EEG analysis: Brain connectivity in stroke

Author: Turki Alrobaian

Alrobaian, Turki, 2017 EEG analysis: Brain connectivity in stroke, Flinders University, School of Computer Science, Engineering and Mathematics

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Abstract

Today in the modernization world everything has become so simple in one click of button, but this fast living life and changes in way of living has led to some major problems. Brain stroke is one of the leading problems in today’s middle or elder age people. Science has progressed a lot and looks for many network analysis to deal with such problems. But still it’s a field in which many unfolds still exist which creates a hinder to successfully unturned the issue. Brain consists of 100 billion neurons and one trillion gila. This network of brain stores experiences and houses consciousness. Focal brain lesions affect multiple network properties simultaneously and how changes on smaller scales influence those on larger scales. Although understanding of the brain was enhanced by the stroke, stroke is one of the biggest killers and a leading cause of disability. Stroke describes damage to the neuraxis (the brain and spinal cord), resulting from an abnormality in cerebral blood supply. It is a neurological disorder characterized by interrupted or insufficient blood supply to certain parts of the brain. It is a significant cause of mortality and morbidity. The main causes of ischemic stroke in large blood vessels are building up of abnormal fatty lumps on the inner lining of arteries supplying blood to the brain, infections such as valvular diseases, ischemic heart disease and diabetes that narrow cerebral blood vessels or abnormal heart rhythms. In small cerebral blood vessels, fatty lumps on the inner walls of the arteries could break off and transported by the bloodstream to lodge in the narrower blood vessels in the brain causing embolic occlusion. The main causes of leaking or rapturing of weak blood vessels in hemorrhagic stroke are aneurysm and arteriovenous malformations. Aneurysm results from a weakened part of a blood vessel ballooning. If unattended, the region continues to balloon and weaken until it raptures and releases blood into the brain. Arteriovenous malformations refer to a group of abnormally formed blood vessels. Any of these blood vessel can rapture and release blood into the brain cavity. Other causes of rapturing of weak cerebral blood vessels are cranial (head) trauma, an abnormal accumulation of blood in the cranial cavity, disorders such as hypertension and cerebral amyloid angiopathy tumors in the brain, the use of therapeutic anticoagulation (blood-thinning medication) or bleeding disorders that cause a considerable reduction in platelets. The motivation for this work includes an electroencephalogram (EEG)-based experiment on the performance of several mental tasks including reading, auditory, subtraction and finger tapping among others in 9 patients with multilateral stroke, the main challenge was to analyse that the anatomic lesion affects the functional brain network on multiple levels. In the past few years a large number of studies in the specific field have been developed. A broad range of detecting follows the morphological based methods. However, many of the methods, proposed previously failed to give good performance at the output. Sahil Bajaj [2015] [35] proposed a method which attracts the field in new limelight. In the paper, the author’s aims at primary motor area during motor imagination (MI) task and how this differs during motor execution (ME) task are still questions of interest. A new scheme to make some improvement on this is to explore the application of Normalised Transfer Entropy (NTE) measure to construct EEG based directed brain network. Along with this, the bedrock is to quantify and plot the network for statistically important connections for both stroke and healthy subjects and to determine information flow patterns during different tasks.

Keywords: Brain Connectivity, electroencephalogram, EEG, Stroke, Normalised Transfer Entropy
Subject: Engineering thesis

Thesis type: Masters
Completed: 2017
School: School of Computer Science, Engineering and Mathematics
Supervisor: Kenneth Pope