Skip to main content

Advertisement

Log in

A telediagnosis assistance system for multiple-lead electrocardiography

  • Scientific Paper
  • Published:
Physical and Engineering Sciences in Medicine Aims and scope Submit manuscript

Abstract

The diffusion of telemedicine opens-up a new perspective for the development of technologies furthered by Biomedical Engineering. In particular, herein we deal with those related to telediagnosis through multiple-lead electrocardiographic signals. This study focuses on the proof-of-concept of an internet-based telemedicine system as a use case that attests to the feasibility for the development, within the university environment, of techniques for remote processing of biomedical signals for adjustable detection of myocardial ischemia episodes. At each signal lead, QRS complexes are detected and delimited with the J-point marking. The same procedure to detect the complex is used to identify the respective T wave, then the area over the ST segment is applied to detect ischemia-related elevations. The entire system is designed on web-based telemedicine services using multiuser, remote access technologies, and database. The measurements for sensitivity and precision had their respective averages calculated at 11.79 and 24.21% for the leads of lower noise. The evaluations regarding the aspects of user friendliness and the usefulness of the application, resulted in 88.57 and 89.28% of broad or total acceptance, respectively. They are robust enough to enable scalability and can be offered by cloud computing, besides enabling the development of new biomedical signal processing techniques within the concept of distance services, using a modular architecture with collaborative bias.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data availability

The datasets analysed during the current study are available in European ST-T Database, available online by Physionet repository, https://physionet.org.

Notes

  1. Web application server developed by Red-Hat, formerly known as JBoss.

Abbreviations

API:

Application programming interface

DAO:

Data access objects

DBMS:

Database manager system

NIST:

National institute of standards and technology

HTML:

Hypertext markup language

JSF:

Java server faces

JSP:

Java server pages

JDBC:

Java database connectivity

JPA:

Java persistence API

MVC:

Model-view-controller

ORM:

Object relational mapping

RMI:

Remote method invocation

PROADI-SUS:

Support program for institutional development of the unified health system

URL:

Uniform resource locator

WFDB:

Waveform database

WWW:

World wide web

References

  1. Marcolino MS, Santos TMM, Stefanelli FC et al (2017) Cardiovascular emergencies in primary care: an observational retrospective study of a large-scale telecardiology service. São Paulo Med J 135(5):481–487. https://doi.org/10.1590/1516-3180.2017.0090110617

    Article  PubMed  Google Scholar 

  2. Marcolino MS, Maia LM, Oliveira JAQ et al (2019) Impact of telemedicine interventions on mortality in patients with acute myocardial infarction: a systematic review and meta-analysis. Heart 105(19):1479–1486. https://doi.org/10.1136/heartjnl-2018-314539

    Article  PubMed  Google Scholar 

  3. Scheffer M, Biancarelli A, Cassenote A (2015) Demografia Médica No Brasil 2015. Departamento de Medicina Preventiva da Faculdade de Medicina da USP; Conselho Regional de Medicina do Estado de São Paulo; Conselho Federal de Medicina, São Paulo

  4. Brito FG, de Rodrigues AAA, Filho JBD (2017) Telemedicina como Instrumento de Soporte en la Atención Primaria a la Salud. Lat Am J Telehealth 2017:155–160

    Google Scholar 

  5. de Souza CHA, Morbeck RA, Steinman M et al (2017) Barriers and benefits in telemedicine arising between a high-technology hospital service provider and remote public healthcare units: a qualitative study in Brazil. Telemed E-Health 23(6):527–532. https://doi.org/10.1089/tmj.2016.0158

    Article  Google Scholar 

  6. Conselho Federal de Medicina. Resolução CFM No 1931 (2009) https://portal.cfm.org.br/index.php?option=com_content&view=article&id=20670:resolucao-cfm-no-19312009-&catid=9:codigo-de-etica-medica-atual&Itemid=122. Accessed 17 Feb 2019

  7. Rad M, Ghuchani S, Bahaadinbeigy K, Khalilzadeh M (2015) Real time recognition of heart attack in a smart phone. Acta Inform Medica 23(3):151. https://doi.org/10.5455/aim.2015.23.151-154

    Article  Google Scholar 

  8. Tseng Y-L, Lin K-S, Jaw F-S (2016) Comparison of support-vector machine and sparse representation using a modified rule-based method for automated myocardial ischemia detection. Comput Math Methods Med 2016:1–8. https://doi.org/10.1155/2016/9460375

    Article  Google Scholar 

  9. Gimeno-Blanes FJ, Blanco-Velasco M, Barquero-Pérez Ó, García-Alberola A, Rojo-Álvarez JL (2016) Sudden cardiac risk stratification with electrocardiographic indices‗a review on computational processing, technology transfer, and scientific evidence. Front Physiol. https://doi.org/10.3389/fphys.2016.00082

    Article  PubMed  PubMed Central  Google Scholar 

  10. Peng Z, Wang G (2017) A novel ECG eigenvalue detection algorithm based on wavelet transform. Biomed Res Int 2017:1–12. https://doi.org/10.1155/2017/5168346

