I.J. Image, Graphics and Signal Processing, 2019, 10, 16-22 Published Online October 2019 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijigsp.2019.10.03 Copyright © 2019 MECS I.J. Image, Graphics and Signal Processing, 2019, 10, 16-22 Detection of Different Brain Diseases from EEG Signals Using Hidden Markov Model Md. Hasin R. Rabbani, Sheikh Md. Rabiul Islam Department of Electronics & Communication Engineering, Khulna university of Engineering & Technology, Khulna, Bangladesh Email: dip.hasin@gmail.com; robi@ece.kuet.ac.bd Received: 01 June 2019; Accepted: 27 June 2019; Published: 08 October 2019 Abstract—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). Index Terms—Electroencephalography (EEG), Hidden Markov Model (HMM), Baum-Welch algorithm (B-W algorithm), Initial probability matrix, Transition probability matrix. I. INTRODUCTION The applications of Electroencephalography (EEG), as a brain signal, is expanding worldwide, mainly for diagnostic purposes and preventive measures of neurological diseases such as epilepsy, stroke, Parkinson’s diseases and others. The continuous monitoring of EEG signals is necessary to observe the electrical activity of the brain to evaluate drug intoxication, trauma and blood flow during surgical procedures. A key advantage of EEG is that it provides higher temporal resolution than other available brain signals as it responds to the cognitive activities of a subject very rapidly (0.5-130 milliseconds). Hidden Markov Model (HMM) is a non-linear probabilistic classifier and it provides the probability of a given set of series in time domain. As a non-linear classification technique, it shows higher classification accuracy than those of linear classifiers, for example, linear discriminant analysis (LDA) and support vector machine (SVM). In recent years, HMM has been applied in the various fields of bioinformatics, data mining, pattern recognition, data analysis, wireless networks etc. Some notable works in recent times are protein secondary structure prediction based on a HMM model for data mining [1], offline recognition cursive of Arabic handwritten text without explicit segmentation [2], muscle-computer interface based on HMM state transitions which uses ultrasound sensing [3], action recognition by Gaussian-Mixture HMM (GMM-HMM) model which yields a greater recognition accuracy [4]. HMM has made its mark in different medical, biology and rehabilitation fields; for example, identifying movement states of Parkinsonian patients [5], personal identification system [6], functional brain networks [7], quantification of wheezing for respiratory sound classification [8], single-molecule data analysis in time series which accommodates complications such as drift [9]. Septic shock of critical care patient causes multiple organ failure and eventual death. A HMM model which predicts septic shock for ICU patients [10]. Rehabilitation of a deaf person is done by a DNN-HMM hybrid system for lip-reading and audio visual speech recognition (AVSR) [11], fall detection and real-life home monitoring for senior citizens [12], emotion classification by a combined SVM-HMM classifier to recognize human emotion states based on EEG signals [13]. Already a stacked HMM model has found its applications in robotics for motion intention recognition based on motion trajectories [14]. Other recent and notable studies of HMM include removal of EEG artifact caused by eye blinks [15], seismocardiography learning based on expectation- maximization algorithm and Viterbi algorithm [16], automatic volcano-seismic events detection based on HMM with state and event duration models [17], neural prosthesis to restore efficient communication to people with motor neurological injury [18], obstructive sleep apnea (OSA) detection based on ECG signals [19], 3D catheter tip tracking inside the patient’s 3D vessel tree using 2D X-ray image sequences and a peri-operative 3D rotational angiography (3DRA) [20], palm rehabilitation with supervised learning[21], automatic significant beats extraction in Holter register and feature extraction of ECG signals [22]. Many studies, mostly of functional near-infrared spectroscopy (fNIRS) have successfully