Machine learning versus classic electrocardiographic criteria for the detection of echocardiographic left ventricular hypertrophy in a pre-participation cohort

Lim, Daniel Yan Zheng; Sng, Gerald; Ho, Wilbert; Wang, Hankun; Sia, Ching‐Hui; Lee, Joshua S.W.; Shen, Xiayan; Tan, Benjamin Yq; Lee, Edward C. Y.; Dalakoti, Mayank; Jie, Wang Kang; Kwan, Clarence K.W.; Chow, Weien; Tan, Ru San; Lam, Carolyn S.P.; Chua, Terrance; Yeo, Tee Joo; Chong, Daniel

doi:10.33963/kp.15955

Cited by 12 publications

(22 citation statements)

References 29 publications

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“…The conventional Sokolow-Lyon and Cornell criteria and machine learning models had high specificity and low sensitivity, as observed in previ-ous studies. 25,26) Additionally, the deep learning model focused on QRS waves of V5 and V6 when detecting LVH. This result is also consistent with previous findings 27) and existing medical knowledge that V5 and V6 voltages are used for the Sokolow-Lyon diagnostic criteria.…”

Section: Discussionmentioning

confidence: 99%

Automatic Detection of Left Ventricular Dilatation and Hypertrophy from Electrocardiograms Using Deep Learning

et al. 2022

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Left ventricular dilatation (LVD) and left ventricular hypertrophy (LVH) are risk factors for heart failure, and their detection improves heart failure screening. This study aimed to investigate the ability of deep learning to detect LVD and LVH from a 12-lead electrocardiogram (ECG). Using ECG and echocardiographic data, we developed deep learning and machine learning models to detect LVD and LVH. We also examined conventional ECG criteria for the diagnosis of LVH. We calculated the area under the receiver operating characteristic (AUROC) curve, sensitivity, specificity, and accuracy of each model and compared the performance of the models. We analyzed data for 18,954 patients (mean age (standard deviation): 64.2 (16.5) years, men: 56.7%). For the detection of LVD, the value (95% confidence interval) of the AUROC was 0.810 (0.801-0.819) for the deep learning model, and this was significantly higher than that of the logistic regression and random forest methods (P < 0.001). The AUROCs for the logistic regression and random forest methods (machine learning models) were 0.770 (0.761-0.779) and 0.757 (0.747-0.767), respectively. For the detection of LVH, the AUROC was 0.784 (0.777-0.791) for the deep learning model, and this was significantly higher than that of the logistic regression and random forest methods and conventional ECG criteria (P < 0.001). The AUROCs for the logistic regression and random forest methods were 0.758 (0.751-0.765) and 0.716 (0.708-0.724), respectively. This study suggests that deep learning is a useful method to detect LVD and LVH from 12-lead ECGs.

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Section: Discussionmentioning

confidence: 99%

Automatic Detection of Left Ventricular Dilatation and Hypertrophy from Electrocardiograms Using Deep Learning

et al. 2022

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“…On the other hand, machine learning models for detecting LVH using ECG signals reported in previous studies (Ref. [20][21][22][24][25][26][27]) are also summarized in Tab. 6.…”

Section: Feature Importancementioning

confidence: 99%

“…[25]), a model of ensemble neural network (ENN) which integrated the convolution neural network (CNN) and DNN was proposed in predicting LVH. [20][21][22][24][25][26][27]) and listed in Tab. 6, in detecting LVH using ECG signals.…”

Section: Feature Importancementioning

confidence: 99%

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Left Ventricular Hypertrophy Detection Using Electrocardiographic Signal

Wu²,

et al. 2022

Preprint

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Left ventricular hypertrophy (LVH) indicates subclinical organ damage, associating with the incidence of cardiovascular diseases. From the medical perspective, electrocardiogram (ECG) is a low-cost, non-invasive, and easily reproducible tool that is often used as a preliminary diagnosis for the detection of heart disease. Nowadays, there are many criteria for assessing LVH by ECG. These criteria usually include that voltage combination of RS peaks in multi-lead ECG must be greater than one or more thresholds for diagnosis. We developed a system for detecting LVH using ECG signals by two steps: firstly, the R-peak and S-valley amplitudes of the 12-lead ECG were extracted to automatically obtain a total of 24 features and ECG beats of each case (LVH or non-LVH) were segmented; secondly, a back propagation neural network (BPN) was trained using a dataset with these features. Echocardiography (ECHO) was used as the gold standard for diagnosing LVH. The number of LVH cases (of a Taiwanese population) identified was 173. As each ECG sequence generally included 8 to 13 cycles (heartbeats) due to differences in heart rate, etc., we identified 1466 ECG cycles of LVH patients after beat segmentation. Results showed that our BPN model for detecting LVH reached the testing accuracy, precision, sensitivity, and specificity of 0.961, 0.958, 0.966 and 0.956, respectively. Detection performances of our BPN model, on the whole, outperform 7 methods using ECG criteria and many ECG-based artificial intelligence (AI) models reported previously for detecting LVH.

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“…Currently, ECG is the most frequently used as a first screening tool to identify LVH [11]. The major limitation of ECG-LVH criteria, in the screening for LVH, is low sensitivity [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Clinical Data, Chest Radiograph and Electrocardiography in the Screening for Left Ventricular Hypertrophy: The CAR2E2 Score

Matusik¹,

Bryll

Pac

et al. 2022

JCM

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Left ventricular hypertrophy (LVH) is associated with adverse clinical outcomes and implicates clinical decision-making. The aim of our study was to assess the importance of different approaches in the screening for LVH. We included patients who underwent cardiac magnetic resonance (CMR) imaging and had available chest radiograph in medical documentation. Cardiothoracic ratio (CTR), transverse cardiac diameter (TCD), clinical and selected electrocardiographic (ECG)-LVH data, including the Peguero-Lo Presti criterion, were assessed. CMR–LVH was defined based on indexed left ventricular mass-to-body surface area. Receiver operating characteristics analyses showed that both the CTR and TCD (CTR: area under the curve: [AUC] = 0.857, p < 0.001; TCD: AUC = 0.788, p = 0.001) were predictors for CMR–LVH. However, analyses have shown that diagnoses made with TCD, but not CTR, were consistent with CMR–LVH. From the analyzed ECG–LVH criteria, the Peguero-Lo Presti criterion was the best predictor of LVH. The best sensitivity for screening for LVH was observed when the presence of heart failure, ≥40 years in age (each is assigned 1 point), increased TCD and positive Peguero-Lo Presti criterion (each is assigned 2 points) were combined (CAR2E2 score ≥ 3 points). CAR2E2 score may improve prediction of LVH compared to other approaches. Therefore, it may be useful in the screening for LVH in everyday clinical practice in patients with prevalent cardiovascular diseases.

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Machine learning versus classic electrocardiographic criteria for the detection of echocardiographic left ventricular hypertrophy in a pre-participation cohort

Cited by 12 publications

References 29 publications

Automatic Detection of Left Ventricular Dilatation and Hypertrophy from Electrocardiograms Using Deep Learning

Automatic Detection of Left Ventricular Dilatation and Hypertrophy from Electrocardiograms Using Deep Learning

Left Ventricular Hypertrophy Detection Using Electrocardiographic Signal

Clinical Data, Chest Radiograph and Electrocardiography in the Screening for Left Ventricular Hypertrophy: The CAR2E2 Score

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