Related Experiment Video
Updated: Jun 23, 2026
Quantitative 3D Imaging of Trypanosoma cruzi-Infected Cells, Dormant Amastigotes, and T Cells in Intact Clarified Organs
Published on: June 23, 2022
ECG Marker Evaluation for the Machine-Learning-Based Classification of Acute and Chronic Phases of Trypanosoma cruzi
Paulina Haro1, Nidiyare Hevia-Montiel2, Jorge Perez-Gonzalez2
1Instituto de Investigaciones en Ciencias Veterinarias, Universidad Autónoma de Baja California, Mexicali 21386, Baja California, Mexico.
Insights
Machine learning effectively classifies Chagas disease (CD) phases using electrocardiogram (ECG) markers. This approach accurately identifies acute and chronic Trypanosoma cruzi infections in mice, aiding disease monitoring.
Area of Science:
- Cardiology
- Parasitology
- Computational Biology
Background:
- Chagas disease (CD), caused by Trypanosoma cruzi, presents acute and chronic phases with potential cardiac complications.
- Electrocardiograms (ECGs) are crucial for monitoring CD, but detailed signal analysis is needed to understand disease progression.
- Current diagnostic methods require enhancement for precise phase differentiation in experimental and clinical settings.
Purpose of the Study:
- To analyze ECG markers using machine learning for classifying acute and chronic Trypanosoma cruzi infection phases.
- To develop and validate machine learning algorithms for differentiating infection stages in a murine model.
- To identify key ECG features indicative of Chagas disease progression.
Main Methods:
- Statistical analysis of ECG data from control and Trypanosoma cruzi-infected mice (acute and chronic phases).
- Automated selection of relevant ECG descriptors, including P wave duration, R and P wave voltages, and QRS complex.
- Implementation of binomial and multiclass machine learning classifiers for infection phase identification.
Main Results:
- Machine learning models achieved 87.5% accuracy in detecting the acute phase of infection.
- Multiclass classification (control vs. acute vs. chronic) reached 91.3% accuracy.
- Key ECG features identified include P wave duration, R and P wave voltages, and QRS complex characteristics.
Conclusions:
- Machine learning analysis of ECG signals can effectively differentiate between acute and chronic Chagas disease phases.
- The study demonstrates the potential of ECG-based machine learning for improved CD diagnosis and monitoring in experimental models.
- These findings support the application of advanced computational methods in Chagas disease research and clinical practice.
Abstract:
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.
