Transient ST-segment episode detection for ECG beat classification

Suma C. Bulusu, Miad Faezipour, Vincent Ng, Mehrdad Nourani, Lakshman S. Tamil, Subhash Banerjee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Scopus citations

Abstract

Sudden Cardiac Death (SCD) is an unexpected death caused by loss of heart function when the electrical impulses fired from the ventricles become irregular. Most common SCDs are caused by cardiac arrhythmias and coronary heart disease. They are mainly due to Acute Myocardial Infarction (AMI), myocardial ischaemia and cardiac arrhythmia. This paper aims at automating the recognition of ST-segment deviations and transient ST episodes which helps in the diagnosis of myocardial ischaemia and also classifying major cardiac arrhythmia. Our approach is based on the application of signal processing and artificial intelligence to the heart signal known as the ECG (Electrocardiogram). We propose an improved morphological feature vector including ST-segment information for heart beat classification by supervised learning using the support vector machine approach. Our system has been tested and yielded an accuracy of 93.33% for the ST episode detection on the European ST-T Database and 96.35% on MIT-BIH Arrhythmia Database for classifying six major groups, i.e. Normal, Ventricular, Atrial, Fusion, Right Bundle and Left Bundle Branch Block beats.

Original languageEnglish (US)
Title of host publicationProceedings of the 2011 IEEE/NIH Life Science Systems and Applications Workshop, LiSSA 2011
Pages121-124
Number of pages4
DOIs
StatePublished - 2011
Event2011 IEEE/NIH Life Science Systems and Applications Workshop, LiSSA 2011 - Bethesda, MD, United States
Duration: Apr 7 2011Apr 8 2011

Publication series

NameProceedings of the 2011 IEEE/NIH Life Science Systems and Applications Workshop, LiSSA 2011

Other

Other2011 IEEE/NIH Life Science Systems and Applications Workshop, LiSSA 2011
CountryUnited States
CityBethesda, MD
Period4/7/114/8/11

ASJC Scopus subject areas

  • Life-span and Life-course Studies

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