ECG Marker Evaluation for the Machine-Learning-Based Classification of Acute and Chronic Phases of Trypanosoma cruzi Infection in a Murine Model.
ECG Marker Evaluation for the Machine-Learning-Based Classification of Acute and Chronic Phases of Trypanosoma cruzi Infection in a Murine Model.
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DOI：
10.3390/tropicalmed8030157
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发表时间：
2023-03-04
影响因子：
2.9
通讯作者：
Perez-Gonzalez J
中科院分区：
文献类型：
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作者：
Haro P;Hevia-Montiel N;Perez-Gonzalez J
恰加斯病（CD）是一种被忽视的寄生虫病，由原生动物克氏锥虫（T。cruzi）。该疾病有两个临床阶段：急性和慢性。在急性期，寄生虫在血液中循环。感染可以是无症状的，也可以引起非特异性临床症状。在慢性期，感染可导致电传导异常并进展为心力衰竭。使用心电图（ECG）已成为诊断和监测CD的方法，但有必要研究ECG信号以更好地了解疾病的行为。本研究的目的是使用基于机器学习的算法分析不同的ECG标记，用于T.在鼠实验模型中的Cruzi感染。所提出的方法包括在两个阶段中对对照与感染模型进行统计分析，然后自动选择ECG描述符并实施几种机器学习算法，用于在急性和/或慢性阶段中对对照与感染小鼠进行自动分类（二项式分类），以及多类分类策略（对照组与急性组与慢性组）。特征选择分析表明，P波持续时间，R和P波电压，和QRS波群是一些最重要的描述符。分类器在检测感染的急性期（准确率为87.5%）以及多类分类（对照组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，在不同的阶段检测感染是可能的，这可以帮助CD的实验和临床研究。
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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影响因子：
3.8
作者：
Rojas LZ;Glisic M;Pletsch-Borba L;Echeverría LE;Bramer WM;Bano A;Stringa N;Zaciragic A;Kraja B;Asllanaj E;Chowdhury R;Morillo CA;Rueda-Ochoa OL;Franco OH;Muka T
通讯作者：
Muka T
影响因子：
7.5
作者：
Guo，Lu;Wang，Wei;Zhang，Yudong
通讯作者：
Zhang，Yudong
DOI：
10.1645/ge-2396.1
发表时间：
2010-08
期刊：
The Journal of parasitology
影响因子：
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作者：
Eickhoff CS;Lawrence CT;Sagartz JE;Bryant LA;Labovitz AJ;Gala SS;Hoft DF
通讯作者：
Hoft DF
影响因子：
2.9
作者：
Hevia-Montiel, Nidiyare;Perez-Gonzalez, Jorge;Haro, Paulina
通讯作者：
Haro, Paulina
影响因子：
2.9
作者：
通讯作者：
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