QRS complex detection and measurement algorithms for multichannel ECGs in cardiac resynchronization therapy patients

Antonia E. Curtin, Kevin V. Burns, Alan J. Bank, Theoden I. Netoff

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


We developed an automated approach for QRS complex detection and QRS duration (QRSd) measurement that can effectively analyze multichannel electrocardiograms (MECGs) acquired during abnormal conduction and pacing in heart failure and cardiac resynchronization therapy (CRT) patients to enable the use of MECGs to characterize cardiac activation in such patients. The algorithms use MECGs acquired with a custom 53-electrode investigational body surface mapping system and were validated using previously collected data from 58 CRT patients. An expert cohort analyzed the same data to determine algorithm accuracy and error. The algorithms: 1) detect QRS complexes; 2) identify complexes of the most prevalent morphology and morphologic outliers; and 3) determine the array-specific (i.e., anterior and posterior) and global QRS complex onsets, offsets, and durations for the detected complexes. The QRS complex detection algorithm had a positive predictivity and sensitivity of ≥96% for complex detection and classification. The absolute QRSd error was 17 ± 14 ms, or 12%, for array-specific QRSd and 12 ± 10 ms, or 8%, for global QRSd. The absolute global QRSd error (12 ms) was less than the interobserver variation in that measurement (15 ± 10 ms). The sensitivity, positive predictivity, and error of the algorithms were similar to the values reported for current state-of-the-art algorithms designed for and limited to simpler data sets and conduction patterns and within the variation found in clinical 12-lead ECG QRSd measurement techniques. These new algorithms permit accurate, real-time analysis of QRS complex features in MECGs in patients with conduction disorders and/or pacing.

Original languageEnglish (US)
Article number8372976
JournalIEEE Journal of Translational Engineering in Health and Medicine
StatePublished - Jun 5 2018

Bibliographical note

Funding Information:
This work was supported by the Medtronic External Research Program, Medtronic plc.

Publisher Copyright:
© 2013 IEEE.


  • Biomedical signal processing
  • classification algorithms
  • detection algorithms
  • electrocardiology

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