An improved approximate entropy based epilepsy seizure detection using multi-wavelet and artificial neural networks

Autor:	M. Sharanreddy, Parthasarathi K. Kulkarni
Rok vydání:	2013
Předmět:	medicine.diagnostic_test Artificial neural network Computer science Speech recognition Biomedical Engineering Electroencephalography medicine.disease Approximate entropy Signal body regions Epilepsy Wavelet medicine Transient (oscillation) Sensitivity (control systems)
Zdroj:	International Journal of Biomedical Engineering and Technology. 11:81
ISSN:	1752-6426 1752-6418
DOI:	10.1504/ijbet.2013.053716
Popis:	Epilepsy seizure is the result of the transient and unexpected electrical disturbance of the brain signal. The detection of epilepsy is only possible by analysing the normal and abnormal changes of brain electrical signal. The detection of epilepsy, which includes EEG recordings for the spikes and seizures, is very time consuming, especially in the case of long recordings. In this paper, an Artificial Intelligence (AI) based epilepsy detection technique is proposed. The technique is the combination of Multi-Wavelet Transform (MWT) and Artificial Neural Network (ANN). MWT is a technique based on wavelet theory, which is used to extract the features of EEG signal. The irregularity of EEG signal is measured by using the propose Improved Approximate Entropy (IApE). ANN is an AI that used to detect the type of EEG signal. The proposed technique is implemented, tested and the sensitivity, specificity, accuracy, precision response of IApE and ApE are compared.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::4832d042489f73c47a4b2e52d6a26151 https://doi.org/10.1504/ijbet.2013.053716 Zobrazit plný text záznamu