    Article  CAS  Google Scholar 

  11. Lines GT, de Oliveira BL, Skavhaug O, Maleckar MM (2017) Simple T-wave metrics may better predict early ischemia as compared to ST segment. IEEE Trans Biomed Eng 64(6):1305–1309. https://doi.org/10.1109/TBME.2016.2600198

    Article  PubMed  Google Scholar 

  12. Wang JJ, Pahlm O, Warren JW, Sapp JL, Horáček BM (2018) Criteria for ECG detection of acute myocardial ischemia: sensitivity versus specificity. J Electrocardiol 51(6):S12–S17. https://doi.org/10.1016/j.jelectrocard.2018.08.018

    Article  PubMed  Google Scholar 

  13. Good WW, Erem B, Zenger B, Coll-Font J, Brooks DH, MacLeod RS (2018) Temporal performance of laplacianeigenmaps and 3D conduction velocity in detecting ischemic stress. J Electrocardiol 51(6):S116–S120. https://doi.org/10.1016/j.jelectrocard.2018.08.017

    Article  PubMed  PubMed Central  Google Scholar 

  14. Yang Z, Zhou Q, Lei L, Zheng K, Xiang W (2016) An IoT-cloud based wearable ECG monitoring system for smart healthcare. J Med Syst. https://doi.org/10.1007/s10916-016-0644-9

    Article  PubMed  Google Scholar 

  15. Hwang HC, Park J, Shon JG (2016) Design and implementation of a reliable message transmission system based on MQTT protocol in IoT. Wirel Pers Commun 91(4):1765–1777. https://doi.org/10.1007/s11277-016-3398-2

    Article  Google Scholar 

  16. Marouf M, Vukomanovic G, Saranova L, Bozic M (2017) Multipurpose ECG telemetry system. Biomed Eng Online. https://doi.org/10.1186/s12938-017-0371-6

    Article  PubMed  PubMed Central  Google Scholar 

  17. Almadani B, Saeed B, Aalroubaiy A (2016) Healthcare systems integration using real time publish subscribe (RTPS) middleware. Comput Electr Eng 50:67–78. https://doi.org/10.1016/j.compeleceng.2015.12.009

    Article  Google Scholar 

  18. Ahmadi H, Arji G, Shahmorad L, Safdari R, Nilashi M, Alizadeh M (2018) The application of internet of things in healthcare: a systematic literature review and classification. Univ Access Inf Soc. https://doi.org/10.1007/s10209-018-0618-4

    Article  Google Scholar 

  19. Griebel L, Prokosch H-U, Köpcke F et al (2015) A scoping review of cloud computing in healthcare. BMC Med Inform DecisMak. https://doi.org/10.1186/s12911-015-0145-7

    Article  Google Scholar 

  20. Goldberger AL, Amaral LAN, Glass L et al (2000) PhysioBank, physiotoolkit, and physionet components of a new research resource for complex physiologic signals. Circulation 101(23):e215–e220. https://doi.org/10.1161/01.CIR.101.23.e215

    Article  CAS  PubMed  Google Scholar 

  21. Krishna V, Jose J, Suri NR (2014) Design and development of a web-enabled data mining system employing JEE technologies. Sadhana 39(6):1259–1270

    Article  Google Scholar 

  22. Heuser CA (2009) Projeto de Banco de Dados, 6th edn. Bookman, Porto Alegre

    Google Scholar 

  23. Souza A (2015) Java EE: aproveitetoda a plataforma para construiraplicações. Casa do Código, São Paulo

    Google Scholar 

  24. Cordeiro G (2012) Aplicações Java para a web com JSF e JPA. Casa do Código, São Paulo

    Google Scholar 

  25. Francesca S, Carlo CG, Di Nunzio L, Rocco F, Marco R (2018) Comparison of low-complexity algorithms for real-time QRS detection using standard ECG database. Int J AdvSciEngInfTechnol 8(2):307. https://doi.org/10.18517/ijaseit.8.2.4956

    Article  Google Scholar 

  26. Kutner MH, Nachtsheim CJ, Neter J, Li W (2005) Applied linear statical models, 5th edn. McGraw-Hill, New York

    Google Scholar 

  27. García ÁL, del Castillo EF (2013) Analysis of scientific cloud computing requirements. http://arxiv.org/abs/1309.6109. Accessed 28 Sept 2018

  28. Balalaie A, Heydarnoori A, Jamshidi P (2016) Microservices architecture enables devops: migration to a cloud-native architecture. IEEE Softw 33(3):42–52. https://doi.org/10.1109/MS.2016.64

    Article  Google Scholar 

  29. Vacari I, de Apolinário DRF, Gonzales LE (2017) Java EE Microsserviços com Wildfly Swarm. Comun Téc 127 Embrapa. 2017:11

    Google Scholar 

  30. Hu X, Xiao Z, Zhang N (2011) Removal of baseline wander from ECG signal based on a statistical weighted moving average filter. J Zhejiang UnivSci C 12(5):397–403. https://doi.org/10.1631/jzus.C1010311

    Article  Google Scholar 

  31. Ojo J, Adetoyi T, Adeniran S (2016) Removal of Baseline wander noise from electrocardiogram (ECG) using fifth-order spline interpolation. J ApplComputSci Math 10(2):9–14. https://doi.org/10.4316/JACSM.201602001

