Wireless Central Monitoring System with Diagnostic Support

Cardiovascular diseases (CVDs) remain a significant global health challenge, with a notable rise in heart attack-related mortality, such as a reported 12.5% increase in deaths in India in 2024. Traditional methods for cardiac monitoring, like Holter monitors and loop event recorders, have limitations in capturing intermittent arrhythmias, especially in patients who show no symptoms, primarily due to their limited monitoring duration and reliance on sporadic data collection [1]. Despite advancements in wearable ECG devices, which offer increased portability and real-time monitoring, challenges persist, particularly in maintaining continuous data transmission, ensuring centralized data accessibility, and improving diagnostic accuracy [2].This study introduces the Wireless Central Monitoring System with Diagnostic Support, designed to overcome these challenges. It integrates ECG stickers, an ESP32 ADC module, and a web-based monitoring platform to enable real-time, wireless cardiac signal transmission across multiple hospital beds, supporting centralized clinical monitoring [3]. Unlike traditional wearable ECG devices, which often depend on Bluetooth-based mobile applications, our approach uses Flask-based server communication to minimize latency and guarantee continuous, seamless access to data for healthcare professionals [4].Additionally, the system incorporates machine learning algorithms to improve the detection of arrhythmia. By using wavelet decomposition for noise reduction and convolutional neural networks (CNNs) for real-time anomaly classification, the system significantly outperforms conventional diagnostic methods [5]. Through continuous multi-patient monitoring, low-latency data transfer, and AI-enhanced diagnostics, the proposed system facilitates early arrhythmia detection, which improves patient outcomes by enabling timely medical interventions [6].