Cardio Rescue Network in AI-Driven Wearable System for Real-Time Cardiac Event Prediction and Management Using 5G-Enabled Edge Computing

The Cardio Rescue Network offers a unique wearable- based real-time cardiac event prediction and management solution. Using modern biosensors linked to wearable devices, this system continuously measures critical cardiovascular metrics such as heart rate, ECG, and blood oxygen levels. The system uses machine learning algorithms and multi-factor authentication (MFA) approaches to detect probable cardiac episodes, alert users and healthcare professionals, and provide real-time intervention recommendations. The gadget detects anomalies in heart function using a mix of sensors, including the AD8232 for ECG, hydrostatic pressure level sensors, and bioelectrical impedance analysis (BIA). This data is examined using artificial intelligence models to anticipate probable cardiac events, such as arrhythmias or heart attacks, even before symptoms show. The network offers low- latency communication using a 5G-enabled Multi-Access Edge Computing (MEC) infrastructure, allowing for speedy replies and urgent medical intervention. Furthermore, the wearable includes multi-factor authentication, which improves the security and privacy of patient data and ensures that health information is only shared with authorized persons. The use of this technology not only attempts to improve patient outcomes by improving early diagnosis and management of cardiac events, but it also increases access to timely care for remote and underserved groups. This paper covers the design, implementation, and possible impact of the Cardio Rescue Network on real-time cardiac monitoring, patient safety, and healthcare accessibility, emphasizing the role of 5G and AI in telemedicine.