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IoT-Based Real-Time ECG Monitoring and Heart Disease Detection Using Raspberry Pi

Author

Listed:
  • Divekar S.N
  • Akshada Wable
  • Pratiksha Gaikwad
  • Nilesh Mhaske
  • Prathamesh Patil

Abstract

Cardiovascular diseases (CVDs) remain one of the leading causes of mortality worldwide, necessitating continuous and real-time heart health monitoring for early diagnosis and timely intervention. Conventional electrocardiogram (ECG) monitoring systems are typically limited to clinical environments and require specialized equipment, restricting continuous patient observation. This paper presents an IoT-based real-time ECG monitoring and heart disease detection system using Raspberry Pi. The proposed system acquires ECG signals through biomedical sensors interfaced with a Raspberry Pi, where the signals are processed and analyzed. The processed data is transmitted to a cloud platform using Internet of Things (IoT) technology, enabling remote monitoring and data accessibility. The system is capable of detecting cardiac abnormalities such as arrhythmia, tachycardia, and bradycardia. Upon detection of abnormal conditions, real-time alerts are generated and sent to healthcare providers or caregivers. The cloud-integrated architecture allows medical professionals to access patient ECG data remotely, enhancing healthcare accessibility and enabling prompt medical intervention. Experimental results demonstrate reliable ECG signal acquisition, accurate disease detection, low latency, and efficient remote monitoring performance. The proposed system offers a cost-effective, portable, and user-friendly solution for continuous cardiac health monitoring and early heart disease detection, making it suitable for home-based and remote healthcare applications.

Suggested Citation

  • Divekar S.N & Akshada Wable & Pratiksha Gaikwad & Nilesh Mhaske & Prathamesh Patil, 2026. "IoT-Based Real-Time ECG Monitoring and Heart Disease Detection Using Raspberry Pi," International Journal of Scientific Research in Artificial Intelligence and Machine Learning, International Journal of Scientific Research in Artificial Intelligence and Machine Learning, vol. 2(3), pages 340-347, May.
  • Handle: RePEc:jbo:ijsrml:v2:y2026:i3:id:85
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