CLC number: TP181
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2014-06-16
Cited: 10
Clicked: 9708
Ya-tao Zhang, Cheng-yu Liu, Shou-shui Wei, Chang-zhi Wei, Fei-fei Liu. ECG quality assessment based on a kernel support vector machine and genetic algorithm with a feature matrix[J]. Journal of Zhejiang University Science C, 2014, 15(7): 564-573.
@article{title="ECG quality assessment based on a kernel support vector machine and genetic algorithm with a feature matrix",
author="Ya-tao Zhang, Cheng-yu Liu, Shou-shui Wei, Chang-zhi Wei, Fei-fei Liu",
journal="Journal of Zhejiang University Science C",
volume="15",
number="7",
pages="564-573",
year="2014",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C1300264"
}
%0 Journal Article
%T ECG quality assessment based on a kernel support vector machine and genetic algorithm with a feature matrix
%A Ya-tao Zhang
%A Cheng-yu Liu
%A Shou-shui Wei
%A Chang-zhi Wei
%A Fei-fei Liu
%J Journal of Zhejiang University SCIENCE C
%V 15
%N 7
%P 564-573
%@ 1869-1951
%D 2014
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1300264
TY - JOUR
T1 - ECG quality assessment based on a kernel support vector machine and genetic algorithm with a feature matrix
A1 - Ya-tao Zhang
A1 - Cheng-yu Liu
A1 - Shou-shui Wei
A1 - Chang-zhi Wei
A1 - Fei-fei Liu
J0 - Journal of Zhejiang University Science C
VL - 15
IS - 7
SP - 564
EP - 573
%@ 1869-1951
Y1 - 2014
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1300264
Abstract: We propose a systematic ECG quality classification method based on a kernel support vector machine (KSVM) and genetic algorithm (GA) to determine whether ECGs collected via mobile phone are acceptable or not. This method includes mainly three modules, i.e., lead-fall detection, feature extraction, and intelligent classification. First, lead-fall detection is executed to make the initial classification. Then the power spectrum, baseline drifts, amplitude difference, and other time-domain features for ECGs are analyzed and quantified to form the feature matrix. Finally, the feature matrix is assessed using KSVM and GA to determine the ECG quality classification results. A Gaussian radial basis function (GRBF) is employed as the kernel function of KSVM and its performance is compared with that of the Mexican hat wavelet function (MHWF). GA is used to determine the optimal parameters of the KSVM classifier and its performance is compared with that of the grid search (GS) method. The performance of the proposed method was tested on a database from PhysioNet/Computing in Cardiology Challenge 2011, which includes 1500 12-lead ECG recordings. True positive (TP), false positive (FP), and classification accuracy were used as the assessment indices. For training database set A (1000 recordings), the optimal results were obtained using the combination of lead-fall, GA, and GRBF methods, and the corresponding results were: TP 92.89%, FP 5.68%, and classification accuracy 94.00%. For test database set B (500 recordings), the optimal results were also obtained using the combination of lead-fall, GA, and GRBF methods, and the classification accuracy was 91.80%.
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