Abstract

Case Report

Automatic heartbeat monitoring system

Franchini Roberto*

Published: 30 September, 2019 | Volume 3 - Issue 1 | Pages: 029-034

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%.

Read Full Article HTML DOI: 10.29328/journal.acr.1001018 Cite this Article Read Full Article PDF

Keywords:

Electrocardiography; Electrodes; Android; Arduino; IoT; Heartbeat monitoring; Wavelet; Bluetooth

References

  1. Braunwald E. Heart Disease: A Textbook of Cardiovascular Medicine. 5th ed., Philadelphia: WB Saunders Co. 1997: 108.
  2. 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.
  3. 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
  4. 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.
  5. 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
  6. 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
  7. Mishra A, Chakraborty B. AD8232 based Smart Healthcare System using Internet of Things (IoT). IJERT. 2018; 7.
  8. 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.
  9. 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.
  10. Harini R, Rama Murthy B, Tanveer Alam K. Development of ECG monitoring system using Android app. IJEEE. 2017; 9: 699-707.
  11. 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.
  12. Wahane V, Ingole PV. An Android-based wireless ECG monitoring system. IEEE Healthcare Innovation Point-Of-Care Technologies Conference. Dic. 2016: 183-187
  13. 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
  14. Zheng L, Tai C. Detection of ECG characteristic points using wavelet transforms. Ieee T Bio-Med Eng. 1995; 42: 21-28.
  15. 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
  16. Awal MA, Mostafa SS, Ahmad M, Rashid MA. An adaptive level dependent wavelet thresholding for ECG denoising. Biocybern Biomed Eng. 2017; 34: 238-249.
  17. 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.
  18. 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
  19. Tompkins WJ. Biomedical Digital Signal Processing. Editorial Prentice Hall. 1993.

Figures:

Figure 1

Figure 1

Figure 1

Figure 2

Figure 1

Figure 3

Figure 1

Figure 4

Figure 1

Figure 5

Similar Articles

Recently Viewed

Read More

Most Viewed

Read More