Machine learning-based detection of cardiovascular disease using ECG signals: performance vs. complexity

Frontiers in Cardiovascular Medicine

Cardiovascular Medicine

Konstantin Egorov¹  Alexey Kazakov²  Semen Budennyy³  Huy Pham⁴ 

[1] AI for Medicine, Sber AI Lab, Moscow, Russia;Applied Research Center, Sber AI Lab, Moscow, Russia;Applied Research Center, Sber AI Lab, Moscow, Russia;New Materials Discovery Group, Artificial Intelligence Research Institute (AIRI), Moscow, Russia;Department of Computer Science, HSE University, Moscow, Russia;

关键词: ECG;    cardiovascular disease;    arrhythmia;    deep learning;    Poincaré diagram;   

DOI  :  10.3389/fcvm.2023.1229743

 received in 2023-05-26, accepted in 2023-07-05,  发布年份 2023

来源: Frontiers

【 摘 要 】

IntroductionCardiovascular 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.MethodsThe 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.ResultsThe 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.DiscussionDespite 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.

【 授权许可】

Unknown   
© 2023 Pham, Egorov, Kazakov and Budennyy.