    Article  Google Scholar 

  32. Hernandes E, Zamboni A, Thommazo AD, Fabbri S (2010) Avaliação da ferramenta StArt utilizando o modelo TAM e o paradigma GQM. In: Proceedings of 7th Experimental Software Engineering Latin American Workshop. ESELAW’10, Goiânia, pp 30–39

  33. Davis FD (1993) User acceptance of information technology: system characteristics, user perceptions and behavioral impacts. Int J Man Mach Stud 38(3):475–487. https://doi.org/10.1006/imms.1993.1022

    Article  Google Scholar 

  34. Basili V, Caldiera G, Rombach HD (1994) Goal question metric paradigm. Wiley, Hoboken

    Google Scholar 

  35. Drew BJ, Pelter MM, Lee E, Zegre J, Schindler D, Fleis-Chmann KE (2005) Designing prehospital ECG systems for acute coronary syndromes. Lessons learned from clinical trials involving 12-lead ST-segment monitoring. J Electrocardiol 38(4):180–185. https://doi.org/10.1016/j.jelectrocard.2005.06.031

    Article  PubMed  Google Scholar 

  36. Murthy AS, Seelamantula CS, Sreenivas TV (2016) Optimum short-time polynomial regression for signal analysis. Sādhanā 41(11):1245–1260

    Article  Google Scholar 

  37. Agarwal S, Rani A, Singh V, Mittal AP (2016) Performance evaluation and implementation of FPGA based SGSF in smart diagnostic applications. J Med Syst. https://doi.org/10.1007/s10916-015-0404-2

    Article  PubMed  Google Scholar 

  38. Tan X, Chen X, Ren R, et al (2013) Real-time baseline wander removal in ECG signal based on weighted local linear regression smoothing. In: Proceedings of information and automation (ICIA), 2013 IEEE International Conference on IEEE. pp 453–456

  39. Dotsinsky IA, Mihov GS (2008) Tremor suppression in ECG. Biomed EngOnLine 7(1):29. https://doi.org/10.1186/1475-925X-7-29

    Article  Google Scholar 

  40. Acharya D, Rani A, Agarwal S, Singh V (2016) Application of adaptive Savitzky-Golay filter for EEG signal processing. PerspectSci 8:677–679. https://doi.org/10.1016/j.pisc.2016.06.056

    Article  Google Scholar 

  41. Mitra S, Mitra M, Chaudhuri BB (2009) Pattern defined heuristic rules and directional histogram based online ECG parameter extraction. Measurement 42(1):150–156. https://doi.org/10.1016/j.measurement.2008.05.002

    Article  Google Scholar 

  42. Guyton AC, Hall JE (2006) Tratado de FisiologiaMédica, 11th edn. Elsevier, Rio de Janeiro

    Google Scholar 

  43. Al-Zoube MA, Alqudah YA (2014) Mobile cloud computing framework for pattients’ health data analysis. Biomed EngAppl Basis Commun 26(02):1450020. https://doi.org/10.4015/S1016237214500203

    Article  Google Scholar 

  44. Gao F, Thiebes S, Sunyaev A (2018) Rethinking the meaning of cloud computing for health care: a taxonomic perspective and future research directions. J Med Internet Res 20(7):e10041. https://doi.org/10.2196/10041

    Article  PubMed  PubMed Central  Google Scholar 

  45. Puelacher C, Wagener M, Abächerli R et al (2017) Diagnostic value of ST-segment deviations during cardiac exercise stress testing: systematic comparison of different ECG leads and time-points. Int J Cardiol 238:166–172. https://doi.org/10.1016/j.ijcard.2017.02.079

    Article  PubMed  Google Scholar 

  46. Herland M, Khoshgoftaar TM, Wald R (2014) A review of data mining using big data in health informatics. J Big Data 1(1):2. https://doi.org/10.1186/2196-1115-1-2

    Article  Google Scholar 

  47. Prosperi M, Min JS, Bian J, Modave F (2018) Big data hurdles in precision medicine and precision public health. BMC Med Inform DecisMak. https://doi.org/10.1186/s12911-018-0719-2

    Article  Google Scholar 

Download references

Acknowledgements

This study received financial support from the Brazilian agencies FINEP and CNPq.

Funding

Funds from FINEP and CNPq agencies were used in the final revision of the text in English.

Author information

Authors and Affiliations

Authors

Contributions

PCLB carried out the research that gave rise to the method of processing biomedical signals, carried out the prospecting and execution of the computational techniques used, performed the tests with the signals and wrote the initial text of the article. JN guided all research and revised the entire text of the article.

Corresponding author

Correspondence to Paulo César Lucena Bentes.

Ethics declarations

Conflict of interest

JN received financial support from the Brazilian agencies FINEP and CNPq.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bentes, P.C.L., Nadal, J. A telediagnosis assistance system for multiple-lead electrocardiography. Phys Eng Sci Med 44, 473–485 (2021). https://doi.org/10.1007/s13246-021-00996-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13246-021-00996-2

Keywords

Navigation