Full Text:   <3406>

Summary:  <2297>

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

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
Open peer comments

Journal of Zhejiang University SCIENCE C 2014 Vol.15 No.7 P.564-573

http://doi.org/10.1631/jzus.C1300264


ECG quality assessment based on a kernel support vector machine and genetic algorithm with a feature matrix


Author(s):  Ya-tao Zhang, Cheng-yu Liu, Shou-shui Wei, Chang-zhi Wei, Fei-fei Liu

Affiliation(s):  School of Control Science and Engineering, Shandong University, Jinan 250061, China; more

Corresponding email(s):   sswei@sdu.edu.cn

Key Words:  ECG quality assessment, Kernel support vector machine, Genetic algorithm, Power spectrum, Cross validation


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%.

基于非线性支持向量机和遗传算法的移动ECG质量评估

研究目的:为减少移动设备采集的ECG信号造成的误报警,避免远程心电监控中心的误诊和诊疗资源浪费,提高诊断准确率和效率,首先必须评估ECG信号质量。本文采用频域、时域相结合的ECG特征分析,结合非线性支持向量机(kernelsupportvectormachine, KSVM)和遗传算法,实现对ECG的质量分类。
创新要点:运用频域和时域相结合的ECG特征分析。对具有易于识别特征(如导联脱落)的ECG信号,直接依据该特征得出分类结果;对依据简单特征无法评判的ECG信号,提取复杂时、频域特征组成特征矩阵,运用KSVM进行分类。
方法提亮:根据ECG特征是否易于识别,分步骤采用根据特征直接分类和非线性支持向量机智能分类技术,降低算法复杂度和运算量(图1)。结合频域和时域,在ECG特征空间选择与扩展上进行了有效提升(公式4)。运用遗传算法优化了SVM参数。
重要结论:时、频域结合的特征分析能够较全面地反映ECG特征。KSVM智能分类技术能够有效提高分类精度。
ECG质量评估;非线性支持向量机;遗传算法;功率谱;交叉验证

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Ataman, E., Aatre, V., Wong, K., 1980. A fast method for real-time median filtering. IEEE Trans. Acoust. Speech Signal Process., 28(4):415-421.

[2]Biswal, B., Biswal, M.K., Dash, P.K., et al., 2013. Power quality event characterization using support vector machine and optimization using advanced immune algorithm. Neurocomputing, 103:75-86.

[3]Chen, Y., Yang, H., 2012. Self-organized neural network for the quality control of 12-lead ECG signals. Physiol. Meas., 33(9):1399-1418.

[4]Clifford, G.D., Moody, G.B., 2012. Signal quality in cardiorespiratory monitoring. Physiol. Meas., 33(9):E01.

[5]Clifford, G.D., Behar, J., Li, Q., et al., 2012. Signal quality indices and data fusion for determining clinical acceptability of electrocardiograms. Physiol. Meas., 33(9):1419-1433.

[6]Han, L., Kendall, G., 2003. Guided operators for a hyperheuristic genetic algorithm. LNCS, 2903:807-820.

[7]Hayn, D., Jammerbund, B., Schreier, G., 2012. QRS detection based ECG quality assessment. Physiol. Meas., 33(9):1449-1462.

[8]Johannesen, L., Galeotti, L., 2012. Automatic ECG quality scoring methodology: mimicking human annotators. Physiol. Meas., 33(9):1479-1489.

[9]Kasturiwale, H.P., Deshmukh, C.N., 2009. Quality assessment of ICA for ECG signal analysis. 2nd Int. Conf. on Emerging Trends in Engineering and Technology, p.73-75.

[10]Khazaee, A., Ebrahimzadeh, A., 2010. Classification of electrocardiogram signals with support vector machines and genetic algorithms using power spectral features. Biomed. Signal Process. Contr., 5(4):252-263.

[11]Kligfield, P., Gettes, L.S., Bailey, J.J., et al., 2007. Recommendations for the standardization and interpretation of the electrocardiogram. Part I: the electrocardiogram and its technology. A scientific statement from the American Heart Association Electrocardiography and Arrhythmias Committee, Council on Clinical Cardiology; the American College of Cardiology Foundation; and the Heart Rhythm Society endorsed by the International Society for Computerized Electrocardiology. J. Am. Coll. Cardiol., 49(10):1109-1127.

[12]Langley, P., di Marco, L.Y., King, S., et al., 2011. An algorithm for assessment of quality of ECGs acquired via mobile telephones. IEEE 38th Annual Scientific Conf. of Computing in Cardiology, p.281-284.

[13]Li, Q., Clifford, G.D., 2012. Signal quality and data fusion for false alarm reduction in the intensive care unit. J. Electrocardiol., 45(6):596-603.

[14]Liu, C.Y., Li, P., Zhao, L.N., et al., 2011. Real-time signal quality assessment for ECGs collected using mobile phones. IEEE 38th Annual Scientific Conf. of Computing in Cardiology, p.357-360.

[15]Moody, G.B., 2011. Physionet/Computing in Cardiology Challenge 2011. Available from http://physionet.org/challenge/2011/.

[16]Ramakrishnan, A.G., Saha, S., 1997. ECG coding by wavelet-based linear prediction. IEEE Trans. Biomed. Eng., 44(12):1253-1261.

[17]Redmond, S.J., Xie, Y., Chang, D., et al., 2012. Electrocardiogram signal quality measures for unsupervised telehealth environments. Physiol. Meas., 33(9):1517-1534.

[18]Suh, S.C., Gurupur, V.P., Tanik, M.M., 2011. Biomedical Engineering: Health Care Systems, Technology and Techniques. Springer, New York, USA, p.211-227.

[19]Thakor, N.V., Webster, J.G., Tompkins, W.J., 1984. Estimation of QRS complex power spectra for design of a QRS filter. IEEE Trans. Biomed. Eng., 31(11):702-706.

[20]Wang, X., Yang, C., Qin, B., et al., 2005. Parameter selection of support vector regression based on hybrid optimization algorithm and its application. J. Contr. Theory Appl., 3(4):371-376.

[21]Zaunseder, S., Huhle, R., Malberg, H., 2011. Assessing the usability of ECG by ensemble decision trees. IEEE 38th Annual Scientific Conf. of Computing in Cardiology, p.277-280.

[22]Zhang, J.H., 2012. Optimization of kernel function parameters SVM based on the GA. Adv. Mater. Res., 433-440:4124-4128.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE