Detection of different brain diseases from EEG signals using hidden markov model
Автор: Md. Hasin R. Rabbani, Sheikh Md. Rabiul Islam
Журнал: International Journal of Image, Graphics and Signal Processing @ijigsp
Статья в выпуске: 10 vol.11, 2019 года.
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The brain imaging device, Electroencephalography (EEG) provides several advantages over other brain signals like Functional Near-infrared Spectroscopy (fNIRS) and Functional Magnetic Resonance Imaging (fMRI). It is non-invasive and easily applicable. EEG provides high temporal resolution with a low setup cost. EEG signals of several subjects which record electric potential caused by neurons firing in the brain are undergone a Hidden Markov Model (HMM) classification technique. We are particularly interested to detect the brain diseases from EEG signals by an HMM probabilistic model. This HMM model is built with a given initial probability matrix of five different states, namely, epilepsy, seizure, dementia, stroke and normality. The transition probability matrix is updated after each iteration of parameter estimation using Baum-Welch algorithm (B-W algorithm).
Electroencephalography (EEG), Hidden Markov Model (HMM), Baum-Welch algorithm (B-W algorithm), Initial probability matrix, Transition probability matrix
Короткий адрес: https://sciup.org/15016087
IDR: 15016087 | DOI: 10.5815/ijigsp.2019.10.03
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