ARTIFICIAL INTELLIGENCE–ENHANCED CARDIAC SIGNAL PROCESSING FOR PREDICTING ARRHYTHMIA RISK IN HEART FAILURE PATIENTS

Abstract

The paper will analyze the effectiveness of artificial intelligence (AI)-enhanced cardiac signal processing in predicting patients with heart failure at risk of arrhythmia.  The proposed system uses a general pipeline which adds additional noise removal, feature detection, and machine-learning classification to classify high-resolution video of ECG recordings to identify the early signs of electrophysiological abnormalities associated with ventricular arrhythmias.  Quantitative findings indicate that deep learning architectures (particularly CNN-LSTM), achieved much higher predictive accuracy, sensitivity and specificity in contrast to traditional cardiac risk-scoring systems.  The system was able to identify nonlinear patterns of heart-rate variability, repolarization abnormalities and micro-level waveform abnormalities that enable arrhythmia to develop, therefore making it possible to make premature and more accurate risk-classifications.  The models of AI also demonstrated that they could be used consistently in a clinical setting since they were effective with diverse groups of patients.  The findings indicate that AI-driven cardiac analytics may enhance the real-time observation, assist physicians with judgment, and reduce the possibility of sudden cardiac death in individuals with heart failure.  The study forms a strong foundation on the integration of smart ECG-based predictive algorithm into modern cardiology practice.