Computer methods and programs in biomedicine · 2018
Wide complex tachycardia discrimination using dynamic time warping of ECG beats.
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Mazandarani F Niknejad, Mohebbi M
Аннотация
BACKGROUND AND OBJECTIVE: Automatic processing and accurate diagnosis of wide complex tachycardia (WCT) arrhythmia groups using electrocardiogram signals (ECG) remains a challenge. WCT arrhythmia consists of two main groups: ventricular tachycardia (VT) and supraventricular tachycardia with aberrancy (SVT-A). These two groups have similar morphologies in the realm of ECG signals. VT and SVT-A arrhythmias originate from the ventricle and atrium, respectively. Hence, inaccurate diagnosis of SVT-A instead of VT can be fatal.
METHODS: In this paper, we present a novel algorithm using dynamic time warping (DTW) to discriminate between VT and SVT-A arrhythmias. This method includes pre-processing, best template search (BTS), and classifier modules. The first module, pre-processing, is responsible for filtering, R-wave detection, and beat detection of ECG signals. The second module, BTS, automatically extracts the minimum possible number of signals as a template from the entire training dataset using an intelligent algorithm. These template signals have the greatest morphological difference, which leads to accurate WCT discrimination. Finally, a 1NN classifier categorizes the test data using DTW distance.
RESULTS: Our proposed method was evaluated on an ECG signal database consisting of 171 subjects. The results showed that the proposed algorithm can accurately discriminate between VT, SVT-A, and normal subjects, and appears to be suitable for future use in clinical application. The obtained accuracy, sensitivity, specificity, and positive predictive values were 93.22%, 88.68%, 96.98%, and 90.27%, respectively.
CONCLUSION: The presented diagnostic method for discriminating VT and SVT-A, using only one ECG lead, is suitable for future clinical use. It can reduce needless therapeutic interventions and minimize risk for patients.
METHODS: In this paper, we present a novel algorithm using dynamic time warping (DTW) to discriminate between VT and SVT-A arrhythmias. This method includes pre-processing, best template search (BTS), and classifier modules. The first module, pre-processing, is responsible for filtering, R-wave detection, and beat detection of ECG signals. The second module, BTS, automatically extracts the minimum possible number of signals as a template from the entire training dataset using an intelligent algorithm. These template signals have the greatest morphological difference, which leads to accurate WCT discrimination. Finally, a 1NN classifier categorizes the test data using DTW distance.
RESULTS: Our proposed method was evaluated on an ECG signal database consisting of 171 subjects. The results showed that the proposed algorithm can accurately discriminate between VT, SVT-A, and normal subjects, and appears to be suitable for future use in clinical application. The obtained accuracy, sensitivity, specificity, and positive predictive values were 93.22%, 88.68%, 96.98%, and 90.27%, respectively.
CONCLUSION: The presented diagnostic method for discriminating VT and SVT-A, using only one ECG lead, is suitable for future clinical use. It can reduce needless therapeutic interventions and minimize risk for patients.