Classification of Human Brain Signal for Diagnosis of Stroke Disease Using Artificial Neural Network

Classification of Human Brain Signal for Diagnosis of Stroke Disease Using Artificial Neural Network JosephSundayIgwe Brain Hub Research Team Computer Science Department Ebonyi State University

Nigeria

HCInyiama drhcinyiama@gmail.com Nnamdi Azikiwe University

Awka Nigeria

Classification of Human Brain Signal for Diagnosis of Stroke Disease Using Artificial Neural Network 3AF324007540D686CAD1B1D84B606C17 GROBID - A machine learning software for extracting information from scholarly documents Artificial Neural Network Brain -Computer Interface Brain Signals Diagnosis Electroencephalogram Signal Classification

The ravaging effect of stroke diseases in Nigeria is growing at an alarming rate. The masses are paying less attention to diagnosis and treatment. Neurologists lack efficient state of the art tools that will aid diagnosis of stroke. Electroencephalograph (EEG) signal records could improve the understanding of the mechanisms of stroke. Artificial neural network (ANN) model was used in classifying Electroencephalograph (EEG) signal report generated by the brain for the diagnosis of stroke. The result obtained agrees with expert's view of the patient's condition.

PROBLEM STATEMENT

This research is motivated by the following problems.

Increasing cases of stroke patients in Nigeria. ii.

Inadequate Equipment for effective diagnosis of stroke. iii.

Lack of good user interface platform for sole purpose of reporting outcome of EEG signal. iv.

Inadequacy of qualified neurologists to handle the increasing number of stroke patients. v.

Inability of most patients to speak to experts so as to ascertain the level of consciousness.

RELEVANCE OF STUDY

The benefits of this work are: i.

The analysis of the result gotten from mining the EEG data will lead to sensitization of programmes for causes, prevention and cure of stroke. ii.

It will aid decision making and is going to lessen pressure on the medical experts. iii.

Postgraduate students in computer science and neuroscience will find this research to be a fertile land for further researches. iv.

Nigeria as a country will find the use of EEG and the ability of ANN to classify its reports for correct diagnosis of stroke as an eye opener. Procedure are (i) Set up the equipment: electrodes  jack box  amplifier  BCI machine  computer system (ii)Radiographer mark scalp area to place electrodes (iii)Rub scalp area with Methylated Spirit (iv) Electrodes fix or wear EEG cap (v) Subject is laid on a bed/sat in comfortable chair [5].

BACKGROUND AND RELATED WORKS

RESEARCH METHODOLOGYMultilayer

Figure 16 below illustrates the major component stages of the proposed system in detail.

EXPECTED CONTRIBUTION TO KNOWLEDGE

(i) Survey will aid stroke diagnosis and help to raise the awareness level of the ravaging effect of stroke in Nigeria.

(ii) The software developed will support and further describes the outcome of EEG experiment to those with little knowledge of radiography.

(iii) This is the first time someone will be using ANN with EEG signal as a data to aid diagnosis of stroke ailment to best of our knowledge.

REFLECTIONS

There is a need to capture EEG signals from more patients to collaborate our results. Also, an innovative EEG based technology (hardware) should be developed with sole aim of diagnosing stroke diseases. This will help to raise the acceptance rate of the EEG technology among the medical practitioners.

Perceptron (MLP) classifier algorithm was used. One stroke patient and one control subject participated. EEG machine captured signals at scalp sites Left Front Parietal (FP1), Right Front Parietal (FP2), Left Front Lobe (F3), Right Front Lobe (F4), Left Center Lobe (C3), Right Center Lobe (C4), Left Parietal Lobe (P3), Right Parietal Lobe (P4), Left Occipital Lobe (O1), Right Occipital Lobe (O2), Left Frontal Lobe (F7), Right Frontal Lobe (F8), Left Temporal Lobe (T3), Right Temporal Lobe (T4), Mid Left Temporal Lobe (T5), Mid Right Temporal (T6), Center (C) and SPO2 based on international 10/20 system.

Figure 1 .1Figure 1. Human Brain Signal Classification for Diagnosis of Stroke Diseases The EEG signals captured from the stroke patient's brain are first passed through Analog to Digital Converter. The digitized signals are filtered to remove artefacts. Feature extraction is used to obtain the needed signals for further analysis/classification. EEG

Figure 2 :2Figure 2: Algorithm for Diagnosis of Stroke Diseases (case: MLP) 6. RESULT The outcomes of this research are itemized below: (i)The survey conducted suggested need for creating more awareness on the ravaging effect of stroke diseases and on the use of EEG in aiding diagnosis of stroke. (ii)EEG signals were successfully captured from both stroke patient and control subject.(iii) ANN was created and trained using MATLAB 2013a (iv) Captured Signals were classified into distinct bands of delta, theta, alpha, beta and gamma. (v)EEG recordings were interpreted using VB.NET IDE (see figure3).

Figure 3 :3Figure 3: Interpretation of EEG Recording using VB.NET IDE

Figure 4 :4Figure 4: Classification of EEG Signals into Five Distinct Frequency Waves 7. EVALUATION PLAN Brain Hub Research Team was established in Ebonyi State University to nurture and evaluate the success of this project in the future. It is team made up of the principal researcher (computer scientist), System analyst, Neurologist, Computer Engineer, and Radiographer. A publication in international reputable journals at a rate of two per year is our expected goal. CoRI'16, Sept 7-9, 2016, Ibadan, Nigeria.

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