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Updated: Jul 19, 2025
Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
Published on: December 11, 2019
Machine learning-based detection of cardiovascular disease using ECG signals: performance vs. complexity
Huy Pham1, Konstantin Egorov2, Alexey Kazakov3
1Department of Computer Science, HSE University, Moscow, Russia.
Insights
The 1D ResNet model effectively detects cardiac arrhythmias from ECGs, offering superior accuracy and energy efficiency compared to other methods. This advancement promises faster, more reliable cardiac disease diagnosis.
Area of Science:
- Cardiology and Medical Informatics
- Artificial Intelligence in Healthcare
Background:
- Cardiovascular disease is a major health concern, necessitating efficient diagnostic tools.
- Electrocardiogram (ECG) interpretation is crucial but requires expertise and time.
- Developing automated methods for early cardiac abnormality detection is vital for improving patient care.
Purpose of the Study:
- To evaluate modern approaches for classifying cardiac diseases using ECG recordings.
- To compare the performance and efficiency of different machine learning models.
- To investigate energy consumption and model interpretability for ECG analysis.
Main Methods:
- Utilized Poincaré representation with deep learning image classifiers.
- Applied one-dimensional convolutional neural networks (1D CNNs) to raw ECG signals.
- Employed XGBoost models for time-series feature prediction.
Main Results:
- The 1D ResNet model achieved the highest F1 scores (85% on CinC 2017, 71% on CinC 2020), outperforming challenge-winning solutions.
- 1D convolutional models demonstrated high specificity and superior energy efficiency (lower power consumption and CO2 emissions).
- Poincaré methods showed promise for Atrial Fibrillation (AF) but not other arrhythmias; XGBoost had long inference times.
Conclusions:
- 1D convolutional models, particularly 1D ResNet, are highly effective and efficient for cardiac disease classification from ECGs.
- Residual connections in 1D CNNs maintain performance while simplifying models.
- Analysis of power consumption and model interpretation provides insights into computational mechanisms and efficiency.
Introduction:
Cardiovascular disease remains a significant problem in modern society. Among non-invasive techniques, the electrocardiogram (ECG) is one of the most reliable methods for detecting cardiac abnormalities. However, ECG interpretation requires expert knowledge and it is time-consuming. Developing a novel method to detect the disease early improves the quality and efficiency of medical care.
Methods:
The paper presents various modern approaches for classifying cardiac diseases from ECG recordings. The first approach suggests the Poincaré representation of ECG signal and deep-learning-based image classifiers. Additionally, the raw signals were processed with the one-dimensional convolutional model while the XGBoost model was facilitated to predict based on the time-series features.
Results:
The Poincaré-based methods showed decent performance in predicting AF (atrial fibrillation) but not other types of arrhythmia. XGBoost model gave an acceptable performance in long-term data but had a long inference time due to highly-consuming calculations within the pre-processing phase. Finally, the 1D convolutional model, specifically the 1D ResNet, showed the best results in both studied CinC 2017 and CinC 2020 datasets, reaching the F1 score of 85% and 71%, respectively, and they were superior to the first-ranking solution of each challenge. The 1D models also presented high specificity. Additionally, our paper investigated efficiency metrics including power consumption and equivalent CO2 emissions, with one-dimensional models like 1D CNN and 1D ResNet being the most energy efficient. Model interpretation analysis showed that the DenseNet detected AF using heart rate variability while the 1D ResNet assessed the AF patterns in raw ECG signals.
Discussion:
Despite the under-performed results, the Poincaré diagrams are still worth studying further because of the accessibility and inexpensive procedure. In the 1D convolutional models, the residual connections are useful to keep the model simple but not decrease the performance. Our approach in power measurement and model interpretation helped understand the numerical complexity and mechanism behind the model decision.
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