12^th International Heart Conference

Arrhythmia Detection

Arrhythmias, or irregular heart rhythms, are among the leading causes of morbidity and mortality worldwide, ranging from atrial fibrillation to life-threatening ventricular tachycardia. Traditional diagnosis relies on intermittent electrocardiogram (ECG) recordings and manual interpretation, which may miss transient or subtle abnormalities. Artificial intelligence has transformed arrhythmia detection by enabling continuous monitoring and real-time analysis of cardiac electrical activity. Machine learning and deep learning algorithms can process large volumes of ECG data from clinical devices or wearable sensors, identifying abnormal patterns with remarkable accuracy. These algorithms are capable of detecting even brief episodes of arrhythmia that could otherwise go unnoticed, allowing early intervention and reducing the risk of stroke or sudden cardiac death. AI-based arrhythmia detection also facilitates remote monitoring, enabling patients to remain at home while their cardiac activity is continuously analyzed. Alerts generated by AI systems can prompt immediate clinical evaluation, medication adjustments, or other interventions. By improving diagnostic accuracy, reducing time to treatment, and enabling personalized management plans, AI-driven arrhythmia detection enhances patient outcomes, lowers healthcare costs, and represents a critical innovation in modern cardiology.