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

Divekar S.n, Akshada Wable, Pratiksha Gaikwad, Nilesh Mhaske and Prathamesh Patil

International Journal of Scientific Research in Artificial Intelligence and Machine Learning, 2026, vol. 2, issue 3, 340-347

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.

Keywords: Internet of Things (IoT); ECG Monitoring; Raspberry Pi; Heart Disease Detection; Remote Healthcare; Real-Time Monitoring (search for similar items in EconPapers)
Date: 2026
Note: Article URL: https://ijsraiml.com/home/article/view/IJSRAIML262322
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ijsraiml.com/home/article/view/IJSRAIML262322 Article URL (text/html)
https://ijsraiml.com/home/article/download/IJSRAIML262322/IJSRAIML262322 Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:jbo:ijsrml:v2:y2026:i3:id:85

Access Statistics for this article

More articles in International Journal of Scientific Research in Artificial Intelligence and Machine Learning from International Journal of Scientific Research in Artificial Intelligence and Machine Learning
Bibliographic data for series maintained by Pankaj Sharma ().