Assessment of automatic strategies for combining QRS detections by multiple algorithms in multiple leads

To develop and evaluate an algorithm for the selection of the best-performing QRS detections from multiple algorithms and ECG leads.

The detections produced by several publicly available single-lead QRS detectors are segmented in 20 s consecutive windows; then a statistical model is trained to estimate a quality metric that is used to rank each 20 s segment of detections. The model describes each heartbeat in terms of six features calculated from the RR interval series, and one feature proportional to the number of heartbeats detected in other leads in a neighborhood of the current heartbeat. With the highest ranked segments, we defined several lead selection strategies (LSS) that were evaluated in a set of 1754 ECG recordings from 14 ECG databases. The LSS proposed were compared with simple strategies such as selecting lead II or the first lead available in a recording. The performance was calculated in terms of the average sensitivity, positive predictive value, and F score.

The best-performing LSS, based on wavedet algorithm, achieved an F score of 98.7, with sensitivity S  =  99.2 and positive predictive value P  =  98.3. The F score for the simpler strategy using the same algorithm was 92.7. The LSS studied in this work have been made available in an open-source toolbox to ease the reproducibility and result comparison.

The results suggest that the use of LSS is convenient for purposes of selecting the best heartbeat locations among those provided by different detectors in different leads, obtaining better results than any of the algorithms individually.