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<p>Recent advances in artificial intelligence have growing importance, with applications in robotics, control systems, biometric recognition, among others. Intelligent systems for medical support represent another important area, where cardiac diagnosis is a valuable possibility. This work deals with an intelligent system capable of identifying and classifying cardiac rhythms from heart signals extracted from electrocardiograms (ECGs). Artificial neural networks are employed, and different configurations are investigated. A dynamical perspective is adopted, using linear and nonlinear features in both the time and frequency domains. Five cardiac rhythms are selected to test the system: normal sinus rhythm, atrial fibrillation, sinus bradycardia, premature ventricular contraction, and ventricular tachyarrhythmia. The neural network is verified, and clinical cases are subsequently analyzed. Results show satisfactory classification of patients’ heart rhythms, consistent with the diagnoses offered by cardiologists in their studies.</p>

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Heart Rhythm Diagnosis Based on Artificial Neural Networks and Nonlinear Features

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月24日

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  • Igor Fortuna L. da Silva,
  • Marcelo A. Savi

摘要

Recent advances in artificial intelligence have growing importance, with applications in robotics, control systems, biometric recognition, among others. Intelligent systems for medical support represent another important area, where cardiac diagnosis is a valuable possibility. This work deals with an intelligent system capable of identifying and classifying cardiac rhythms from heart signals extracted from electrocardiograms (ECGs). Artificial neural networks are employed, and different configurations are investigated. A dynamical perspective is adopted, using linear and nonlinear features in both the time and frequency domains. Five cardiac rhythms are selected to test the system: normal sinus rhythm, atrial fibrillation, sinus bradycardia, premature ventricular contraction, and ventricular tachyarrhythmia. The neural network is verified, and clinical cases are subsequently analyzed. Results show satisfactory classification of patients’ heart rhythms, consistent with the diagnoses offered by cardiologists in their studies.