Detecção automática de AVNRT em pacientes pediátricos utilizando aprendizado de máquina e sinais de ECQ

Abstract

This work proposes a computationally efficient and reproducible approach for the automatic detection of beats indicative of AVNRT in electrocardiograms of pediatric patients. The automatic identification of AVNRT in pediatric patients is a subject practically unexplored in the literature, but it has many benefits, including faster triage, support for emergency clinical decisions, and enabling continuous monitoring solutions in wearable devices. For the proposed technique, beats were segmented based on the detection of R peaks, and temporal and morphological characteristics were extracted from each beat, including classic descriptors (amplitudes and RR intervals) and Hjorth parameters (Activity, Mobility, and Complexity), which synthesize information on amplitude, approximate frequency, and temporal variation with low computational cost. The evaluation was conducted using the public database Leipzig Heart Center ECG Database, containing records of 13 pediatric patients diagnosed with AVNRT. Three supervised machine learning models were trained and compared: Support Vector Machine (SVM), Random Forest, and LightGBM. The metrics obtained in the tests indicated performance comparable to or superior to the state of the art in terms of discrimination and balance between precision and sensitivity, with an F1-score of 0.983 and an AUC of 0.989 in SVM; LightGBM presented a comparable F1-score (0.986) with lower prediction latency, suggesting an operational advantage for real-time applications. Compared to previous studies, the results obtained were similar to or superior to those reported in analogous tasks in predominantly adult populations, indicating the potential of the proposed low-complexity methodology as an efficient alternative to deep models, especially in data-scarce scenarios.