Haro Paulina, Hevia-Montiel Nidiyare, Perez-Gonzalez Jorge
Instituto de Investigaciones en Ciencias Veterinarias, Universidad Autónoma de Baja California, Mexicali 21386, Baja California, Mexico.
Unidad Académica del Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas del Estado de Yucatán, Universidad Nacional Autónoma de México, Sierra Papacal 97302, Yucatan, Mexico.
Trop Med Infect Dis. 2023 Mar 4;8(3):157. doi: 10.3390/tropicalmed8030157.
Chagas disease (CD) is a neglected parasitic disease caused by the protozoan (). 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 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.
恰加斯病（CD）是一种由原生动物（此处原文缺失具体名称）引起的被忽视的寄生虫病。该疾病有两个临床阶段：急性期和慢性期。在急性期，寄生虫在血液中循环。感染可能无症状，也可能引起非特异性临床症状。在慢性期，感染可导致心电传导异常并发展为心力衰竭。使用心电图（ECG）一直是诊断和监测恰加斯病的一种方法，但有必要研究心电图信号以更好地了解该疾病的行为。本研究的目的是使用基于机器学习的算法分析不同的心电图标志物，以便在小鼠实验模型中对感染的急性期和慢性期进行分类。所提出的方法包括对两个阶段的对照模型与感染模型进行统计分析，随后自动选择心电图描述符，并实施几种机器学习算法，用于对急性期和/或慢性期的对照小鼠与感染小鼠进行自动分类（二项式分类），以及多类分类策略（对照 vs. 急性组 vs. 慢性组）。特征选择分析表明，P波持续时间、R波和P波电压以及QRS波群是一些最重要的描述符。分类器在检测感染急性期（准确率为87.5%）以及多类分类（对照 vs. 急性组 vs. 慢性组）方面显示出良好的结果，准确率为91.3%。这些结果表明，可以检测不同阶段的感染，这有助于恰加斯病的实验和临床研究。
