Key result
An automated ECG algorithm using wavelet transform detected atrial flutter with 44% sensitivity, 99.2% specificity, and 84.6% positive predictive value in a testing group of 1,344 patients.
Why the study?
Does an automated ECG algorithm using wavelet transform accurately diagnose atrial flutter?
Cross-Sectional (n=1,669)
Does an automated ECG algorithm using wavelet transform accurately diagnose atrial flutter?
A wavelet transform-based automated ECG algorithm provides high specificity (99.2%) but variable sensitivity (44-80%) for diagnosing atrial flutter.
The authors report a new algorithm for the automated diagnosis of atrial flutter (AF) by computer ECG programs. It consists of applying the wavelet transform (WT) technique to the detection of atrial flutter waves in the inferior lead Y of the Frank orthogonal EGG. The algorithm was developed and tested on a learning group of 325 cases with 47% having AF. Its clinical usefulness was prospectively assessed on a testing group of 1344 patients with 1.8% having AF. The sensitivity in this testing group was 44% It was raised to 80% by including the diagnostic statement of possible AF, but at the same time the positive predictive value decreased from 84.6% to 66%. However, the specificity remained quite high: 99.2%. This WT procedure allowed the authors to improve the performance of the automated diagnosis of AF as compared with their previous experience. >
No takes yet. Share an insight, caveat, or question.
Brohet et al. (2002) conducted a cross-sectional in Atrial flutter (n=1,669). Automated ECG diagnosis using wavelet transform was evaluated on Diagnostic sensitivity, specificity, and positive predictive value for atrial flutter. An automated ECG algorithm using wavelet transform detected atrial flutter with 44% sensitivity, 99.2% specificity, and 84.6% positive predictive value in a testing group of 1,344 patients.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: