Key result
An autoregressive modeling and generalized linear model-based classification algorithm achieved accuracies ranging from 93.2% to 100% in detecting normal sinus rhythm and five cardiac arrhythmias.
Autoregressive modeling combined with a generalized linear model provides a computationally simple and highly accurate method for classifying multiple cardiac arrhythmias from ECG signals.
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Promising ECG arrhythmia classification accuracy shown; leaves open prospective clinical validation before any practice integration.
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.
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Ge et al. (2002) studied Cardiac arrhythmias (n=1,216). Autoregressive (AR) modeling and Generalized Linear Model (GLM) classification was evaluated on Classification accuracy for normal sinus rhythm and various arrhythmias. An autoregressive modeling and generalized linear model-based classification algorithm achieved accuracies ranging from 93.2% to 100% in detecting normal sinus rhythm and five cardiac arrhythmias.
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