A new hierarchical method for inter-patient heartbeat classification using random projections and RR intervals

BioMedical Engineering OnLine

Huifang Huang¹  Jie Liu¹  Qiang Zhu¹  Ruiping Wang¹  Guangshu Hu² 

[1] Department of Biomedical Engineering, School of Computer and Information Technology, Beijing Jiaotong University, 3 Shang Yuan Cun, Hai Dian District, Beijing, China

[2] Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China

关键词: Supraventricular ectopic beat (SVEB);    Ventricular ectopic beat (VEB);    Ensemble;    Support vector machine;    Random projection;    Heartbeat classification;   

Others  :  809228
DOI  :  10.1186/1475-925X-13-90

 received in 2014-03-20, accepted in 2014-06-23,  发布年份 2014

【 摘 要 】

Background

The inter-patient classification schema and the Association for the Advancement of Medical Instrumentation (AAMI) standards are important to the construction and evaluation of automated heartbeat classification systems. The majority of previously proposed methods that take the above two aspects into consideration use the same features and classification method to classify different classes of heartbeats. The performance of the classification system is often unsatisfactory with respect to the ventricular ectopic beat (VEB) and supraventricular ectopic beat (SVEB).

Methods

Based on the different characteristics of VEB and SVEB, a novel hierarchical heartbeat classification system was constructed. This was done in order to improve the classification performance of these two classes of heartbeats by using different features and classification methods. First, random projection and support vector machine (SVM) ensemble were used to detect VEB. Then, the ratio of the RR interval was compared to a predetermined threshold to detect SVEB. The optimal parameters for the classification models were selected on the training set and used in the independent testing set to assess the final performance of the classification system. Meanwhile, the effect of different lead configurations on the classification results was evaluated.

Results

Results showed that the performance of this classification system was notably superior to that of other methods. The VEB detection sensitivity was 93.9% with a positive predictive value of 90.9%, and the SVEB detection sensitivity was 91.1% with a positive predictive value of 42.2%. In addition, this classification process was relatively fast.

Conclusions

A hierarchical heartbeat classification system was proposed based on the inter-patient data division to detect VEB and SVEB. It demonstrated better classification performance than existing methods. It can be regarded as a promising system for detecting VEB and SVEB of unknown patients in clinical practice.

【 授权许可】

   
2014 Huang et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

Figure 8.

Figure 9.

Figure 10.

