TY - GEN
T1 - ECG quality assessment based on multi-feature fusion
AU - Xia, Yong
AU - Jia, Honghong
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2018/6/21
Y1 - 2018/6/21
N2 - This paper proposes a new method for ECG quality classification based on multi-feature fusion. Lots of features, including waveform attributes, power spectrum, R-wave detection, etc., are given and each feature is evaluated independently. For the best performance, different combinations of features are tested. Rule-based method and learning-based method are considered for classification. The database from PhysioNet/Computing in Cardiology Challenge 2011 is used for performance evaluation and 92.8% and 90.4% classification accuracy are obtained in the training and test collection respectively using the rule-based method, and the average processing time of each ECG recording is 0.78s. Furthermore, learning-based method gets higher classification accuracy, and 94.0% and 91.6% are achieved in the training and test collection respectively, but the time cost is a little larger than rule-based method, and it is 2.03s.
AB - This paper proposes a new method for ECG quality classification based on multi-feature fusion. Lots of features, including waveform attributes, power spectrum, R-wave detection, etc., are given and each feature is evaluated independently. For the best performance, different combinations of features are tested. Rule-based method and learning-based method are considered for classification. The database from PhysioNet/Computing in Cardiology Challenge 2011 is used for performance evaluation and 92.8% and 90.4% classification accuracy are obtained in the training and test collection respectively using the rule-based method, and the average processing time of each ECG recording is 0.78s. Furthermore, learning-based method gets higher classification accuracy, and 94.0% and 91.6% are achieved in the training and test collection respectively, but the time cost is a little larger than rule-based method, and it is 2.03s.
KW - ECG quality assessment
KW - feature fusion
KW - learning-based classification
KW - rule-based classifation
UR - https://www.scopus.com/pages/publications/85050221674
U2 - 10.1109/FSKD.2017.8393352
DO - 10.1109/FSKD.2017.8393352
M3 - 会议稿件
AN - SCOPUS:85050221674
T3 - ICNC-FSKD 2017 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery
SP - 672
EP - 676
BT - ICNC-FSKD 2017 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery
A2 - Zhao, Liang
A2 - Wang, Lipo
A2 - Cai, Guoyong
A2 - Li, Kenli
A2 - Liu, Yong
A2 - Xiao, Guoqing
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2017
Y2 - 29 July 2017 through 31 July 2017
ER -