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An Open-Source Analysis of Cardiomyopathy Using Machine Learning and Electrocardiograms
Arda Altintepe1, Asu Rustemli2, Amir Reza Vazifeh3,4
1Horace Mann School, Bronx, NY 10471, USA.
Insights
Electrocardiogram (ECG) machine learning models can distinguish between dilated cardiomyopathy (DCM) and hypertrophic cardiomyopathy (HCM) using open-source data. Distinct ECG features help differentiate these heart conditions, aiding potential diagnostic screening.
Area of Science:
- Cardiology
- Medical Informatics
- Machine Learning
Background:
- Dilated cardiomyopathy (DCM) and hypertrophic cardiomyopathy (HCM) are leading causes of heart failure.
- Current diagnostic methods may not be readily accessible in all areas, highlighting the need for streamlined screening.
- Existing machine learning studies on cardiomyopathy often lack open-source data and direct comparisons between HCM and DCM subtypes.
Purpose of the Study:
- To develop and validate an open-source, ECG-based machine learning pipeline for differentiating between DCM and HCM.
- To identify distinct ECG features that characterize HCM, DCM, and subtypes like obstructive HCM (HOCM) and non-obstructive HCM (HNCM).
- To assess the feasibility of using ECG screening for early cardiomyopathy diagnosis.
Main Methods:
- Extracted standard and vectorcardiogram-derived (VCG) ECG features from the MIMIC-IV-ECG database.
- Utilized logistic regression (LR) and extreme gradient boosting (XGBoost) models with cross-validation on a cohort of 599 patients.
- Compared the models' performance in distinguishing HCM from DCM subtypes (DCM-I, DCM-NI) and HOCM from HNCM using AUC-ROC.
Main Results:
- LR models achieved high discrimination between HCM and both DCM subtypes (AUC-ROC 0.90-0.92).
- Differentiating HOCM from HNCM was more challenging (XGBoost AUC-ROC 0.81).
- Distinct ECG patterns were observed: DCM showed lower QRS amplitudes and right-posterior gradients, while HCM exhibited higher amplitudes and complex T-loops. HOCM displayed stronger leftward electrical activity.
Conclusions:
- An interpretable, open-access ECG analysis pipeline can effectively discriminate between cardiomyopathy types.
- Specific ECG features offer measurable separation between HCM and DCM, supporting their use in diagnostic screening.
- While differentiating obstructive from non-obstructive HCM via ECG remains challenging, the study provides a reproducible framework for future research.
Abstract:
Background/Objectives: Dilated cardiomyopathy (DCM) and hypertrophic cardiomyopathy (HCM) are common cardiomyopathies associated with heart failure. Electrocardiogram (ECG) screening before an echocardiogram could help streamline diagnosis, particularly in rural areas. Prior ECG-machine learning (ML) studies do not use open-source data when studying cardiomyopathy, and very few proprietary studies directly compare HCM and DCM or address ECG differences within obstructive (HOCM) and non-obstructive HCM (HNCM). Methods: Standard and vectorcardiogram-derived (VCG) ECG features were extracted from the MIMIC-IV-ECG database. The final cohort comprised 599 patients (HCM = 208 [HOCM = 99, HNCM = 53, unknown = 56]; DCM = 391 [ischemic cardiomyopathy with left ventricular dilation = 250, non-ischemic = 141]). Logistic regression (LR) and extreme gradient boosting (XGBoost) with five-fold cross-validation separated HCM from ischemic cardiomyopathy with left ventricular dilation (DCM-I) and non-ischemic DCM (DCM-NI), and HOCM from HNCM. Results: Using the area under the receiver-operating-characteristic curve (AUC-ROC) as the performance metric, LR achieved high discrimination of HCM from DCM-I (0.92) and DCM-NI (0.90). However, differentiating HOCM from HNCM proved more difficult (XGBoost = 0.81; LR = 0.75). Both DCM subtypes (especially ischemic) showed lower QRS amplitudes and right-posterior ventricular gradient orientation; HCM displayed higher amplitudes and larger, more complex T-loops. Within HCM, HOCM had stronger leftward electrical activity and more dipolar to non-dipolar QRS energy after singular value decomposition. Conclusions: Using only open-access data, we demonstrate an interpretable ECG-based pipeline that discriminates cardiomyopathy and highlights distinct features. While detecting obstruction remains difficult, ECG features provide measurable separation, supporting possible diagnostic screening and offering a reproducible framework for future studies.
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Definition
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