Exploring the Future of Artificial Intelligence-Enhanced Wearable Electronics for Real-Time Arrhythmia Detection

The field of arrhythmia detection and cardiovascular health monitoring is rapidly changing due to wearable technology. With a focus on developments in flexible materials, sensor integration, and electronic design for ongoing arrhythmia monitoring, this review offers a thorough examination of both established and new wearable sensor technologies. The article describes both commercial and experimental devices, such as textile-based, patch, and wrist-worn platforms, emphasizing their performance in clinical and real-world settings as well as their sensing modalities, including bioelectrical, optoelectrical, and mechanoelectrical techniques. The integration of machine learning (ML) and artificial intelligence (AI) algorithms is given special attention because it greatly improves wearable monitors' clinical utility, predictive power, and diagnostic accuracy. There is discussion of case studies, such as the use of deep learning to analyze photoplethysmography (PPG) and electrocardiogram (ECG) data, and their effects on earlier detection and improved management of atrial fibrillation and other arrhythmias. Critically analyzed are issues with data security, regulatory approval, signal fidelity, user adherence, and sensor ergonomics. In order to enhance long-term wearability and user comfort, the review also looks at the market environment, legal frameworks, and advancements in material science, including textile-integrated graphene electrodes and epidermal electronics. The importance of interoperable device architectures, strong privacy protection that complies with international standards, and the ongoing development of AI-driven analytics for real-time decision support in healthcare are highlighted as future research directions. The purpose of the synthesis is to direct researchers, engineers, and clinicians toward the upcoming generation of patient-centered, intelligent wearable technologies for arrhythmia detection.