PESC: A Parallel System for Clustering ECG Streams Based on MapReduce

MPhil Thesis Defence


Title: "PESC: A Parallel System for Clustering ECG Streams Based on MapReduce"

By

Mr. Lin YANG


Abstract

Nowadays, due to the unhealthy lifestyle and high stress in modern 
society, cardiovascular disease (CVD) has become a disease of the 
majority. As an important instrument for diagnosing CVD, 
electrocardiography (ECG) is used to extract useful information about the 
functioning status of the heart. To help clinicians better utilize the ECG 
data, various systems have been proposed in last decades. One of the key 
issues in these system is the analysis of ECG data. In this domain, 
cluster analysis is a commonly applied approach to gain an overview of the 
data, detect outliers or pre-process before further analysis. In recent 
years, to provide better medical care for CVD patients, the new-generation 
cardiac telehealth system, which could monitor patients' ECG in a 
real-time manner, has draw a great attention from both academia and 
industry. In these systems, the collected ECG data is transmitted to a 
remote server and analysed in a real-time manner. However, the extremely 
large volume and high update rate of data in these telehealth systems have 
made cluster analysis a challenging work. In this paper, we design and 
implement a novel parallel system for clustering massive ECG stream data 
based on the MapReduce framework. In our approach, a global optimum of 
clustering is achieved by merging and splitting clusters dynamically. 
Meanwhile, a good performance is gained by distributing computation over 
multiple computing nodes. According to the evaluation, our system not only 
provides good clustering results but also has an excellent performance on 
multiple computing nodes.


Date:			Friday, 19 July 2013

Time:			4:00pm – 6:00pm

Venue:			Room 3501
 			Lifts 25/26

Committee Members:	Prof. Qian Zhang (Supervisor)
 			Dr. Lei Chen (Chairperson)
 			Dr. Lin Gu


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