portable holter with cloud-based learning analytics for real-time health monitoring

The increasing prevalence of cardiovascular diseases underscores the need for efficient and user-friendly tools to monitor heart health. traditional holter monitors, while effective, are often bulky and inconvenient, limiting their use in real-world scenarios. this study introduces the smart portable holter, a wireless device designed for real-time cardiac monitoring, enabling early detection of heart irregularities with enhanced accuracy and user convenience. the device captures continuous electrocardiogram signals and transmits them to a secure cloud platform for processing. machine learning models, including random forest and extreme gradient boosting (xgboost), analyze the data to detect cardiac events. the system’s performance was evaluated using real-world datasets, emphasizing accuracy and reliability in identifying cardiac arrhythmias. the smart portable holter delivers an impressive 98% accuracy in detecting cardiac events. its compact and wireless design enhances user comfort, allowing for seamless wear throughout the day. coupled with advanced analytics, it offers detailed, time-stamped records that empower both users and healthcare professionals. these features facilitated early diagnosis and supported personalized treatment planning for patients with varying cardiac conditions. the smart portable holter represents a significant advancement in cardiac care, combining portability, real-time analytics, and high diagnostic accuracy. by empowering patients and healthcare providers with actionable insights, it fosters proactive heart health management and contributes to improved clinical outcomes.