Machine Learning Assessment of Left Ventricular Diastolic Function Based on Electrocardiographic Features

Kagiyama, Nobuyuki; Piccirilli, Marco; Yanamala, Naveena; Shrestha, Sirish; Farjo, Peter; Casaclang‐Verzosa, Grace; Tarhuni, Wadea; Nezarat, Negin; Budoff, Matthew J.; Narula, Jagat; Sengupta, Partho P.

doi:10.1016/j.jacc.2020.06.061

Cited by 66 publications

(42 citation statements)

References 23 publications

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“…The present prospective ML study also differs from previous ones in that it involves patients who were very carefully selected, thereby excluding those with CVD. 30,31 This may explain the fact that in our study, analysis of patients with LVH achieved a higher AUC in comparison to recently published work, 30 despite the fact that the number of our patients is smaller. ML is susceptible to major errors in interpretation, and generalizability.…”

Section: Discussioncontrasting

confidence: 63%

“…There are limited data in the literature that attempt to predict cardiac structural or functional abnormalities with ECG data interpreted through ML algorithms. 30–32 However, the existing knowledge has focused only on patients who have already shown LVH. There are no data for patients in earlier stages of cardiac geometry change prior to hypertrophy.…”

Section: Discussionmentioning

confidence: 99%

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Prediction of abnormal left ventricular geometry in patients without cardiovascular disease through machine learning: An ECG-based approach

Angelaki

Marketou

Barmparis

et al. 2020

Preprint

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Cardiac remodeling is recognized as an important aspect of cardiovascular disease (CVD) progression. Machine learning (ML) techniques were applied on basic clinical parameters and electrocardiographic features for detecting abnormal left ventricular geometry (LVG), even before the onset of left ventricular hypertrophy (LVH), in a population without established CVD. After careful screening, we enrolled 528 subjects with and without essential hypertension, but no other indications of CVD. All patients underwent a full echocardiographic evaluation and were classified into 3 groups; normal geometry (NG), concentric remodeling without LVH (CR), and LVH. Abnormal LVG was identified as increased relative wall thickness (RWT) and/or left ventricular mass index (LVMi). We trained nonlinear predictive ML models, to classify subjects with abnormal LVG and calculated SHAP values to perform feature importance and interaction analysis. Hypertension, age, body mass index over the Sokolow-Lyon voltage, QRS-T angle, and QTc duration were some of the most important features. Our model was able to distinguish NG from all others (CR+LVH), with accuracy 86%, specificity 75%, sensitivity 95%, and area under the receiver operating curve (AUC/ROC) 0.89. We also trained our model to classify NG and CR (NG+CR) against those with established LVH, with accuracy 89%, specificity 97%, sensitivity 50%, and AUC/ROC 0.85. Our ML algorithm effectively detects abnormal LVG even at early stages. Innovative solutions are needed to improve risk stratification of patients without established CVD, especially in primary care settings, and ML may enable this direction.

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Section: Discussioncontrasting

confidence: 63%

Section: Discussionmentioning

confidence: 99%

Prediction of abnormal left ventricular geometry in patients without cardiovascular disease through machine learning: An ECG-based approach

Angelaki

Marketou

Barmparis

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…In their subsequent study [29], Kagiyama et al used the RFbased classifier for traditional ECG, signal processed surface ECG and 10 basic clinical features to predict LVDD in a total of 1,202 subjects. The AUCs of the ROCs were estimated to be 83% in the internal test set and 84% in the external test set.…”

Section: Resultsmentioning

confidence: 99%

Electrocardiographic Machine Learning to Predict Left Ventricular Diastolic Dysfunction in Asian Young Male Adults

