Cardiac arrhythmia classification using autoregressive modeling

Dingfei Ge, Narayanan Srinivasan, Shankar M Krishnan, Dingfei Ge, Narayanan Srinivasan, Shankar M Krishnan

Abstract

Background: 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).

Methods: 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.

Results: 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.

Conclusion: 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.

Figures

Figure 1
Figure 1
GLM-based classification algorithm
Figure 2
Figure 2
SNR for various AR model orders
Figure 3
Figure 3
A patient ECG and simulated ECG having NSR
Figure 4
Figure 4
A patient ECG and simulated ECG with APC
Figure 5
Figure 5
A patient ECG and simulated ECG with PVC
Figure 6
Figure 6
A patient ECG and simulated ECG with SVT
Figure 7
Figure 7
A patient ECG and simulated ECG with VT
Figure 8
Figure 8
A patient ECG and simulated ECG with VF

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Source: PubMed

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