Automated detection of Chagas disease from ECG signals using wavelet scattering transform and RUSBoost classifier

Abstract Objective. Early diagnosis of Chagas disease plays a vital role in enabling timely treatment and reducing the likelihood of underlying severe cardiovascular complications. Electrocardiogram (ECG) signals contain vital information that reflects cardiac disease progression, motivating the use of advanced signal processing and machine intelligence for accurate diagnosis of Chagas disease. This work presents an automated system for Chagas disease detection using conventional 12-lead ECG recordings. Approach. The method begins with preprocessing by standardizing the ECG sampling frequency and detecting QRS complexes. Then, four categories of features are extracted: (a) wavelet scattering transform coefficients from limb lead II and chest leads V1 and V3; (b) statistical descriptors from heart rate variability; (c) statistical features across all leads; and (d) patient metadata. The computed diagnostic feature vector is used as an input to the random under-sampling boosting classifier for its ability to handle class imbalance for binary classification between Chagas and non-Chagas cases. Main results. The proposed framework was evaluated on the PhysioNet/Computing in Cardiology (CinC) Challenge 2025 dataset. Evaluation on the hidden test data set yielded an accuracy of 90.53 % , F 1 Chagas = 10.73 % , AUROC = 63.67 % , AUPRC = 11.96 % and Challenge Score = 20.5 % under the team name Medics. Significance. The findings of this work highlight the potential of signal processing and machine learning-based analysis of ECG for scalable, non-invasive, and cost-effective early detection of Chagas disease, supporting improved clinical decision-making and preventive healthcare strategies.