Construcción de un sistema electrocardiográfico con conexión inalámbrica a teléfonos inteligentes
DOI:
https://doi.org/10.22517/23447214.23711Keywords:
Biomedical signal processingAbstract
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.
Downloads
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.
Downloads
-
Vistas(Views): 250
- PDF (Español (España)) Descargas(Downloads): 136
Published
How to Cite
Issue
Section
License
Copyright (c) 2021 Scientia et Technica
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Copyrights
The journal is free open access. The papers are published under the Creative Commons Attribution / Attribution-NonCommercial-NoDerivatives 4.0 International - CC BY-NC-ND 4.0 license. For this reason, the author or authors of a manuscript accepted for publication will yield all the economic rights to the Universidad Tecnológica of Pereira free of charge, taking into account the following:
In the event that the submitted manuscript is accepted for publication, the authors must grant permission to the journal, in unlimited time, to reproduce, to edit, distribute, exhibit and publish anywhere, either by means printed, electronic, databases, repositories, optical discs, Internet or any other required medium. In all cases, the journal preserves the obligation to respect, the moral rights of the authors, contained in article 30 of Law 23 of 1982 of the Government Colombian.
The transferors using ASSIGNMENT OF PATRIMONIAL RIGHTS letter declare that all the material that is part of the article is entirely free of copyright. Therefore, the authors are responsible for any litigation or related claim to intellectual property rights. They exonerate of all responsibility to the Universidad Tecnológica of Pereira (publishing entity) and the Scientia et Technica journal. Likewise, the authors accept that the work presented will be distributed in free open access, safeguarding copyright under the Creative Commons Attribution / Recognition-NonCommercial-NoDerivatives 4.0 International - https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es license.