Abstract
Purpose
Automatic heartbeat classification is an important technique to assist doctors to identify ectopic heartbeats in long-term ECG recording. In this paper, we employed a multi-lead fused classification schema to improve the performance of heartbeat classification.
Methods
In this paper, we introduce a multi-lead fused classification schema, in which a multi-class heartbeat classification task is decomposed into a serials of one-versus-one (OvO) support vector machine (SVM) binary classifiers, then the corresponding OvO binary classifiers of all leads are fused based on the decision score of each binary classifier, the final label is predicted by voting the fused OvO classifiers. The ECG features adopted include inter-beat and intra-beat intervals, amplitude morphology, area morphology, morphological distance and wavelet coefficients. The electrocardiograms (ECG) from the MIT-BIH arrhythmia database (MIT-BIH-AR) are used to evaluate the proposed fusion method. Following the recommendation of the Advancement of Medical Instrumentation (AAMI), all the heartbeat samples of MIT-BIH-AR are grouped into four classes, namely, normal or bundle branch block (N), supraventricular ectopic (S), ventricular ectopic (V) and fusion of ventricular and normal (F). The division of training and testing data complies with the inter-patient schema.
Results
Experimental results show that the average classification accuracy of the proposed feature selection method is 87.88%, the sensitivities for the classes N, S, V and F are 88.63%, 74.23%, 88.06% and 73.45% respectively, and the corresponding positive predictive values are 98.54%, 59.76%, 82.33% and 6.96% respectively.
Conclusions
The proposed method demonstrates better performance than the existing fusion methods.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Martis RJ, Chakraborty C, Ray AK. A two-stage mechanism for registration and classification of ECG using gaussian mixture model. Pattern Recogn. 2009; 42(11):2979–88.
Minhas FU, Arif M. Robust electrocardiogram (ECG) beat classification using discrete wavelet transform. Physiol Meas. 2008; 29(5):555–70.
Llamedo M, Martínez JP. Heartbeat classification using feature selection driven by database generalization criteria. IEEE T Biomed Eng. 2011; 58(3):616–25.
Ye C, Kumar BV, Coimbra MT. Heartbeat classification using morphological and dynamic features of ecg signals. IEEE T Biomed Eng. 2012; 59(10):2930–41.
de Lannoy G, Francois D, Delbeke J, Verleysen M. Weighted conditional random fields for supervised interpatient heartbeat classification. IEEE T Biomed Eng. 2012; 59(1):241–7.
de Chazal P, O’Dwyer M, Reilly RB. Automatic classification of heartbeats using ecg morphology and heartbeat interval features. IEEE T Biomed Eng. 2004; 51(7):1196–206.
Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation. 2000; 101(23):e215–20.
Testing and reporting performance results of cardiac rhythm and ST segment measurement algorithms. AAMI. 2013. https://standards.aami.org/kws/public/projects/project/details?project_id=30. Accessed 18 Nov 2014.
Wiens J, Guttag JV. Active learning applied to patient-adaptive heartbeat classification. Conf Proc Neural Inf Process Syst. 2010; 1:2442–50.
ECGpedia, a free electrocardiography (ECG) tutorial and textbook. http://en.ecgpedia.org/wiki/Main_Page. Accessed 16 Nov 2014.
Christov I, Gómez-Herrero G, Krasteva V, Jekova I, Gotchev A, Egiazarian K. Comparative study of morphological and timefrequency ECG descriptors for heartbeat classification. Med Eng Phys. 2006; 28(9):876–87.
Kittler J, Hatef M, Duin RPW, Matas J. On combining classifiers. IEEE T Pattern Anal Mach Intell. 1998; 20(3):226–39.
Poh N, Kittler J. A unified framework for biometric expert fusion incorporating quality measures. IEEE T Pattern Anal Mach Intell. 2012; 34(1):3–18.
Terrades OR, Valveny E, Tabbone S. Optimal classifier fusion in a non-bayesian probabilistic framework. IEEE T Pattern Anal Mach Intell. 2009; 31(9):1630–44.
Moody GB, Mark RG. The impact of the mit-bih arrhythmia database. IEEE Eng Med Biol Mag. 2001; 20(3):45–50.
de Chazal P, Reilly RB. A patient-adapting heartbeat classifier using ecg morphology and heartbeat interval features. IEEE T Biomed Eng. 2006; 53(12):2535–43.
Chang C-C, Lin C-J. LIBSVM: a library for support vector machines, software. http://www.csie.ntu.edu.tw/-cjlin/libsvm. Accessed 31 Dec 2010.
Wu M, Ye J. A small sphere and large margin approach for novelty detection using training data with outliers. IEEE T Pattern Anal Mach Intell. 2009; 31(11):2088–92.
Wu T-F, Lin C-J, Weng RC. Probability estimates for multi-class classification by pairwise coupling. J Mach Learn Res. 2004; 5:975–1005.
Ince T, Kiranyaz S, Gabbouj M. Ageneric and robust system for automated patient-specific classification of ecg signals. IEEE T Biomed Eng. 2009; 56(5):1415–26.
Llamedo M, Martínez JP. An automatic patient-adapted ecg heartbeat classifier allowing expert assistance. IEEE T Biomed Eng. 2012; 59(8):2312–20.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zhang, Z., Luo, X. Heartbeat classification using decision level fusion. Biomed. Eng. Lett. 4, 388–395 (2014). https://doi.org/10.1007/s13534-014-0158-7
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13534-014-0158-7