A New Wavelet-Based ECG Delineator for the Evaluation of the Ventricular Innervation

Abstract

T-wave amplitude (TWA) has been proposed as a marker of the innervation of the myocardium. Until now, TWA has been calculated manually or with poor algorithms, thus making its use not efficient in a clinical environment. We introduce a new wavelet-based algorithm for the delineation QRS complexes and T-waves, and the automatic calculation of TWA. When validated in the MIT/BIH Arrhythmia database, the QRS detector achieved sensitivity and positive predictive value of 99.84% and 99.87%, respectively. The algorithm was validated also on the QT database and it achieved sensitivity of 99.50% for T-peak detection. In addition, the algorithm achieved delineation accuracy that is similar to the differences in delineation between expert cardiologists. We applied the algorithm for the evaluation of the influence in TWA of anticholinergic and antiadrenergic drugs (i.e., atropine and metoprolol) for healthy subjects. We found that the TWA decreased significantly with atropine and that metoprolol caused a significant increase in TWA, thus confirming the clinical hypothesis that the TWA is a marker of the innervation of the myocardium. The results of this paper show that the proposed algorithm can be used as a useful and efficient tool in clinical practice for the automatic calculation of TWA and its interpretation as a non-invasive marker of the autonomic ventricular innervation.

QRS Complex Detection and Measurement Algorithms for Multichannel ECGs in Cardiac Resynchronization Therapy Patients

“…The QRS complex detection algorithm had at least 96% sensitivity and positive predictivity for QRS complex detection and classification. The accuracy of the QRS delineation and measurement algorithm was close to the interobserver variation in the expert cohort responses, similar to that of existing single-lead ECG methods [33] , and similar to documented differences in QRSd results between clinical ECG machines and manual clinician measurements [34] . The QRSd measurement algorithm clinical classification accuracy was 95% in the 20 native conduction validation studies, which is better than the reported classification agreement between clinical ECG machines and doctors [34] .…”

“…No previously published QRS complex detection and/or classification algorithms for MECGs were found in the literature. Reported sensitivity and positive predictivity for single-lead ECG algorithms are usually over 99% for QRS complex detection without morphologic classification [15] , [33] . Fully automated real-time QRS detectors with morphologic classification (i.e., PVC detectors), however, tend to have positive predictivity and sensitivity values closer to 90% to 95% [15] .…”

An Automatic R and T Peak Detection Method Based on the Combination of Hierarchical Clustering and Discrete Wavelet Transform

“…Therefore, a post-processing is needed to remove such R peaks from RsR pattern or a fake R-peak due to a half ECG beat in a window. We set a threshold of the shortest period between each R peak from two successive QRS complexes, which is from 100 ms [7] to 250 ms [17]. The experimental results showed that 100 ms window is too small if there is a RsR pattern in a wide QRS complex and 250 ms is too large to a small QRS complex.…”

“…The results and the comparison with the literature are given in Table III. Finally, we built a record-by-record classification as proposed in [5] and more recently in [17] for T peak detection. We chose the same threshold in [5] to compare with the previous works.…”

“…The existing literature on ECG wave delineation algorithms is extensive and focuses particularly on frequency aspect, e.g. DWT [17] [18], the combination of wavelet transform and hybrid hidden Markov models [19]. Among other popular methods, the phasor transform [20], movingaverage filters [21], [22], morphological mathematical filtering with Elgendi's algorithm [23] and the correlation analysisbased method [24] have also been applied to detect ECG fiducial points.…”