A novel feature extraction method for the localization of accessory pathways in patients with Wolff-Parkinson-White syndrome

Abstract

Background and objectiveThe localization of accessory pathways (APs) in patients with Wolff-Parkinson-White (WPW) syndrome using surface electrocardiogram (ECG) is often entirely subjective. This study aimed to develop a novel, more automated, and non-invasive localization method to differentiate right and left APs. Materials and methodsThe participants were 31 patients (aged 8–69 y, mean age: 31.19 ± 14.69 y, 32.3 % women) with manifest WPW syndrome who were treated successfully in the first ablation session. The novel localization of APs was based on feature extraction using McSharry’s model, and SFS-LOV, a combination of three methods (sequential forward selection (SFS), leave-one-subject-out cross-validation (LOSO), and majority voting). The k-nearest neighbor (KNN) and support vector machine (SVM) classifier were separately used in the SFS-LOV method. In this method, the features were extracted from three segments of the ECG signals. Each segment was started from the onset of the P-wave and terminated with the offset of the T-wave. The majority voting was applied to the results of the classifier on the segments of each participant. ResultsThe proposed method differentiated the right from the left APs with an accuracy of 87% (sensitivity: 80%, specificity: 94%). This was achieved by feeding four selected parameters of McSharry’s model into the KNN classifier. These selected parameters were estimated from the lead V4 of the ECG signals. ConclusionThe parameters of McSharry’s model can semi-automatically localize the right and left APs in patients with manifest WPW syndrome.