Advertisement

Journal of Medical and Biological Engineering

, Volume 38, Issue 6, pp 1026–1045 | Cite as

A Collaborative Framework for Sensing Abnormal Heart Rate Based on a Recommender System: Semantic Recommender System for Healthcare

  • Giovanni Guzmán
  • Miguel Torres-Ruiz
  • Vianney Tambonero
  • Miltiadis D. Lytras
  • Blanca López-Ramírez
  • Rolando Quintero
  • Marco Moreno-Ibarra
  • Wadee Alhalabi
Original Article
  • 55 Downloads

Abstract

According to different studies, people are not able to identify the physiological symptoms related to high risk cardiovascular condition that could require medical attention. In consequence, when they see a medical doctor, the heart damage could be quite advanced. Moreover, there are several studies focused on applying the Framingham or systematic coronary risk evaluation indexes; however, the combination with other physiological variables such as lifestyle, current activity, and maximal heart rate has not been deeply studied in the state-of-the-art. This paper proposes a collaborative framework for sensing physiological variables to determine possible high risk cardiovascular conditions, it will also provide a weighted ranking list of medical speciality centers. The framework will consist of two stages: in the first one, an ubiquitous heart rate monitoring by using an ID3 decision tree is applied to classify sensed-data for identifying the presence of a high risk cardiovascular condition. The second stage proposes a recommender system leading towards extracting and clustering of a set of hospitals, in which the medical specialities are defined in an application ontology. The clustering process matches the hospital attention factor, in order to estimate the number of possible medical doctors and the required cardiovascular medical speciality. In conclusion, the proposal applies different decision trees such as ID3, J48, NBTree, and BFTree in order to evaluate and compare the classification performance. The effectiveness of the ID3 decision tree was 85.71%.

Keywords

Healthcare Monitoring Bluetooth sensor device Recommender system Medical ontology Decision tree 

Notes

Acknowledgements

This work was partially sponsored by the Instituto Politécnico Nacional (IPN), Consejo Nacional de Ciencia y Tecnología (CONACyT) under grant PN-2016/2110, and the Secretaría de Investigación y Posgrado (SIP) under Grants Nos. 20180308, 20181568, 20182159 and 20180409. Additionally, we are thankful to the reviewers for their invaluable and constructive feedback that helped improve the quality of the paper.

