Delta Wave Detection Method of Wolff-Parkins on-White Syndrome Based on Electrocardiogram

The main indicator in an Electrocardiogram (ECG) signal that marks the existence of Wolff-Parkins on-White (WPW) syndrome is the slurred upstroke. This index is called the Delta wave and appears on the signal morphology due to the existence of a pathological conduction pathway from the atria to the ventricles. Therefore, during ventricular activity, a QRS complex with modified morphology will result. This paper presents a new Delta wave detection algorithm based on the ECG signal. The proposed method is based on analysis of the QRS complex to accurately identify the location of the Delta waves. The identification technique consists in the implementation of a detection algorithm by analyzing the slurred upstroke between the Q and R peaks. In the last step, an additional check is performed based on the area of a portion of the R wave and the duration of the QRS complex. This step has the role to determine whether the identified Delta wave is representative of the preexcitation pattern. The algorithm was tested on 48 samples of ECG signals that were retrieved from two databases from the PhysioN et platform. The samples tested contain normal heartbeats and Wolff-Parkins on-White pathology. The purpose of these signals is to evaluate the robustness of the proposed algorithm. After testing the proposed method, the accuracy obtained is 100%. Testing was also performed on a signal that was not annotated with preexcitation syndrome, but it was mentioned that its morphology resembled Wolff-Parkins on-White pathology. In this case, Delta waves were identified on all 8 samples analyzed. Therefore, the proposed method for detecting Delta waves based on the ECG signal provides 1 00% accuracy, demonstrating superior effectiveness in distinguishing abnormal R waves from normal ones in the context of WPW detection.