Lin

2021

IEEE Access

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Left ventricular diastolic dysfunction (LVDD) occurs at the initial stage of heart failure. Electrocardiographic (ECG) criteria and machine learning for ECG features have been applied to predict LVDD in middle-and old-aged individuals. The purpose of this study is to clarify the performance of machine learning in young adults. Three machine learning classifiers including random forest (RF), support vector machine (SVM) and gradient boosting decision tree (GBDT) for the input of 26 ECG features with or without 6 other biological features (age, anthropometrics and blood pressures) are compared with the corrected QT (QTc) interval, a traditional ECG criterion for LVDD. The definition of LVDD is based on either one of the following echocardiographic criteria: (1) an E/A ratio of mitral inflow <0.8; (2) a lateral mitral annulus velocity e' <10 cm/s; and (3) an E/e' ratio >14. The best areas under the receiver operating characteristic curve were observed in machine learning of the RF for ECG only (84.1%) and of the SVM for all ECG and biological features (82.1%), both of which were superior to the QTc interval (64.6%). If the specificity is chosen to be approximately 75.0%, the sensitivity of the RF for ECG only reaches 81.0% and that of the SVM for all ECG and biological features is raised to 85.7%, both of which are higher than 47.6% by the QTc interval. This study suggests that using machine learning for ECG features only or with other biological features to predict LVDD in young Asian adults is reliable. The proposed methods provide for the early detection of LVDD for young adults and are helpful for taking preventive action on heart failure.

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“…Enriching training echocardiographic datasets with TTS/STEMI imaging features in different evolving stages may help further improve the diagnostic accuracy of DL neural networks. [ 26 , 27 ] 2. The RNN model, which implements the concept of memory with introducing feedback links between layers backward, has the capability of learning temporal context across video frames, which captures more global motion features; however, our current experiments based on limited views of echocardiograms shows that the RNN model is inferior to DCNN (2D+ t ), which may be due to RNN's relatively weak capability of learning spatial features.…”

Section: Discussionmentioning

confidence: 99%

Spatio-temporal hybrid neural networks reduce erroneous human “judgement calls” in the diagnosis of Takotsubo syndrome

et al. 2021

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Background: We investigate whether deep learning (DL) neural networks can reduce erroneous human "judgment calls" on bedside echocardiograms and help distinguish Takotsubo syndrome (TTS) from anterior wall ST segment elevation myocardial infarction (STEMI). Methods: We developed a single-channel (DCNN[2D SCI]), a multi-channel (DCNN[2D MCI]), and a 3-dimensional (DCNN[2D+t]) deep convolution neural network, and a recurrent neural network (RNN) based on 17,280 still-frame images and 540 videos from 2-dimensional echocardiograms in 10 years (1 January 2008 to 1 January 2018) retrospective cohort in University of Iowa (UI) and eight other medical centers. Echocardiograms from 450 UI patients were randomly divided into training and testing sets for internal training, testing, and model construction. Echocardiograms of 90 patients from the other medical centers were used for external validation to evaluate the model generalizability. A total of 49 board-certified human readers performed human-side classification on the same echocardiography dataset to compare the diagnostic performance and help data visualization. Findings: The DCNN (2D SCI), DCNN (2D MCI), DCNN(2D+t), and RNN models established based on UI dataset for TTS versus STEMI prediction showed mean diagnostic accuracy 73%, 75%, 80%, and 75% respectively, and mean diagnostic accuracy of 74%, 74%, 77%, and 73%, respectively, on the external validation. DCNN(2D+t) (area under the curve [AUC] 0¢787 vs. 0¢699, P = 0¢015) and RNN models (AUC 0¢774 vs. 0¢699, P = 0¢033) outperformed human readers in differentiating TTS and STEMI by reducing human erroneous judgement calls on TTS. Interpretation: Spatio-temporal hybrid DL neural networks reduce erroneous human "judgement calls" in distinguishing TTS from anterior wall STEMI based on bedside echocardiographic videos.

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Machine Learning Assessment of Left Ventricular Diastolic Function Based on Electrocardiographic Features

Cited by 66 publications

References 23 publications

Prediction of abnormal left ventricular geometry in patients without cardiovascular disease through machine learning: An ECG-based approach

Prediction of abnormal left ventricular geometry in patients without cardiovascular disease through machine learning: An ECG-based approach

Electrocardiographic Machine Learning to Predict Left Ventricular Diastolic Dysfunction in Asian Young Male Adults

Spatio-temporal hybrid neural networks reduce erroneous human “judgement calls” in the diagnosis of Takotsubo syndrome

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