Automatic diagnosis of strict left bundle branch block using a wavelet-based approach
Resumen: Patients with left bundle branch block (LBBB) are known to have a good clinical response to cardiac resynchronization therapy. However, the high number of false positive diagnosis obtained with the conventional LBBB criteria limits the effectiveness of this therapy, which has yielded to the definition of new stricter criteria. They require prolonged QRS duration, a QS or rS pattern in the QRS complexes at leads V1 and V2 and the presence of mid-QRS notch/slurs in 2 leads within V1, V2, V5, V6, I and aVL. The aim of this work was to develop and assess a fully-automatic algorithm for strict LBBB diagnosis based on the wavelet transform. Twelve-lead, high-resolution, 10-second ECGs from 602 patients enrolled in the MADIT-CRT trial were available. Data were labelled for strict LBBB by 2 independent experts and divided into training (n = 300) and validation sets (n = 302) for assessing algorithm performance. After QRS detection, a wavelet-based delineator was used to detect individual QRS waves (Q, R, S), QRS onsets and ends, and to identify the morphological QRS pattern on each standard lead. Then, multilead QRS boundaries were defined in order to compute the global QRS duration. Finally, an automatic algorithm for notch/slur detection within the QRS complex was applied based on the same wavelet approach used for delineation. In the validation set, LBBB was diagnosed with a sensitivity and specificity of Se = 92.9% and Sp = 65.1% (Acc = 79.5%, PPV = 74% and NPV = 89.6%). The results confirmed that diagnosis of strict LBBB can be done based on a fully automatic extraction of temporal and morphological QRS features. However, it became evident that consensus in the definition of QRS duration as well as notch and slurs definitions is necessary in order to guarantee accurate and repeatable diagnosis of complete LBBB.
Idioma: Inglés
DOI: 10.1371/journal.pone.0212971
Año: 2019
Publicado en: PLoS ONE 14, 2 (2019), e0212971[11 pp]
ISSN: 1932-6203

Factor impacto SCIMAGO:

Financiación: info:eu-repo/grantAgreement/ES/DGA/T39-17R-BSICoS
Financiación: info:eu-repo/grantAgreement/ES/ISCIII/CIBER-BBN
Financiación: info:eu-repo/grantAgreement/ES/MINECO-FEDER/DPI2016-75458-R
Tipo y forma: Artículo (Versión definitiva)
Área (Departamento): Área Teoría Señal y Comunicac. (Dpto. Ingeniería Electrón.Com.)

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