References

  1. 1.
    United Nations. (2010). Department of Economic. World population ageing 2009. New York: United Nations Publications.Google Scholar
  2. 2.
    World Health Organization. (2009). Global status report on road safety: time for action. Geneva: World Health Organization.Google Scholar
  3. 3.
    Beaglehole, R., Epping-Jordan, J., Patel, V., Chopra, M., Ebrahim, S., Kidd, M., et al. (2008). Improving the prevention and management of chronic disease in low-income and middle-income countries: A priority for primary health care. The Lancet, 372(9642), 940–949.CrossRefGoogle Scholar
  4. 4.
    World Health Organization et al. (2011) The top 10 causes of death. Geneva: World Health Organization.Google Scholar
  5. 5.
    Kendall, K. E., Kendall, J. E. (2007). Systems analysis and design, (Vol. 7). New Jersey: Prentice-Hall.Google Scholar
  6. 6.
    Mihailidis, A., & Bardram, J. E. (2010). Pervasive computing in healthcare. Boca Raton: CRC Press.Google Scholar
  7. 7.
    Noguera, J. M., Barranco, M. J., Segura, R. J., & Martínez, L. (2012). A mobile 3D-GIS hybrid recommender system for tourism. Information Sciences, 215, 37–52.CrossRefGoogle Scholar
  8. 8.
    Kumar, S., Kambhatla, K., Fei, H., Lifson, M., & Xiao, Y. (2008). Ubiquitous computing for remote cardiac patient monitoring: A survey. International Journal of Telemedicine and Applications, 2008, 3.CrossRefGoogle Scholar
  9. 9.
    Griffiths, E., Saponas, T. S., & Brush, A. J. (2014). Health chair: Implicitly sensing heart and respiratory rate. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing (pp. 661–671). ACM.Google Scholar
  10. 10.
    Hamedani, K., Bahmani, Z., & Mohammadian, A. (2016). Spatio-temporal filtering of thermal video sequences for heart rate estimation. Expert Systems with Applications, 54, 88–94.CrossRefGoogle Scholar
  11. 11.
    Ravichandran, R., Rahman, T., Adams, A., Choudhury, T., Kientz, J., & Patel, S. (2015). Real time heart rate and breathing detection using commercial motion sensors. In Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers (pp. 325–328). ACM.Google Scholar
  12. 12.
    Tamura, T., Maeda, Y., Sekine, M., & Yoshida, M. (2014). Wearable photoplethysmographic sensors-past and present. Electronics, 3(2), 282–302.CrossRefGoogle Scholar
  13. 13.
    Tu, L., Huang, J., Bi, C., & Xing, G. (2017). Fitbeat: A lightweight system for accurate heart rate measurement during exercise. In 2017 IEEE International Conference on Smart Computing (SMARTCOMP) (pp 1–8). IEEE.Google Scholar
  14. 14.
    Temko, A. (2017). Accurate heart rate monitoring during physical exercises using PPG. IEEE Transactions on Biomedical Engineering, 64(9), 2016–2024.CrossRefGoogle Scholar
  15. 15.
    Chai, J. (2013). The design of mobile ECG monitoring system. In 2013 IEEE 4th International Conference on Electronics Information and Emergency Communication (ICEIEC) (pp. 148–151). IEEE.Google Scholar
  16. 16.
    Alzate, E. B., & Martinez, F. M. (2010). ECG monitoring system based on ARM9 and mobile phone technologies. In 2010 IEEE on ANDESCON (pp. 1–6). IEEE.Google Scholar
  17. 17.
    Kai, L., Zhang, X., Wang, Y., Suibiao, H., Ning, G., & Wangyong, P., et al. (2011). A system of portable ecg monitoring based on bluetooth mobile phone. In 2011 International Symposium on IT in Medicine and Education (ITME) (Vol. 2, pp. 309–312). IEEE.Google Scholar
  18. 18.
    Qidwai, U., Chaudhry, J. A., Shakir, M., & Rittenhouse, R. G. (2012). Ubiquitous monitoring system for critical cardiac abnormalities. In Computer Applications for Bio-technology, Multimedia, and Ubiquitous City (pp. 124–134.) Berlin: Springer.Google Scholar
  19. 19.
    Patel, A. M., Gakare, P. K., & Cheeran, A. N. (2012). Real time ECG feature extraction and arrhythmia detection on a mobile platform. International Journal of Computer Applications, 44(23), 40–45.Google Scholar
  20. 20.
    Gradl, S., Kugler, P., Lohmuller, C., & Eskofier, B. (2012). Real-time ECG monitoring and arrhythmia detection using android-based mobile devices. In 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 2452–2455). IEEE.Google Scholar
  21. 21.
    Pandey, S., Voorsluys, W., Niu, S., Khandoker, A., & Buyya, R. (2012). An autonomic cloud environment for hosting ECG data analysis services. Future Generation Computer Systems, 28(1), 147–154.CrossRefGoogle Scholar
  22. 22.
    Salvador, C. H., Carrasco, M. P., De Mingo, M. G., Carrero, A. M., & Montes, J. M., et al. (2005). Airmed-cardio: A GSM and internet services-based system for out-of-hospital follow-up of cardiac patients. IEEE Transactions on Information Technology in Biomedicine, 9(1), 73–85.CrossRefGoogle Scholar
