorithms including BayesNet, DecisionTable, IBK, J48/C4.5, and VFI. The reconstructed and noisy EEG data are decomposed with discrete cosine transform into several sub-bands. In addition, some of statistical features are extracted from the wavelet coefficients to represent the whole EEG data inputs into the classifiers. Benchmark on widely used dataset is utilized for automatic epileptic seizure detection including both normal and epileptic EEG datasets. The classification accuracy results confirm that the selected classifiers have greater potentiality to identify the noisy epileptic disorders. 2014 IEEE. Scopus |
| Databáze: | OpenAIRE |
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