Cardiac arrhythmia classification using autoregressive modeling
Tóm tắt
Computer-assisted arrhythmia recognition is critical for the management of cardiac disorders. Various techniques have been utilized to classify arrhythmias. Generally, these techniques classify two or three arrhythmias or have significantly large processing times. A simpler autoregressive modeling (AR) technique is proposed to classify normal sinus rhythm (NSR) and various cardiac arrhythmias including atrial premature contraction (APC), premature ventricular contraction (PVC), superventricular tachycardia (SVT), ventricular tachycardia (VT) and ventricular fibrillation (VF). AR Modeling was performed on ECG data from normal sinus rhythm as well as various arrhythmias. The AR coefficients were computed using Burg's algorithm. The AR coefficients were classified using a generalized linear model (GLM) based algorithm in various stages. AR modeling results showed that an order of four was sufficient for modeling the ECG signals. The accuracy of detecting NSR, APC, PVC, SVT, VT and VF were 93.2% to 100% using the GLM based classification algorithm. The results show that AR modeling is useful for the classification of cardiac arrhythmias, with reasonably high accuracies. Further validation of the proposed technique will yield acceptable results for clinical implementation.
Tài liệu tham khảo
Goldschlager N, Goldman MJ: Principles of Clinical Electrocardiography. Appleton and Lange 1989.
Barro S, Ruiz R, Cabello D, Mira J: Algorithmic sequential decision-making in the frequency domain for life threatening ventricular arrhythmias and imitative artefacts: a diagnostic system. J Biomed Eng 1989, 11: 320–328.
Coast DA, Stren RM, Cano GG, Briller SA: An approach to cardiac arrhythmia analysis using hidden Markov models. IEEE Trans Biomed Eng 1990, 37: 826–836. 10.1109/10.58593
Thakor NV, Zhu YS, Pan KY: Ventricular tachycardia and fibrillation detection by a sequential hypothesis testing algorithm. IEEE Trans Biomed Eng 1990, 37: 837–843. 10.1109/10.58594
Caswell SA, Kluge KS, Chiang CMJ: Pattern recognition of cardiac arrhythmias using two intracardiac channels. Proc Comp Cardiol 1993, 181–184.
Zhou SH, Rautaharju PM, Calhoun HP: Selection of a reduced set of parameters for classification of ventricular conduction defects by cluster analysis. Proc Comp Cardiol 1993, 879–882.
Afonoso VX, Tompkins WJ: Detecting ventricular fibrillation: Selecting the appropriate time-frequency analysis tool for the application. IEEE Eng Med Biol Mag 1995, 14: 152–159. 10.1109/51.376752
Ham FM, Han S: Classification of cardiac arrhythmias using fuzzy ARTMAP. IEEE Trans Biomed Eng 1996, 43: 425–430. 10.1109/10.486263
Finelli CJ: The time-sequenced adaptive filter for analysis of cardiac arrhythmias in intraventricular electrograms. IEEE Trans Biomed Eng 1996, 43: 811–819. 10.1109/10.508543
Chen SW, Clarkson PM, Fan Q: A robust sequential detection algorithm for cardiac arrhythmia classification. IEEE Trans Biomed Eng 1996, 43: 1120–1125. 10.1109/10.541254
Guvenir HA, Acar B, Demiroz G, Cekin A: A supervised learning algorithm for arrhythmia analysis. Comp Cardiol 1997, 24: 433–436.
Minami KC, Nakajima H, Toyoshima T: Real-time discrimination of ventricular tachyarrythmia with Fourier-transform neural network. IEEE Trans Biomed Eng 1999, 46: 179–185. 10.1109/10.740880
Xu SZ, Yi SZ, Thakor NV, Wang ZZ: Detecting ventricular tachycardia and fibrillation by complexity measure. IEEE Trans Biomed Eng 1999, 46: 548–555. 10.1109/10.759055
Melo SL, Caloba LP, Nadal J: Arrhythmia analysis using artificial neural network and decimated electrocardiographic data. Comp Cardiol 2000, 27: 73–76.
Small M, Yu DJ, Grubb N, Simonotto J, Fox KAA, Harrison RG: Automatic identification and recording of cardiac arrhythmia. Comp Cardiol 2000, 27: 355–358.
Chen SW: Two-stage discrimination of cardiac arrhythmias using a total least squares-based prony modeling algorithm. IEEE Trans Biomed Eng 2000, 47: 1317–1326. 10.1109/10.827310
Mukhopadhyay S, Sircar P: Parametric modelling of ECG signal. Med Biol Eng Comp 1996, 34: 171–173.
Pinna GD, Maestri R, Cesare AD: Application of time series spectral analysis theory: analysis of cardiovascular variability signals. Med Biol Eng Comp 1996, 34: 142–148.
Bennett FM, Chrisstini DJ, Ahmed H, Lutchen K: Time series modeling of heart rate dynamics. Proc Comp Cardiol 1993, 273–276.
Arnold M, Miltner WHR, Witte H: Adaptive AR modeling of nonstationary time series by means of Kalman filtering. IEEE Trans Biomed Eng 1998, 45: 553–562. 10.1109/10.668741
Mainardi LT, Bianchi AM, Baselli G, Cerutti S: Pole-tracking algorithms for the extraction of time-variant heart rate variability spectral parameters. IEEE Trans Biomed Eng 1995, 42: 250–258. 10.1109/10.364511
Lin KP, Chang WH: QRS feature extraction using linear prediction. IEEE Trans Biomed Eng 1989, 36: 1050–1055. 10.1109/10.40806
Marple SL: Digital spectral analysis with applications. Prentice Hall, Englewood Cliffs, New Jersey 1987.
Ljung L: System Identification: Theory for the user. Prentice Hall, Englewood Cliffs, New Jersey, 1999.
Anderson CW, Stolz EA, Shamssunder S: Multivariate autoregressive models for classification of spontaneous electroencephalographic signals during mental tasks. IEEE Trans Biomed Eng 1998, 45: 277–286. 10.1109/10.661153
Miller AS, Blott BH, Hames TK: Review of neural network applications in medical imaging and signal processing. Med Biol Eng Comp 1992, 30: 449–464.
Ramirez-Rodriguez CA, Hernandez-Silveira MA: Multi-thread implementation of a fuzzy neural network for automatic ECG arrhythmia detection. Comp Cardiol 2001, 28: 297–300.
Silipo R, Marchesi C: Artificial neural networks for automatic ECG analysis. IEEE Trans Sig proc 1998, 46: 1417–1425. 10.1109/78.668803
McCullagh P, Nelder JA: Generalized Linear Model. Chapman and Hall, London, 1989.
Tompkins W: Biomedical Digital Signal Processing. Prentice Hall, Englewood Cliffs, New Jersey 1993.