TY - GEN
T1 - Automatic classification of heartbeats
AU - Basil, Tony
AU - Lakshminarayan, Choudur
N1 - Publisher Copyright:
© 2014 EURASIP.
PY - 2014/11/10
Y1 - 2014/11/10
N2 - We report improvement in the detection of a class of heart arrhythmias based on electrocardiogram signals (ECG). The detection is performed using a 4 dimensional feature vector obtained by applying an iterative feature selection method used in conjunction with artificial neural networks. The feature set includes the pre-RR interval, which is a primary measure that cardiologists use in a clinical setting. A transformation applied to the pre-RR interval reduced the false positive rate. Our solution as opposed to existing literature does not rely on high-dimensional features such as wavelets, signal amplitudes which do not have direct relationship to heart function and difficult to interpret. Also we avoid obtaining patient specific labeled recordings. Furthermore, we propose semi-parametric classifiers as opposed to restrictive parametric linear discriminant analysis and its variants, which are a mainstay in ECG classification. Extensive experiments from the MIT-BIH databases demonstrate superior performance by our methods.
AB - We report improvement in the detection of a class of heart arrhythmias based on electrocardiogram signals (ECG). The detection is performed using a 4 dimensional feature vector obtained by applying an iterative feature selection method used in conjunction with artificial neural networks. The feature set includes the pre-RR interval, which is a primary measure that cardiologists use in a clinical setting. A transformation applied to the pre-RR interval reduced the false positive rate. Our solution as opposed to existing literature does not rely on high-dimensional features such as wavelets, signal amplitudes which do not have direct relationship to heart function and difficult to interpret. Also we avoid obtaining patient specific labeled recordings. Furthermore, we propose semi-parametric classifiers as opposed to restrictive parametric linear discriminant analysis and its variants, which are a mainstay in ECG classification. Extensive experiments from the MIT-BIH databases demonstrate superior performance by our methods.
KW - Artificial neural networks
KW - Classification
KW - Discriminant analysis
KW - ECG
KW - False positives
UR - http://www.scopus.com/inward/record.url?scp=84911888370&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84911888370&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84911888370
T3 - European Signal Processing Conference
SP - 1542
EP - 1546
BT - 2014 Proceedings of the 22nd European Signal Processing Conference, EUSIPCO 2014
T2 - 22nd European Signal Processing Conference, EUSIPCO 2014
Y2 - 1 September 2014 through 5 September 2014
ER -