The 43rd International Society for Computerized Electrocardiology ECG initiative for the automated detection of strict left bundle branch block

Zusterzeel, Robbert; Vicente, José; Ochoa-Jimenez, Roberto Carlos; Zhu, Jiaan; Couderc, Jean‐Philippe; Akinnagbe-Zusterzeel, Esther; Strauss, David G.

doi:10.1016/j.jelectrocard.2018.08.001

Cited by 8 publications

(15 citation statements)

References 16 publications

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“…Results of the LBBB initiative according to the uncorrected strict LBBB criteria (including the restriction to the end of the notch/slur) have been recently published in [8]. With those criteria, we obtained an accuracy of 71% on test dataset (5 th position).…”

Section: Discussionmentioning

confidence: 96%

“…In 2018, the International Society for Computerized Electrocardiology (ISCE) and the Telemetric and Holter Warehouse (THEW) project prompted the LBBB initiative in order to give research teams the opportunity to test automatic algorithms for strict LBBB diagnosis in patients with heart failure and reduced ejection fraction from the MADIR-CRT trial [8]. The aim of this work, as part of the LBBB initiative, is to develop and assess a fully automatic algorithm for strict LBBB diagnosis using a wavelet-based approach.…”

Section: Introductionmentioning

confidence: 99%

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Automatic diagnosis of strict left bundle branch block using a wavelet-based approach

Martín-Yebra

Martínez

2019

PLoS ONE

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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.

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Section: Discussionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Automatic diagnosis of strict left bundle branch block using a wavelet-based approach

Martín-Yebra

Martínez

2019

PLoS ONE

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“…Two other parameters based on different ECG properties—QRS area [2, 5, 6, 7] and the analysis of the high-frequency QRS complex [8, 9]–have been recently suggested as CRT response predictors. The ability to predict CRT response was confirmed for all three predictors [4, 5, 6, 9, 10]. A relationship between these parameters is anticipated, but has not been assessed yet, mainly because the corresponding data was not available.…”

Section: Introductionmentioning

confidence: 95%

“…The prediction of CRT outcome based on the ECG signal is an important task. The criteria for strict LBBB (SLBBB) based on QRS shape and QRS duration (QRSd) have been previously proposed [3, 4] to identify patients with complete LBBB to benefit most from CRT. Two other parameters based on different ECG properties—QRS area [2, 5, 6, 7] and the analysis of the high-frequency QRS complex [8, 9]–have been recently suggested as CRT response predictors.…”

Section: Introductionmentioning

confidence: 99%

The relationship between ECG predictors of cardiac resynchronization therapy benefit

et al. 2019

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Introduction Cardiac resynchronization therapy (CRT) is an effective treatment that reduces mortality and improves cardiac function in patients with left bundle branch block (LBBB). However, about 30% of patients passing the current criteria do not benefit or benefit only a little from CRT. Three predictors of benefit based on different ECG properties were compared: 1) “strict” left bundle branch block classification (SLBBB); 2) QRS area; 3) ventricular electrical delay (VED) which defines the septal-lateral conduction delay. These predictors have never been analyzed concurrently. We analyzed the relationship between them on a subset of 602 records from the MADIT-CRT trial. Methods & results SLBBB classification was performed by two experts; QRS area and VED were computed fully automatically. High-frequency QRS (HFQRS) maps were used to inspect conduction abnormalities. The correlation between SLBBB and other predictors was R = 0.613, 0.523 and 0.390 for VED, QRS area in Z lead, and QRS duration, respectively. Scatter plots were used to pick up disagreement between the predictors. The majority of SLBBB subjects– 295 of 330 (89%)–are supposed to respond positively to CRT according to the VED and QRS area, though 93 of 272 (34%) non-SLBBB should also benefit from CRT according to the VED and QRS area. Conclusion SLBBB classification is limited by the proper setting of cut-off values. In addition, it is too “strict” and excludes patients that may benefit from CRT therapy. QRS area and VED are clearly defined parameters. They may be used to optimize biventricular stimulation. Detailed analysis of conduction irregularities with CRT optimization should be based on HFQRS maps.

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“…Data from the International Society of Computerized Electrocardiology (ISCE), LBBB initiative were used in this study [2,4] with corrected adjudicated criteria [4]. The dataset consists of 12-lead ECGs sampled at 1 kHz with an amplitude resolution of 3.75 µV, recorded before CRT using Holter recorders with Mason-Likar lead placement, randomly divided into a training set with 300 records and a test set with 302 records.…”

Section: Experimental Datasetmentioning

confidence: 99%

Detecting Strict Left Bundle Branch Block From 12-Lead Electrocardiogram Using Support Vector Machine Classification and Derivative Analysis

Perera¹,

Daluwatte²

2018

2018 Computing in Cardiology Conference (CinC)

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Cardiac Resynchronization Therapy (CRT) is generally indicated for heart failure patients with a left bundle branch block (LBBB). "Strict" LBBB criteria have been proposed as a better predictor of benefit from CRT. Automatic detection of "strict" LBBB criteria may improve outcomes for heart failure patients by reducing high false positive rates in LBBB detection. This study proposes an algorithm to automatically detect "strict" LBBB, developed and tested using ECGs made available via the International Society of Computerized Electrocardiology (ISCE) LBBB initiative. The dataset consists of 12-lead Holter ECGs recorded before the therapy from the MADIT-CRT clinical trial. The algorithm consists of multi-lead QRS complex detection using length transform, a support vector machine (SVM) classifier to identify QS-or rS-configurations and identification of mid-QRS notching and slurring by analyzing the variation of first and second derivatives of the signals respectively. The algorithm achieved an accuracy of 80%, sensitivity of 86%, specificity of 73%, positive predictive value (PPV) of 81% and negative predictive value of 79% on the training set. It achieved accuracy, sensitivity, specificity, PPV and NPV of 81%, 88%, 75%, 79% and 85% on the test set. High sensitivity to minor slurring and errors in QRS detection result in low specificity for LBBB detection.

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The 43rd International Society for Computerized Electrocardiology ECG initiative for the automated detection of strict left bundle branch block

Cited by 8 publications

References 16 publications

Automatic diagnosis of strict left bundle branch block using a wavelet-based approach

Automatic diagnosis of strict left bundle branch block using a wavelet-based approach

The relationship between ECG predictors of cardiac resynchronization therapy benefit

Detecting Strict Left Bundle Branch Block From 12-Lead Electrocardiogram Using Support Vector Machine Classification and Derivative Analysis

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