Detection of Different Brain Diseases from EEG Signals Using Hidden Markov Model

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Md. Hasin R. Rabbani 1,* Sheikh Md. Rabiul Islam 1

1. Department of Electronics & Communication Engineering, Khulna university of Engineering & Technology, Khulna, Bangladesh

* Corresponding author.


Received: 1 Jun. 2019 / Revised: 11 Jun. 2019 / Accepted: 27 Jun. 2019 / Published: 8 Oct. 2019

Index Terms

Electroencephalography (EEG), Hidden Markov Model (HMM), Baum-Welch algorithm (B-W algorithm), Initial probability matrix, Transition probability matrix.


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).

Cite This Paper

Md. Hasin R. Rabbani, Sheikh Md. Rabiul Islam, "Detection of Different Brain Diseases from EEG Signals Using Hidden Markov Model", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.11, No.10, pp. 16-22, 2019. DOI: 10.5815/ijigsp.2019.10.03


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