Short Communication - (2025) Volume 12, Issue 1
Received: 02-Jan-2025, Manuscript No. bset-25-168441;
Editor assigned: 04-Jan-2025, Pre QC No. P-168441;
Reviewed: 18-Jan-2025, QC No. Q-168441;
Revised: 23-Jan-2025, Manuscript No. R-168441;
Published:
30-Jan-2025
, DOI: 10.37421/2952-8526.2025.12.243
Citation: Mansour, Tariq. "Design and Development of a Real-time Wearable Cardiac Monitoring System." J Biomed Syst Emerg Technol 12 (2025): 243.
Copyright: © 2025 Mansour T. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
The core of a wearable cardiac monitoring system lies in its biosensors particularly ECG electrodes that capture the heart's electrical activity. Modern designs use flexible, dry-contact or textile-based electrodes integrated into clothing, chest bands, or adhesive patches to ensure user comfort and long-term wearability. These sensors detect minute voltage fluctuations caused by cardiac muscle depolarization and send them to a microcontroller for real-time processing. Low-power microcontrollers or System-on-Chip (SoC) platforms such as ARM Cortex-M series or ESP32 are commonly used to digitize, filter, and analyze the ECG signal. Signal preprocessing includes noise reduction (e.g., baseline wander removal, power-line interference filtering) and QRS complex detection to extract key cardiac parameters such as heart rate, Heart Rate Variability (HRV), and arrhythmia detection.
Once the physiological data is processed locally, the wearable device employs wireless communication modules (e.g., Bluetooth Low Energy, Wi-Fi, or LoRa) to transmit the information to a smartphone, gateway, or cloud-based health monitoring platform. These platforms host dashboards where clinicians or caregivers can access real-time or retrospective data to make informed medical decisions. Advanced systems incorporate machine learning algorithms that classify different cardiac rhythms (normal sinus rhythm, atrial fibrillation, ventricular tachycardia, etc.) on the edge or in the cloud. These models are trained on large annotated datasets and can achieve high accuracy in detecting anomalies with minimal false alarms. Alerts can be instantly sent to users and medical staff when critical conditions are detected, ensuring rapid response and potentially life-saving interventions [2].
User ergonomics and compliance are equally vital for system effectiveness. Devices must be lightweight, skin-friendly, and minimally invasive to encourage prolonged usage. For instance, smartwatches and fitness bands with integrated ECG functionality have gained popularity due to their familiar form factor and ease of use. However, clinical-grade accuracy remains a challenge in such consumer-grade devices. Therefore, ongoing development aims to strike a balance between comfort and clinical fidelity. Personalization is also gaining tractionâ??devices that adapt alert thresholds based on user-specific baselines or learning from longitudinal data can improve accuracy and reduce unnecessary alerts.
Google Scholar Cross Ref Indexed at
Google Scholar Cross Ref Indexed at