bullet Sensors & Transducers Journal

    (ISSN: 2306-8515, e-ISSN 1726-5479)

0.705

2013 Global Impact Factor

205.767

2008 e-Impact Factor

25 Top Downloaded Articles

Best Selling Articles 2012

Journal Subscription

Editorial Calendar

Submit an Article

Editorial Board

Current Issue

S&T journal's cover

Sensors & Transducers Journal 2011

Sensors & Transducers Journal 2010

Sensors & Transducers Journal 2009

Sensors & Transducers Journal 2008

Sensors & Transducers Journal 2007

2000-2002 S&T e-Digest Contents

2003 S&T e-Digest Contents

2004 S&T e-Digest Contents

2005 S&T e-Digest Contents

2006 S&T e-Digest Contents

 

Best Articles 2011

 

 

 

Vol. 184, Issue 1, January 2015, pp. 92-100

 

Bullet

 

Electrocardiogram Classification Sensor System Supporting an Autonomous Mobile
Cardiovascular Disease Detection Aid
 

1 Patrick DaSILVA, 2 Paul FORTIER, 3 Kristen SETHARES

1, 2 Electrical and Computer Engineering Department, University of Massachusetts Dartmouth, 285 Old Westport Road, Dartmouth, MA, 02747, USA
3 Adult and Child Nursing Department, University of Massachusetts Dartmouth, 285 Old Westport Road, Dartmouth, MA, 02747, USA
2 Tel.: 15089998544, fax: 15089998489

E-mail: pdasilva@umassd.edu, pfortier@umassd.edu, ksethares@umassd.edu

 

Received: 14 November 2014 /Accepted: 15 December 2014 /Published: 31 January 2015

Digital Sensors and Sensor Sysstems

 

Abstract: Current healthcare mobile monitoring solutions do not offer the ability to autonomously recognize cardiac arrhythmias. The proposed electrocardiogram detection and classification software is designed to run on a mobile cardiovascular disease detection sensor suite alleviating the need for human interpretation. The electrocardiogram is filtered using the Wavelet Transform; the principally important wave points detected using a modified version of the Pan Tompkins rule set and the cardiac rhythm is classified using an N-ary tree. Implemented on a custom designed printed circuit board, testing results show autonomous classifications are possible using a three lead electrocardiogram system while the patient is at rest. The proposed solution serves as a stepping stone towards a fully reliable patient disease management teaching tool with the potential to serve as an aid to the cardiovascular healthcare industry.

 

Keywords: Embedded ECG sensor, Real-time algorithm, ECG classification.

 

Acrobat reader logo Click <here> or title of paper to download the full pages article in pdf format

 

 

Subscribe the full-page Sensors & Transducers journal in print (paper) or pdf formats

(shipping cost by standard mail for paper version is included)

(25 % discount for IFSA Members)

 

 

 

Alternatively we accept a money transfer to our bank account. Please contact for details: sales@sensorsportal.com

 

 

Download <here> the Library Journal Recommendation Form

 

 

 

 

 


1999 - 2015 Copyright , International Frequency Sensor Association (IFSA) Publishing, S.L. All Rights Reserved.


Home - News - Links - Archives - Tools - Voltage-to-Frequency Converters - Standardization - Patents - Marketplace - Projects - Wish List - e-Shop - Sensor Jobs - Membership - Videos - Publishing - Site Map - Subscribe - Search

 Members Area -Sensors Portal -Training Courses - S&T Digest - For advertisers - Bookstore - Forums - Polls - Submit Press Release - Submit White Paper - Testimonies - Twitter - Facebook - LinkedIn