TY - GEN
T1 - Arrhythmia classification with reduced features by linear discriminant analysis
AU - Lee, J.
AU - Park, K. L.
AU - Song, M. H.
AU - Lee, K. J.
PY - 2005
Y1 - 2005
N2 - In this study, we proposed 17 input features based on wavelet coefficients for arrhythmia detection and, by applying linear discriminant analysis to these, reduced the feature dimension to be 4. Then, with newly constructed 4 dimension input feature, a multi-layer perceptrons classifier was tried to detect 6 types of arrhythmia beats. For evaluation of input features by linear discriminant analysis, the arrhythmia detection efficiency with these (LDA) was compared to that with original input features (ORG) and that with of input features by principle component analysis (PCA) respectively. When LDA was compared to ORG, the former showed similar or a little higher values than the latter for different types of arrhythmia beats except SVT. And, LDA showed to be persistently higher than PCA. By theses cross-validations, for the detection of several types of arrhythmia beats, the reduction of input feature dimension by linear discriminant analysis was revealed to be prior to that by principle component analysis. Even if LDA was compared to ORG, it maintained the acceptable level efficiency so that the time and computational costs would be expected to be cutdown dramatically. Finally, by the proposed algorithm, we could obtain the good accuracy of arrhythmia detection and that of NSR, SVT, PVC and VF was 99.52%, 99.43%, 98.59% and 99.88%, respectively.
AB - In this study, we proposed 17 input features based on wavelet coefficients for arrhythmia detection and, by applying linear discriminant analysis to these, reduced the feature dimension to be 4. Then, with newly constructed 4 dimension input feature, a multi-layer perceptrons classifier was tried to detect 6 types of arrhythmia beats. For evaluation of input features by linear discriminant analysis, the arrhythmia detection efficiency with these (LDA) was compared to that with original input features (ORG) and that with of input features by principle component analysis (PCA) respectively. When LDA was compared to ORG, the former showed similar or a little higher values than the latter for different types of arrhythmia beats except SVT. And, LDA showed to be persistently higher than PCA. By theses cross-validations, for the detection of several types of arrhythmia beats, the reduction of input feature dimension by linear discriminant analysis was revealed to be prior to that by principle component analysis. Even if LDA was compared to ORG, it maintained the acceptable level efficiency so that the time and computational costs would be expected to be cutdown dramatically. Finally, by the proposed algorithm, we could obtain the good accuracy of arrhythmia detection and that of NSR, SVT, PVC and VF was 99.52%, 99.43%, 98.59% and 99.88%, respectively.
UR - http://www.scopus.com/inward/record.url?scp=33846922056&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33846922056&partnerID=8YFLogxK
U2 - 10.1109/iembs.2005.1616623
DO - 10.1109/iembs.2005.1616623
M3 - Conference contribution
AN - SCOPUS:33846922056
SN - 0780387406
SN - 9780780387409
T3 - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
SP - 1142
EP - 1144
BT - Proceedings of the 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005
Y2 - 1 September 2005 through 4 September 2005
ER -