Description

Specific supraventricular tachycardia (SVT) classifi-cation using surface ECG is considered a challenging task, since the atrial electrical activity (AEA) waves, which are a crucial ele-ment for obtaining diagnosis, are frequently hidden. In this paper, we present a fully automated SVT classification method that em-beds our recently developed hidden AEA detector in a clinically based tree scheme. The process begins with initial noise removal and QRS detection. Then, ventricular features are extracted. Ac-cording to these features, an initial AEA-wave search window is defined and a single AEA-wave is detected. Using a synthetic Gaus-sian signal and a linear combination of 12-lead ECG signals, all AEA-waves are detected. In accord with the atrial and ventricular information found, classification to atrial fibrillation, atrial flutter, atrioventricular nodal reentry tachycardia, atrioventricular reen-try tachycardia, or sinus rhythm is performed in the framework of a clinically oriented decision tree. A study was performed to evaluate the classification from 68 patients (26 were used for the classifier’s design, 42 were used for its validation). Average sen-sitivity of 83.21% [95% confidence interval (CI): 79.33–86.49%] , average specificity of 95.80% (95% CI: 94.73–96.67%), and aver-age accuracy of 93.29% (95% CI: 92.13–94.28%) were achieved compared to the definite diagnosis. In conclusion, the presented method may serve as a valuable decision support tool, allowing accurate detection of SVTs using noninvasive means.