XXXX Classification in the 12-Lead ECG using Atrial Waves Detection and a Clinically Based Tree Scheme.

@entity1 @entity2917 @entity956 @entity955 @entity4710
UNASSIGNED: Specific @entity4710 ( @entity4710 ) classification using surface ECG is considered a challenging task, since the atrial electrical activity (AEA) waves, which are a crucial element for obtaining diagnosis, are frequently hidden. In this paper we present a fully automated @entity4710 classification method that embeds 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. According to these features, an initial AEA-wave search window is defined and a single AEA-wave is detected. Using a synthetic Gaussian 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 @entity955 , @entity956 , @entity2917 ( @entity2917 ), @entity2917 ( @entity2917 ), or sinus rhythm is performed in the framework of a clinically oriented decision tree. A study was performed to evaluate the classification from 68 @entity1 (26 were used for the classifier's design, 42 were used for its validation). Average sensitivity of 83.21% [95% confidence interval (CI): 79.33-86.49%], average specificity of 95.80% (95% CI: 94.73-96.67%), and average 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.
