Key result
New continuous wavelet transform algorithm detects ECG delta waves to aid WPW syndrome diagnosis.
Why the study?
Does a new continuous wavelet transform-based algorithm accurately detect delta waves in ECG signals of patients with WPW syndrome?
Does a new continuous wavelet transform-based algorithm accurately detect delta waves in ECG signals of patients with WPW syndrome?
A new continuous wavelet transform-based algorithm can detect delta waves in ECG signals, potentially assisting practitioners in diagnosing Wolff-Parkinson-White syndrome.
May aid delta-wave detection on ECG; leaves open prospective validation before clinical use.
The delta wave remains an important indicator to diagnose the WPW syndrome. In this paper, a new method of detection of delta wave in an ECG signal is proposed. Firstly, using the continuous wavelet transform, the P wave, the QRS complex and the T wave are detected, then their durations are computed after determination of the boundary location (onsets and offsets of the P, QRS and T waves). Secondly, the PR duration, the QRS duration and the upstroke of the QRS complex are used to determine the presence or absence of the delta wave. This algorithm has been tested on the Physionel database (ptbdb) in order to evaluate its robustness. It has been applied to clinical signals from patients affected by WPW syndrome. This method can provide assistance to practitioners in order to detect the WPW syndrome.
No takes yet. Share an insight, caveat, or question.
Mahamat et al. (2016) studied Wolff-Parkinson-White (WPW) syndrome. Delta wave detection algorithm using continuous wavelet transform was evaluated on Detection of delta wave. A new algorithm using continuous wavelet transform to detect the delta wave in ECG signals was developed and tested on the Physionet database to assist practitioners in diagnosing WPW syndrome.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: