Key result
Machine-learning-based algorithms using ECG markers achieved an accuracy of 87.5% in detecting the acute phase of T. cruzi infection and 91.3% in multiclass classification.
Why the study?
Although electrocardiography is used for diagnosing and monitoring Chagas disease, studying ECG signals is necessary to better understand disease behavior across acute and chronic phases.
Machine learning algorithms using ECG markers can accurately classify the acute and chronic phases of Trypanosoma cruzi infection in a murine model.
Answers are grounded in this paper and cite their sources.
ECG findings in T. cruzi animal models may guide monitoring; leaves open human translation and prospective validation.
Chagas disease (CD) is a neglected parasitic disease caused by the protozoan Trypanosoma cruzi (T. cruzi). The disease has two clinical phases: acute and chronic. In the acute phase, the parasite circulates in the blood. The infection can be asymptomatic or can cause unspecific clinical symptoms. During the chronic phase, the infection can cause electrical conduction abnormalities and progress to cardiac failure. The use of an electrocardiogram (ECG) has been a methodology for diagnosing and monitoring CD, but it is necessary to study the ECG signals to better understand the behavior of the disease. The aim of this study is to analyze different ECG markers using machine-learning-based algorithms for the classification of the acute and chronic phases of T. cruzi infection in a murine experimental model. The presented methodology includes a statistical analysis of control vs. infected models in both phases, followed by an automatic selection of ECG descriptors and the implementation of several machine learning algorithms for the automatic classification of control vs. infected mice in acute and/or chronic phases (binomial classification), as well as a multiclass classification strategy (control vs. the acute group vs. the chronic group). Feature selection analysis showed that P wave duration, R and P wave voltages, and the QRS complex are some of the most important descriptors. The classifiers showed good results in detecting the acute phase of infection (with an accuracy of 87.5%), as well as in multiclass classification (control vs. the acute group vs. the chronic group), with an accuracy of 91.3%. These results suggest that it is possible to detect infection at different phases, which can help in experimental and clinical studies of CD.
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Haro et al. (2023) studied Trypanosoma cruzi infection (Chagas disease). Machine-learning-based algorithms using ECG markers vs. Control mice was evaluated on Classification accuracy of acute and chronic phases of T. cruzi infection. Machine-learning-based algorithms using ECG markers achieved an accuracy of 87.5% in detecting the acute phase of T. cruzi infection and 91.3% in multiclass classification.
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