Paul Stanley Addison

Finding coordinated atrial activity during ventricular fibrillation using wavelet decomposition

Until recently, the ECG recorded during ventricular fibrillation was thought to represent disorganized and unstructured electrical activity of the heart. Using a new signal analysis technique based on wavelet decomposition, we have begun to reveal previously unreported structure within the ECG tracing. We report preliminary findings that provide the first evidence linking this structure to unexpected mechanical phenomena occurring in the heart.

Secondary wavelet feature decoupling (SWFD) and its use in detecting patient respiration from the photoplethysmogram

We describe a method for the identification of time-frequency features associated with patient respiration in the wavelet decomposition of the photoplethysmogram where the respiration features are masked by other signal components with similar spectral content. In the novel methodology a secondary transform is performed on a signal derived from the original wavelet decomposition in the region of the pulse band. The method has wide application to many other problematic signals.

Wavelet power spectrum-based prediction of successful defibrillation from ventricular fibrillation

This paper reports on work in progress to develop a wavelet-based method of outcome prediction after DC countershock. The method correlates return of spontaneous circulation (ROSC) with features of the wavelet-based power spectra derived from the ECG during ventricular fibrillation.

A comparison of continuous wavelet transform and modulus maxima analysis of characteristic ECG features

The continuous wavelet transform (CWT) offers a valuable tool for the analysis of signals as it provides precise location in time of high frequency components. The selection of a mother wavelet with high correlation with the signal under study provides a more accurate time-frequency analysis. Continuous wavelet transform modulus maxima (CWTMM) reduce the computational requirement by representing only the pertinent information contained within the scalogram obtained from continuous wavelet analysis. This new domain has an easy interpretation and offers a useful tool for the automatic characterization of the different components observed in the ECG in health and disease. The aim of this work was to compare the two time-frequency domains for ECG analysis: CWT and CWTMM, providing example applications of both methods

Analysis of ventricular late potentials prior to the onset of ventricular tachyarrhythmias: end of QRS point detector

Ventricular tachyarrithmias are one of the most dangerous cardiac pathologies often causing sudden death. Studies for the prediction of these events are based on feature extraction prior to their initiation. Ventricular late potentials are considered a marker of abnormality in ventricular function which can cause ventricular tachycardia and ventricular fibrillation. A ventricular late potential detector is proposed in this paper based on the continuous wavelet transform. Simulated ECGs with added artificial ventricular late potentials was used to develop and test the proposed algorithm. A second study of the ECGs of 8 CCU patients who subsequently developed a ventricular tachyarrhythmia was then carried out. However, no ventricular late potentials were found for this small group of patients using the proposed algorithm