【 参考文献 】

• [1]de Chazal P, O’Dwyer M, Reilly RB: Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Trans Biomed Eng 2004, 51:1196-1206.
• [2]Reisner AT, Clifford GD, Mark RG: The physiological basis of the electrocardiogram. In Advanced methods and tools for ECG data analysis. Edited by Clifford GD, Azuaje F, McSharry PE. Boston: Artech House Publishers; 2006:1-25.
• [3]Inan OT, Giovangrandi L, Kovacs GTA: Robust neural-network based classification of premature ventricular contractions using wavelet transform and timing interval features. IEEE Trans Biomed Eng 2006, 53:2507-2515.
• [4]Minhas FA, Arif M: Robust electrocardiogram (ECG) beat classification using discrete wavelet transform. Physiol Meas 2008, 29:555-570.
• [5]Melgani F, Bazi Y: Classification of electrocardiogram signals with support vector machines and particle swarm optimization. IEEE Trans Inf Technol Biomed 2008, 12:667-677.
• [6]Jekova I, Bortolan G, Christov I: Assessment and comparison of different methods for heartbeat classification. Med Eng Phys 2008, 30:248-257.
• [7]Yu SN, Chou KT: Selection of significant independent components for ECG beat classification. Expert Syst Appl 2009, 36:2088-2096.
• [8]Yu SN, Chen YH: Noise-tolerant electrocardiogram beat classification based on higher order statistics of subband components. Artif Intell Med 2009, 46:165-178.
• [9]Pasolli E, Melgani F: Active learning methods for electrocardiographic signal classification. IEEE T Inf Technol Biomed 2010, 14:1405-1416.
• [10]Javadia M, Abbaszadeh Aranib SAA, Sajedina A, Ebrahimpourb R: Classification of ECG arrhythmia by a modular neural network based on mixture of experts and negatively correlated learning. Biomed Signal Process 2013, 8:289-296.
• [11]Llamedo-Soria M, Martinez JP: Heartbeat classification using feature selection driven by database generalization criteria. IEEE Trans Biomed Eng 2011, 58:616-625.
• [12]de Chazal P, Reilly RB: A patient-adapting heartbeat classifier using ECG morphology and heartbeat interval features. IEEE Trans Biomed Eng 2006, 53:2535-2543.
• [13]Jiang W, Kong S: Block-based neural networks for personalized ECG signal classification. IEEE Trans Biomed Eng 2007, 18:1750-1761.
• [14]Ince T, Kiranyaz S, Gabbouj M: A generic and robust system for automated patient-specific classification of ecg signals. IEEE Trans Biomed Eng 2009, 56:1415-1426.
• [15]Association for the Advancement of Medical Instrumentation: Testing and Reporting Performance Results of Cardiac Rhythm and ST Segment Measurement Algorithms. Arlington: ANSI/AAMI EC38:1998; 1998.
• [16]Mark R, Moody G: MIT-BIH arrhythmia database. 1997. http://www.physionet.org/physiobank/database/mitdb/
• [17]Rodríguez-Sotelo JL, Castellanos-Domínguez G, Acosta-Medina CD: Recognition of Cardiac Arrhythmia by Means of Beat Clustering on ECG-Holter Recordings. In Advances in Electrocardiograms - Methods and Analysis. Edited by Millis RM. Croatia: InTech Open Access Publisher; 2011:221-250.
• [18]Llamedo M, Martinez JP: An ECG classification model based on multilead wavelet transform features. Comput Cardiol 2007, 34:105-108.
• [19]Mar T, Zaunseder S, Martinez JP: Optimization of ECG classification by means of feature selection. IEEE Trans Biomed Eng 2011, 58:2168-2177.
• [20]de Lannoy G, Francois D, Delbeke J, Verleysen M: Weighted SVMs and feature relevance assessment in supervised heart beat classification. Commun Comput Inf Sci 2011, 127:212-223.
• [21]Doquire G, de Lannoy G, Francois D, Verleysen M: Feature selection for supervised interpatient heart beat classification. Comput Intell Neurosci 2011, 643816. Available: http://dx.doi.org/10.1155/2011/643816
• [22]de Lannoy G, Francois D, Delbeke J, Verleysen M: Weighted conditional random fields for supervised interpatient heartbeat classification. IEEE Trans Biomed Eng 2012, 59:241-247.
• [23]Zhang ZC, Dong J, Luo XQ, Choi KS, Wu XJ: Heartbeat classification using disease-specific feature selection. Comput Biol Med 2014, 46:79-89.
• [24]Park KS, Cho BH, Lee DH, Song SH, Lee JS, Chee YJ, Kim IY, Kim SI: Hierarchical Support Vector Machine Based Heartbeat Classification Using Higher Order Statistics and Hermite Basis Function. Bologna: Institute of Electrical and Electronics Engineers; 2008:229-232. [Proceedings of the 35th Annual Computers in Cardiology Conference: 14–17 September 2008]
• [25]Bingham E, Mannila H: Random Projection in Dimensionality Reduction: Applications to Image and Text Data. San Francisco: Association for Computing Machinery; 2001:245-250. [Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining: 26–29 August 2001]
• [26]Candès EJ, Wakin MB: An introduction to compressive sampling. IEEE Signal Proc Mag 2008, 25:21-30.
• [27]Vapnik V: Statistical Learning Theory. New York: John Wiley & Sons Inc.; 1998.
• [28]Chang CC, Lin CJ: LIBSVM: a library for support vector machines. ACM TIST 2011, 2(27):1-27. Software available online: http://www.csie.ntu.edu.tw/~cjlin/libsvm
• [29]Hoai LT, Osowski S: Integration of multiple neural classifiers for heart beat recognition. Int J Comput Math Elect Electron Eng 2005, 24:648-661.
• [30]Osowski S, Markiewicz T, Hoai LT: Recognition and classification system of arrhythmia using ensemble of neural networks. Measurement 2008, 41:610-617.
• [31]Osowski S, Siwek K, Siroic R: Neural system for heartbeats recognition using genetically integrated ensemble of classifiers. Comput Biol Med 2011, 41:173-180.
• [32]Javadi M, Ebrahimpour R, Sajedin A, Faridi S, Zakernejad S: Improving ECG classification accuracy using an ensemble of neural network modules. PLoS One 2011, 6:e24386. doi:10.1371/journal.pone.0024386
• [33]Zhou ZH: Ensemble Learning. In Encyclopedia of Biometrics. Edited by Li SZ, Jain AK. Berlin: Springer-Verlag New York Inc; 2009:270-273.
• [34]Li CW, Zheng CX, Tai CF: Detection of ECG characteristic points using wavelet transform. IEEE Trans Biomed Eng 1995, 42:21-28.
• [35]Afonso VX, Tompkins WJ, Nguyen TQ, Luo S: ECG beat detection using filter banks. IEEE Trans Biomed Eng 1999, 46:556-564.
• [36]Yeh YC, Wang WJ: QRS complexes detection for ECG signal: the difference operation method. Comput Meth Prog Bio 2008, 91:245-254.