Chagas disease is a severe and life threatening illness that in the last decades was becoming a public health problem because of the change in the epidemiological pattern, becoming mainly an urban disease, thus putting 70 million people at risk of infection. It may be silent and asymptomatic in the chronic phase (where 40% of the population has cardiac compromise). That is the justification of the necessity of the development of early markers. To achieve this, we propose a deep neural network architecture in order to classify 292 patients into three different groups: The Control group with 83 volunteers, the CH1 group with 102 patients with positive serology and no cardiac involvement and the CH2 group with 107 patients with positive serology and mild to moderate incipient heart failure. The used data comes from 24-hour ECG, the RR intervals from each subject was divided in 288 frames of 5 minutes each. Then the RR data was preprocessed using permutation entropy obtaining a circadian profile for each patient which was used in the training of the proposed architecture. The classification performed with 89 % accuracy and 89% precision, consisting in a great work of classification validated by the AUC in each ROC curve. As this results were obtained with a limited quantity of data, this study can be improved provided with more samples, making this model a tool for analyzing ECG in order to try to do an early evaluation and diagnosis of a cardiac compromise related to the generally silent chronic phase.