Automatic heartbeat monitoring system
Article Sidebar
Main Article Content
Abstract
The proliferation and popularity of open source hardware and software, such as Arduino and Raspberry PI, together with IoT and Embedded System, has brought the health industry to rapid evolution, creating portable and low-cost medical devices for monitoring vital signals. Electrocardiographic (ECG) equipment plays a vital role for diagnosis of cardiac disease. However, the cost of this equipment is huge and the operation is too much complex which cannot offer better services to a large population in developing countries. In this paper, I have designed and implemented a low cost fully portable ECG monitoring system using android smartphone and Arduino. The results obtained by the device were tested comparing them with those obtained from a traditional ECG used in clinical practice on 70 people, in resting and under-activity conditions. The values of beats per minute (BPM), ECG waveform and ECG parameters were identical, and presented a sensitivity of 97.8% and a specificity of 78.52%.
Article Details
Copyright (c) 2019 Roberto F.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The Archives of Case Reports is committed in making it easier for people to share and build upon the work of others while maintaining consistency with the rules of copyright. In order to use the Open Access paradigm to the maximum extent in true terms as free of charge online access along with usage right, we grant usage rights through the use of specific Creative Commons license.
License: Copyright © 2017 - 2025 | 
Open Access by Archives of Case Reports is licensed under a Creative Commons Attribution 4.0 International License. Based on a work at Heighten Science Publications Inc.
With this license, the authors are allowed that after publishing with the journal, they can share their research by posting a free draft copy of their article to any repository or website.
Compliance 'CC BY' license helps in:
| Permission to read and download | ✓ |
| Permission to display in a repository | ✓ |
| Permission to translate | ✓ |
| Commercial uses of manuscript | ✓ |
'CC' stands for Creative Commons license. 'BY' symbolizes that users have provided attribution to the creator that the published manuscripts can be used or shared. This license allows for redistribution, commercial and non-commercial, as long as it is passed along unchanged and in whole, with credit to the author.
Please take in notification that Creative Commons user licenses are non-revocable. We recommend authors to check if their funding body requires a specific license.
Braunwald E. Heart Disease: A Textbook of Cardiovascular Medicine. 5th ed., Philadelphia: WB Saunders Co. 1997: 108.
Naazneen MG, Fathima S, Mohammadi SH, Indikar SIL, Saleem A, et al. Design and Implementation of ECG Monitoring and Heart Rate Measurement System. IJARSE 2013; 2: 2319-5967.
Anderson RD, Kumar S, Parameswaran R, Wong G, Voskoboinik A. et al. Differentiating Right- and Left-Sided Outflow Tract Ventricular Arrhythmias. Circ Arrhythm Electrophysiol. 2019; 12: e007392. PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31159581
Shokoueinejad M, Chiang M, Lines F. Wang F, Tompkins W, et al. Systematic Design and HRV Analysis of a Portable ECG System Using Arduino and LabVIEW for Biomedical Engineering Training. IJEEE 2017; 5: 301-311.
Martinez-Millana A, Palao C, Fernandez-Llatas C, de Carvalho P, Bianchi AM, et al. Integrated IoT intelligent system for the automatic detection of cardiac variability. Conf Proc IEEE Eng Med Biol Soc. 2018; 5798-5801. PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30441653
Das S, Pal S, Mitra M. Arduino-based noise robust online heart-rate detection. J Med Eng Technol. 2017; 41: 170-178. PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28078906
Mishra A, Chakraborty B. AD8232 based Smart Healthcare System using Internet of Things (IoT). IJERT. 2018; 7.
Shokoueinejad M, Chiang M, Lines S, Wang F, Tompkins W, et aL. Systematic Design and HRV Analysis of a Portable ECG System Using Arduino and Lab View for Biomedical Engineering Training. IJEEE. 2017; 5: 301-311.
Bhimasen K, Pranjal P, Parbej K, Vinay B. Design and Implementation of Low Cost ECG Monitoring System and Analysis using Smart. IJARSET. 2018; 6: 1025-1029.
Harini R, Rama Murthy B, Tanveer Alam K. Development of ECG monitoring system using Android app. IJEEE. 2017; 9: 699-707.
Satija U, Ramkumar B, Sabarimalai Manikandan M. Real-Time Signal Quality-Aware ECG Telemetry System for IoT-Based Health Care Monitoring. Ieee Internet of Things Journal. 2017; 4: 815-823.
Wahane V, Ingole PV. An Android-based wireless ECG monitoring system. IEEE Healthcare Innovation Point-Of-Care Technologies Conference. Dic. 2016: 183-187
Vargas Escobar LJ, Salinas SA. e-Health prototype system for cardiac telemonitoring. Conf Proc IEEE Eng Med Biol Soc. 2016; 4399-4402.PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28269253
Zheng L, Tai C. Detection of ECG characteristic points using wavelet transforms. Ieee T Bio-Med Eng. 1995; 42: 21-28.
Shokoueinejad M, Fernandez C, Carroll E, Wang F, Levin J, et al. Sleep apnea: A review of diagnostic sensors, algorithms, and therapies. Physiological Measurement. 2017; 38: R204- R252. PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28820743
Awal MA, Mostafa SS, Ahmad M, Rashid MA. An adaptive level dependent wavelet thresholding for ECG denoising. Biocybern Biomed Eng. 2017; 34: 238-249.
Xu MF, Wei SS, Qin XW. et al. Rule-based method for morphological classification of ST segment in ECG signals. J Med Biol Eng. 2015; 35: 816–823.
Pan J, Tompkins WJ. A real-time QRS detection algorithm. Ieee T Bio-Med Eng. 1985; 230-236. PubMed: https://www.ncbi.nlm.nih.gov/pubmed/3997178
Tompkins WJ. Biomedical Digital Signal Processing. Editorial Prentice Hall. 1993.