Heartbeat classification using disease-specific feature selection

Computers in Biology and Medicine

Volumn 46, Issue 1, 2014, Pages 79-89

  Zhang, Zhancheng ^a   Dong, Jun ^a   Luo, Xiaoqing ^b   Choi, Kup Sze ^c   Wu, Xiaojun ^b  

^a SUZHOU INSTITUTE OF NANO TECH AND NANO BIONICS   (China)

^b JIANGNAN UNIVERSITY   (China)

^c HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

Author keywords

Disease specific; Feature selection; Heartbeat classification; Support vector machine

Indexed keywords

DISEASE SPECIFIC; FEATURE SELECTION; HEARTBEAT CLASSIFICATION; SUPPORT VECTOR MACHINE;

FEMALE; HEART DISEASES; HEART SOUNDS; HUMANS; MALE; MODELS, CARDIOVASCULAR; MYOCARDIAL CONTRACTION; SENSITIVITY AND SPECIFICITY; SIGNAL PROCESSING, COMPUTER-ASSISTED; SUPPORT VECTOR MACHINES;

EID: 84893972699     PISSN: 00104825     EISSN: 18790534     Source Type: Journal    
DOI: 10.1016/j.compbiomed.2013.11.019     Document Type: Article

References (31)

1. Amann A., Tratnig R., Unterkofler K. Detecting ventricular fibrillation by time-delay methods. IEEE Trans. Biomed. Eng. 2007, 54(1):174-177.
2. Irusta U., Ruiz J., de Gauna S., Eftestol T., Kramer-Johansen J. A least mean-square filter for the estimation of the cardiopulmonary resuscitation artifact based on the frequency of the compressions. IEEE Trans. Biomed. Eng. 2009, 56(4):1052-1062.
3. Aramendi E., Irusta U., Pastor E., Bodegas A., Benito F. Ecg spectral and morphological parameters reviewed and updated to detect adult and paediatric life-threatening arrhythmia. Physiol. Meas. 2010, 31(6):749.
4. Martis R.J., Chakraborty C., Ray A.K. A two-stage mechanism for registration and classification of ECG using gaussian mixture model. Pattern Recognit. 2009, 42(11):2979-2988.
5. ul Amir Afsar Minhas F., Arif M. Robust electrocardiogram beat classification using discrete wavelet transform. Physiol. Meas. 2008, 29(5):555-570.
6. de Chazal P., O'Dwyer M., Reilly R. Automatic classification of heartbeats using ecg morphology and heartbeat interval features. IEEE Trans. Biomed. Eng. 2004, 51(7):1196-1206.
7. Llamedo M., Martínez J. Heartbeat classification using feature selection driven by database generalization criteria. IEEE Trans. Biomed. Eng. 2011, 58(3):616-625.
8. Ye C., Kumar B., Coimbra M. Heartbeat classification using morphological and dynamic features of ECG signals. IEEE Trans. Biomed. Eng. 2012, 59(10):2930-2941.
9. de Lannoy G., Francois D., Delbeke J., Verleysen M. Weighted conditional random fields for supervised interpatient heartbeat classification. IEEE Trans. Biomed. Eng. 2012, 59(1):241-247.
10. Goldberger A.L., Amaral L.A.N., Glass L., Hausdorff J.M., Ivanov P.C., Mark R.G., Mietus J.E., Moody G.B., Peng C.-K., Stanley H.E. Physiobank, physiotoolkit, and physionet. components of a new research resource for complex physiologic signals. Circulation 2000, 101(23):e215-e220.
11. Moody G., Mark R. The impact of the MIT-BIH arrhythmia database. IEEE Eng. Med. Biol. Mag. 2001, 20(3):45-50.
12. de Chazal P., Reilly R. A patient-adapting heartbeat classifier using ecg morphology and heartbeat interval features. IEEE Trans. Biomed. Eng. 2006, 53(12):2535-2543.
13. Ince T., Kiranyaz S., Gabbouj M. A generic and robust system for automated patient-specific classification of ECG signals. IEEE Trans. Biomed. Eng. 2009, 56(5):1415-1426.
14. J. Wiens, J.V. Guttag, Active learning applied to patient-adaptive heartbeat classification, in: Advances in Neural Information Processing Systems, 2010, pp. 2442-2450.
15. Llamedo M., Martínez J. An automatic patient-adapted ECG heartbeat classifier allowing expert assistance. IEEE Trans. Biomed. Eng. 2012, 59(8):2312-2320.
16. Testing and Reporting Performance Results of Cardiac Rhythm and ST-Segment Measurement Algorithms, ANSI/AAMI Std. EC57:1998, Rev. 2008.
17. ECGpedia, A Free Electrocardiography (ECG) Tutorial and Textbook. URL 〈〉. http://en.ecgpedia.org/wiki/Main_Page.
18. Christov I., Gómez-Herrero G., Krasteva V., Jekova I., Gotchev A., Egiazarian K. Comparative study of morphological and time-frequency ECG descriptors for heartbeat classification. Med. Eng. Phys. 2006, 28(9):876-887.
19. Guyon I., Elisseeff A. An introduction to variable and feature selection. J. Mach. Learn. Res. 2003, 3:1157-1182.
20. H. Zhu, X. Hu, J. Dong, An improved QRS detection approach based on two-lead bi-referring, in: The IASTED 2012 African Conferences, 2012.
21. Zhu H., Dong J. An R-peak detection method based on peaks of Shannon energy envelope. Biomed. Signal Process. Control 2013, 8(5):466-474.
22. Ghaffari A., Homaeinezhad M., Akraminia M., Atarod M., Daevaeiha M. A robust wavelet-based multi-lead electrocardiogram delineation algorithm. Med. Eng. Phys. 2009, 31(10):1219-1227.
23. Martínez J., Almeida R., Olmos S., Rocha A., Laguna P. A wavelet-based ECG delineator. evaluation on standard databases. IEEE Trans. Biomed. Eng. 2004, 51(4):570-581.
24. Fan R.-E., Chang K.-W., Hsieh C.-J., Wang X.-R., Lin C.-J. LIBLINEAR. a library for large linear classification. J. Mach. Learn. Res. 2008, 9:1871-1874.
25. Y.-W. Chen, C.-J. Lin, Combining SVMS with various feature selection strategies, in: Feature Extraction, Springer, 2006, pp. 315-324.
26. B. Sarojini, N. Ramaraj, S. Nickolas, Enhancing the performance of LIBSVM classifier by kernel f-score feature selection, in: Communications in Computer and Information Science, vol. 40, Noida, India, 2009, pp. 533-543.
27. Wu M., Ye J. A small sphere and large margin approach for novelty detection using training data with outliers. IEEE Trans. Pattern Anal. Mach. Intell. 2009, 31:2088-2092.
28. Kittler J., Hatef M., Duin R.P.W., Matas J. On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 1998, 20(3):226-239.
29. Wu T.-F., Lin C.-J., Weng R.C. Probability estimates for multi-class classification by pairwise coupling. J. Mach. Learn. Res. 2004, 5:975-1005.
30. A.Y. Ng, Feature selection, L1 vs. L2 regularization, and rotational invariance, in: Proceedings of the 21st International Conference on Machine Learning, ACM, 2004, pp. 78-85.
31. T. Evgeniou, M. Pontil, Regularized multi-task learning, in: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2004, pp. 109-117.