Intelligent Edge Device for Real Time Cardiac Arrhythmia Detection
Kiran Bobby1, Linu Jose2, Ridhin Thomas Alex3, Isra Emmanuel4, Rameesa Parveen5, Joel Vinod6
Assistant Professor Department of EEE , Mar Athanasius College of Engineering, Kothamangalam, Kerala
Assistant Professor Department of EEE , Mar Athanasius College of Engineering, Kothamangalam, Kerala
UG Student Department of EEE , Mar Athanasius College of Engineering, Kothamangalam, Kerala
UG Student Department of EEE , Mar Athanasius College of Engineering, Kothamangalam, Kerala
UG Student Department of EEE , Mar Athanasius College of Engineering, Kothamangalam, Kerala
UG Student Department of EEE , Mar Athanasius College of Engineering, Kothamangalam, Kerala
Abstract - Cardiac arrhythmias are irregular heart rhythms that may appear suddenly and remain undetected without continuous monitoring. Conventional monitoring approaches often depend on cloud-based analysis, leading to increased latency, higher power consumption, and concerns regarding data privacy. This work presents a compact intelligent edge device capable of performing real-time arrhythmia detection directly on embedded hardware. The proposed system combines a MAX30003 ECG analog front-end for accurate biopotential acquisition with an ESP32-S3 microcontroller for on device signal processing and classification. Acquired ECG signals undergo filtering, segmentation, and normalization before being analyzed using a lightweight convolutional neural network optimized for resource-constrained environments. The model identifies five classes of arrhythmia defined by AAMI standards (N, S, V, F, Q) without requiring continuous internet connectivity. A local OLED interface provides instant visual alerts, while optional wireless communication enables data transfer when needed. The implementation demonstrates that reliable cardiac monitoring and classification can be achieved using a portable, low-power architecture, making it suitable for wearable and remote healthcare applications where real-time response, energy efficiency, and data security are critical.
Key Words: ECG Monitoring, Arrhythmia Classification, Edge Computing, ESP32-S3, MAX30003, Embedded AI.
