97056 20210820090352.0 doi 10.1088/1361-6579/ab553a sideral 117013 ART-2019-117013 eng Llamedo Mariano Assessment of automatic strategies for combining QRS detections by multiple algorithms in multiple leads 2019 Access copy available to the general public Unrestricted Objective: To develop and evaluate an algorithm for the selection of the best-performing QRS detections from multiple algorithms and ECG leads. Approach: 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. Main results: 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. Significance: 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. info:eu-repo/grantAgreement/ES/MINECO/TEC2016-75458-R info:eu-repo/grantAgreement/ES/DGA-FEDER/T39-17R-BSICoS info:eu-repo/semantics/openAccess by-nc-nd http://creativecommons.org/licenses/by-nc-nd/3.0/es/ 2.309 2019 BIOPHYSICS 39 / 71 = 0.549 2019 Q3 T2 PHYSIOLOGY 43 / 81 = 0.531 2019 Q3 T2 ENGINEERING, BIOMEDICAL 49 / 87 = 0.563 2019 Q3 T2 0.702 2019 Biomedical Engineering 2019 Q2 Biophysics 2019 Q2 Physiology 2019 Q3 Physiology (medical) 2019 Q3 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion (orcid)0000-0002-7503-3339 Martínez Cortés, Juan Pablo Universidad de Zaragoza 5008 800 Universidad de Zaragoza Dpto. Ingeniería Electrón.Com. Área Teoría Señal y Comunicac. 40 (2019), 114002 [11 pp.] Physiol. meas. PHYSIOLOGICAL MEASUREMENT 0967-3334 979103 http://zaguan.unizar.es/record/97056/files/texto_completo.pdf Postprint 344027 http://zaguan.unizar.es/record/97056/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:97056 articulos driver 2021-08-20-08:38:38 ARTICLE