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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.

 

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