Extreme learning machine based ECG analyzer for distributed diagnosis and home health care (D2H2)

83 p.

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Bibliographic Details
Main Author:	Sattu Sreenu Babu.
Other Authors:	Soh Cheong Boon
Format:	Theses and Dissertations
Published:	2010
Subjects:	DRNTU::Engineering::Mechanical engineering::Assistive technology
Online Access:	http://hdl.handle.net/10356/36087
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Institution:	Nanyang Technological University

id	sg-ntu-dr.10356-36087
record_format	dspace
spelling	sg-ntu-dr.10356-360872023-03-11T17:07:10Z Extreme learning machine based ECG analyzer for distributed diagnosis and home health care (D2H2) Sattu Sreenu Babu. Soh Cheong Boon School of Mechanical and Aerospace Engineering DRNTU::Engineering::Mechanical engineering::Assistive technology 83 p. Cardiac diseases are by far the most common reason for death in developed countries, which leads to the need of permanent surveillance of cardiac risk patients. The ECQ which provides the key information about the electrical activity of the heart is the most important biosignal used by cardiologists for diagnostic purposes. Modem medicine is dependent on the monitoring of patients and their conditions and illnesses. In preventive control of cardiovascular diseases, cardiac patient monitoring systems play a vital role by providing early detection and constant monitoring. To aid this, telemedicine systems and efficient protocols are developed to help cardiac patients with the transition from hospital to home-based cardiac outpatient programs, with improved continuity of care. Conventional methods of monitoring and diagnosing arrhythmia rely on detecting the presence of particular signal features by a human observer. (Due to the large number of patients in intensive care units and the need for continuous observation of such conditions, several techniques for automated arrhythmia detection have been developed in the past ten years to attempt to solve this problem. Many algorithms have been expensively used for ECQ classification such as Hidden Markov models, Neural Networks and Support Vector Machines etc. Master of Science (Biomedical Engineering) 2010-04-23T02:27:26Z 2010-04-23T02:27:26Z 2006 2006 Thesis http://hdl.handle.net/10356/36087 application/pdf
institution	Nanyang Technological University
building	NTU Library
continent	Asia
country	Singapore Singapore
content_provider	NTU Library
collection	DR-NTU
topic	DRNTU::Engineering::Mechanical engineering::Assistive technology
spellingShingle	DRNTU::Engineering::Mechanical engineering::Assistive technology Sattu Sreenu Babu. Extreme learning machine based ECG analyzer for distributed diagnosis and home health care (D2H2)
description	83 p.
author2	Soh Cheong Boon
author_facet	Soh Cheong Boon Sattu Sreenu Babu.
format	Theses and Dissertations
author	Sattu Sreenu Babu.
author_sort	Sattu Sreenu Babu.
title	Extreme learning machine based ECG analyzer for distributed diagnosis and home health care (D2H2)
title_short	Extreme learning machine based ECG analyzer for distributed diagnosis and home health care (D2H2)
title_full	Extreme learning machine based ECG analyzer for distributed diagnosis and home health care (D2H2)
title_fullStr	Extreme learning machine based ECG analyzer for distributed diagnosis and home health care (D2H2)
title_full_unstemmed	Extreme learning machine based ECG analyzer for distributed diagnosis and home health care (D2H2)
title_sort	extreme learning machine based ecg analyzer for distributed diagnosis and home health care (d2h2)
publishDate	2010
url	http://hdl.handle.net/10356/36087
_version_	1761781853705994240

Extreme learning machine based ECG analyzer for distributed diagnosis and home health care (D2H2)

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