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Comparative Analysis Between Automatic Heartbeat Monitoring System and ECG: A Review

Shubhaseesh S.

Abstract


Instrumentation for electrocardiography (ECG) is essential for making a diagnosis of heart illness. However, because this equipment is so expensive and the operation is so complicated, emerging nations cannot provide better services to their enormous populations. The health care system has seen a rapid transition as a result of the widespread use of free and open-source equipment and software, including Arduino and Raspberry PI and other microcontrollers, as well as IoT and embedded systems, resulting in the development of affordable, transportable medical equipment for monitoring vital indicators. Microcontrollers and smartphones are combined to produce an automatic system to create and implement a fully portable, affordable ECG monitoring system that is economically feasible. The product's results were evaluated by contrasting them with that of a conventional ECG used throughout clinical practice. The readings of the electrocardiography, ECG parameters and beats per minute (BPM) were identical through different parameters.


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References


Prakash V, Mr. Pandey MK. Heart rate monitoring system. J Emerg Technol Innov Res. May 2018;5(5):879–86.

Satija U, Ramkumar B, Sabarimalai Manikandan M. Real-time signal quality-aware ECG telemetry system for IoT-based health care monitoring. IEEE Internet Things J. 2017;4(3):815–23. doi: 10.1109/JIOT.2017.2670022.

Braunwald E. Heart disease: A textbook of cardiovascular medicine. 5th ed. Philadelphia: W B Saunders Company; 1997. p. 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;5967:2319.

Anderson RD, Kumar S, Parameswaran R, Wong G, Voskoboinik A, Sugumar H. et al. Differentiating right- and left-sided outflow tract ventricular arrhythmias: Classical ECG Signatures and Prediction Algorithms. Circ Arrhythm Electrophysiol. 2019;12(6):e007392. doi: 10.1161/CIRCEP.119.007392.

Shokoueinejad M, Chiang M, Lines S, Wang F, Tompkins W, Webster JG. Systematic design and HRV analysis of a portable ECG system using Arduino and LabVIEW for biomedical engineering training. IJEEE. 2017;5(5):301–11. doi: 10.18178/ijeee.5.5.301–311.

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(3):170–8. doi: 10.1080/03091902.2016.1271044.

Mishra A. Chakraborty B, Das D, Bose P. AD8232 based Smart Healthcare System using Internet of Things (IoT). Int J Eng Res Tech. 2018;7(4). doi: 10.17577/IJERTV7IS040040.

Tompkins WJ. Biomedical digital signal processing. Editorial. Prentice Hall; 1993.

Bhimasen K, Pranjal P, Parbej K, Vinay B. Design and implementation of low cost ECG monitoring system and analysis using smart. IJARSET. 2018;6:1025–9.

Harini R, Rama Murthy B, Tanveer Alam K. Development of ECG monitoring system using android app. IJEEE. 2017;9:699–707.

Wahane V, Ingole PV. An Android-based wireless ECG monitoring system. IEEE healthcare innovation point-of-care technologies conference. Dic. 2016: 183–7.

Vargas Escobar LJ, Salinas SA. E-health prototype system for cardiac telemonitoring. Annu Int Conf IEEE Eng Med Biol Soc. 2016;2016:4399–402. doi: 10.1109/EMBC.2016.7591702.

Li C, Zheng C, Tai C. Detection of ECG characteristic points using wavelet transforms. IEEE Trans Bio Med Eng. 1995;42(1):21–8. doi: 10.1109/10.362922.

Shokoueinejad M, Fernandez C, Carroll E, Wang F, Levin J, Rusk S, et al. Sleep apnea: a review of diagnostic sensors, algorithms, and therapies. Physiol Meas. 2017;38(9):R204–52. doi: 10.1088/1361–6579/aa6ec6.

Awal MA, Mostafa SS, Ahmad M, Rashid MA. An adaptive level dependent wavelet thresholding for ECG denoising. Biocybern Biomed Eng. 2014;34(4):238–49. doi: 10.1016/j.bbe.2014.03.002.

Xu MF, Wei SS, Qin XW, Zhang Y, Liu C. Rule-based method for morphological classification of ST segment in ECG signals. J Med Biol Eng. 2015;35(6):816–23. doi: 10.1007/s40846–015–0092-x.

Pan J, Tompkins WJ. A real-time QRS detection algorithm. IEEE Trans Bio Med Eng. 1985;32(3):230–6. doi: 10.1109/TBME.1985.325532.


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