Construcción de un sistema electrocardiográfico con conexión inalámbrica a teléfonos inteligentes


Authors

DOI:

https://doi.org/10.22517/23447214.23711

Keywords:

Biomedical signal processing

Abstract

According to the World Health Organization, cardiovascular diseases are the leading cause of death worldwide. For prevention, diagnosis and treatment of heart disease, a medical examination known as electrocardiogram is required, the exam records the electrical activity of the heart and is acquired through a device called an electrocardiograph. In the same way, there is a growing motivation towards the development of new technologies to monitor health and ensure the general well-being of the population, which is speeded up through the rise and advancement of mobile devices. This paper presents the design and implementation of an electrocardiograph which allows the graphic display of the electrocardiographic signal on a mobile device with Android operating system and has an interface to a personal computer where the signals obtained are deployed, processed and analyzed. To build the device, an evolutionary-incremental methodology was followed. The functioning of the system was evaluated in the detection of arrhythmias and acute myocardial infarction; achieving performance indicators of TPR = 87.50% for signals with arrhythmias and TPR = 92.59% for signals with infarction. In this way, information can be captured, processed, parameterized, transmitted, stored in integral health computer systems and used to perform diagnostics by remote specialists; profiling, this system, as alternatives for the diagnosis, care and monitoring of people who have heart problems.

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References

Ruonan, L., Boyuan, Y., Hauptmann, G. Simultaneous bearing faultrecognition and remaining useful life prediction using joint-loss convolutional neural network. IEEE Transactions on industrial informatics, vol. 16, No. 1, pp 87-96, January 2020.

IEA, Change in final energy consumption by sector, 2000-2018, and by

scenario to 2040, IEA, Paris https://www.iea.org/data-andstatistics/charts/change-in-final-energy-consumption-by-sector-2000- 2018-and-by-scenario-to-2040.

Gougam, F., Rahmoune, C., Benazzouz, D., Varnier, C., Nicod. J-M.

Health monitoring approach of bearing: application of adaptive neuro

fuzzy inference system (ANFIS) for RUL-estimation and autogram

analysis for fault-localization. 2020 Prognostics and Health Management

Conference. 2020.

Xiaohang, J., Zijun, Q., Yi, S. Development of vibration-based health

indexes for bearing remaining useful life prediction. Prognostics &

System Health Management Conference—Qingdao. 2019.

Jin, X., Que, Z., Sun, Y., Guo, Y., Qiao, W. “A data-driven approach for

bearing fault prognostics,” IEEE Trans. on Ind. Appl., vol.55, no. 4, pp.

-3401, July/August 2019.

Jinbing, L., Kai, Z., Min, H., Jun, W. Reconstruction-based fault

prognosis for bearings with principal component analysis. Proceedings of

the 38th Chinese Control Conference July 27-30, Guangzhou, China.

Elforjani, M., Shanbr, S. Prognosis of bearing acoustic emission signals using supervised machine learning. IEEE Trans. on Industrial Electronics,

(7), pp. 5864–5871, 2018.

Sikorska, J., Hodkiewicz, M., Ma, L.: Prognostic modelling options for remaining useful life estimation by industry. MSSP 25(5) (2011) 1803 –

Janjarasjitt, S., Ocak, H., Loparo, K.: Bearing condition diagnosis and

prognosis using applied nonlinear dynamical analysis of machine

vibration signal. Journal of Sound and Vibration 317(1–2) (2008) 112 –

Attoui, I., Boutasseta, N., Fergani, N., Oudjani, B., Deliou, A.: Vibrationbased bearing fault diagnosis by an integrated dwt-fft approach and an adaptive neuro-fuzzy inference system. In: CEIT, 2015 3rd International Conference on. (May 2015) 1–6.

Jardine, A.K., Lin, D., Banjevic, D.: A review on machinery diagnostics

and prognostics implementing condition-based maintenance. MSSP 20(7)

(2006) 1483 – 1510.

Darley, S., Robson, P.: Application of wavelet transformto detect faults

in rotatingmachinery. In: ABCM Symposium Series in Mechatronics.

(2004) 616–624.

Cococcioni, M., Lazzerini, B., Volpi, S.L.: Robust diagnosis of rolling element bearings based on classification techniques. IEEE Transactions 9(4) (Nov 2013) 2256–2263.

Zaidi, S.S.H., Aviyente, S., Salman,M., Shin, K.K., Strangas, E.G.: Prognosis of gear failures in dc starter motors using hmm. IEEE Transactions 58(5) (May 2011) 1695–1706.

Cardona, O.: Análisis tiempo-frecuencia de señales de vibraciones

mecánicas para la detección de fallos en máquinas rotativas. Master’s

thesis, Automatización Industrial, Universidad Nacional de Colombia,

Manizales, Colombia (2011).

Nelwamondo, F.V., Marwala, T.: Faults detection using gaussian mixture

models, melfrequency cepstral coefficients and kurtosis. In: 2006 IEEE

International Conference on Systems, Man and Cybernetics. Volume 1.

(Oct 2006) 290–295.

Muda, L., Begam, M., Elamvazuthi, I.: Voice Recognition Algorithms

using Mel Frequency, Cepstral Coefficient (MFCC) and Dynamic Time

Warping (DTW) Techniques. Journal of computing. Vol. 2, Issue 3. ISSN:

-9617. March, 2010.

Ephraim, Y., Merhav, N.: Hidden markov processes. IEEE Transactions

on Information Theory 48(6) (Jun 2002) 1518–1569.

Samko, O., Marshall, A.D., Rosin, P.L.: Automatic construction of hierarchical hidden markov model structure for discovering semantic patterns in motion data. In: Proceedings of VISIGRAPP 2010. (2010) 275–280.

Loparo, K.: Seeded fault test data, bearing data center, case western

reserve university.http://csegroups.case.edu/bearingdatacenter/pages/download-data-file.

Castaño, J.C., Agudelo, C.H.: "Extensibilidad de criterios de decisión para

un clasificador de fallos en rodamientos basado en HMM, sobre resultados en un clasificador de menos estados, empleando características en diferentes espacios de representación". Undergraduate thesis. Electric Engineering, Universidad Tecnológica de Pereira. T621.31042 C346; 6310000108472 F3256, 2015. URL: http://hdl.handle.net/11059/5198.

Kolesnikov. A., Trichina. E.: Determining the Number of Clusters with

Rate-Distortion Curve Modeling. Image Analysis and Recognition Lecture Notes in Computer Science, Vol. 7324, pp 43-50, 2012.

Boudiaf, A., Moussaoui, A., Dahane, A., Atoui, I.: A Comparative Study

of Various Methods of Bearing Faults Diagnosis Using the Case Western

Reserve University Data. J Fail. Anal. and Preven. (2016) 16: 271–284.

DOI: 10.1007/s11668-016-0080-7.

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Published

2021-09-30

How to Cite

Vargas-Cañas, R., Muñoz, A. R., & Campo Cuaran, W. A. (2021). Construcción de un sistema electrocardiográfico con conexión inalámbrica a teléfonos inteligentes. Scientia Et Technica, 26(03), 269–277. https://doi.org/10.22517/23447214.23711