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

• Published in: International journal of image, graphics and signal processing Vol. 11; no. 10; pp. 16 - 22
• Main Authors: R. Rabbani, Md. Hasin, Rabiul Islam, Sheikh Md
• Format: Journal Article
• Language: English
• Published: Hong Kong Modern Education and Computer Science Press 01.10.2019
• Subjects: Algorithms; Brain; Brain diseases; Electroencephalography; Epilepsy; Infrared spectra; Iterative methods; Magnetic resonance imaging; Markov analysis; Markov chains; Medical imaging; Near infrared radiation; Normality; Parameter estimation; Probabilistic models; Seizures; Statistical analysis; Temporal resolution; Transition probabilities
• ISSN: 2074-9074; 2074-9082
• DOI: 10.5815/ijigsp.2019.10.03

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).