TY - GEN
T1 - Transient ST-segment episode detection for ECG beat classification
AU - Bulusu, Suma C.
AU - Faezipour, Miad
AU - Ng, Vincent
AU - Nourani, Mehrdad
AU - Tamil, Lakshman S.
AU - Banerjee, Subhash
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=79956086850&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79956086850&partnerID=8YFLogxK
U2 - 10.1109/LISSA.2011.5754171
DO - 10.1109/LISSA.2011.5754171
M3 - Conference contribution
AN - SCOPUS:79956086850
SN - 9781457704208
T3 - Proceedings of the 2011 IEEE/NIH Life Science Systems and Applications Workshop, LiSSA 2011
SP - 121
EP - 124
BT - Proceedings of the 2011 IEEE/NIH Life Science Systems and Applications Workshop, LiSSA 2011
T2 - 2011 IEEE/NIH Life Science Systems and Applications Workshop, LiSSA 2011
Y2 - 7 April 2011 through 8 April 2011
ER -