  23. 23.
    Jones, V., Van Halteren, A., Dokovsky, N., Koprinkov, G., Peuscher, J., & Bults, R., et al. (2006). Mobihealth: Mobile services for health professionals. In M-Health (pp. 237–246). Boston: Springer.Google Scholar
  24. 24.
    Shih, D.-H., Chiang, H.-S., Lin, B., & Lin, S.-B. (2010). An embedded mobile ECG reasoning system for elderly patients. IEEE Transactions on Information Technology in Biomedicine, 14(3), 854–865.CrossRefGoogle Scholar
  25. 25.
    Ren, Y., Werner, R., Pazzi, N., & Boukerche, A. (2010). Monitoring patients via a secure and mobile healthcare system. IEEE Wireless Communications, 17(1), 59–65.CrossRefGoogle Scholar
  26. 26.
    Gay, V., & Leijdekkers, P. (2007). A health monitoring system using smart phones and wearable sensors. International Journal of ARM, 8(2), 29–35.Google Scholar
  27. 27.
    Bao, X., Chen, X., Fang, Z., & Xia, S. (2016). Design and development of a ubiquitous healthcare monitoring system based on android platform. DEStech Transactions on Engineering and Technology Research, (ICMITE2016).Google Scholar
  28. 28.
    Mohamed, R., & Youssef, M. (2017). Heartsense: Ubiquitous accurate multi-modal fusion-based heart rate estimation using smartphones. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 1(3), 97.CrossRefGoogle Scholar
  29. 29.
    Novo, J., Hermida, A., Ortega, M., Barreira, N., & Penedo, M., et al. (2017). A web-based system for cardiovascular analysis, diagnosis and treatment. Computer Methods and Programs in Biomedicine, 139, 61–81.CrossRefGoogle Scholar
  30. 30.
    Epping-Jordan, J. E., Pruitt, S. D., Bengoa, R., & Wagner, E. H. (2004). Improving the quality of health care for chronic conditions. Quality and Safety in Health Care, 13(4), 299–305.CrossRefGoogle Scholar
  31. 31.
    Paganelli, F., & Giuli, D. (2007). An ontology-based context model for home health monitoring and alerting in chronic patient care networks. AINA Workshops, 2, 838–845.Google Scholar
  32. 32.
    Appventive LLC. (2014). In case of emergency (ICE).Google Scholar
  33. 33.
    Bottazzi, D., Corradi, A., & Montanari, R. (2006). Context-aware middleware solutions for anytime and anywhere emergency assistance to elderly people. IEEE Communications Magazine, 44(4), 82–90.CrossRefGoogle Scholar
  34. 34.
    Wang, S., Ji, L., Li, A., & Jiankang, W. (2011). Body sensor networks for ubiquitous healthcare. Journal of Control Theory and Applications, 9(1), 3–9.MathSciNetCrossRefGoogle Scholar
  35. 35.
    Yuan, B., & Herbert, J. (2011). Web-based real-time remote monitoring for pervasive healthcare. In 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops) (pp. 625–629). IEEE.Google Scholar
  36. 36.
    Soto, J. (2011). Plataforma de geolocalización de centros de salud con tecnología móvil implementando el protocolo de comunicación hl7. TELEMATIQUE, 9(3), 79–101.Google Scholar
  37. 37.
    Banerjee, S., & Mitra, M. (2014). A cross wavelet transform based approach for ECG feature extraction and classification without denoising. In 2014 International Conference on Control, Instrumentation, Energy and Communication (CIEC) pp. 162–165. IEEE.Google Scholar
  38. 38.
    Zeybekoglu, S., & Özkan, M. (2010). Classification of ECG arrythmia beats with artificial neural networks. In 2010 15th National Biomedical Engineering Meeting (BIYOMUT) (pp. 1–4). IEEE.Google Scholar
  39. 39.
    Arif, M., Malagore, I. A., & Afsar, F. A .(2010). Automatic detection and localization of myocardial infarction using back propagation neural networks. In 2010 4th International Conference on Bioinformatics and Biomedical Engineering (iCBBE) (pp. 1–4). IEEE.Google Scholar
  40. 40.
    Chen, S., Hua, W., Li, Z., Li, J., & Gao, X. (2017). Heartbeat classification using projected and dynamic features of ECG signal. Biomedical Signal Processing and Control, 31, 165–173.CrossRefGoogle Scholar
  41. 41.
    Fatin, A., Salim, N., Harris, A. R., Swee, T. T., & Ahmed, T. (2016). Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals. Computer Methods and Programs in Biomedicine, 127, 52–63.CrossRefGoogle Scholar
  42. 42.
    Shadmand, S., & Mashoufi, B. (2016). A new personalized ECG signal classification algorithm using block-based neural network and particle swarm optimization. Biomedical Signal Processing and Control, 25, 12–23.CrossRefGoogle Scholar
  43. 43.
    Tan, W. W., Foo, C. L., & Chua, T. W. (2007). Type-2 fuzzy system for ECG arrhythmic classification. In FUZZ-IEEE 2007. IEEE International on Fuzzy Systems Conference (pp. 1–6). IEEE.Google Scholar
  44. 44.
    Dumont, J., Hernández, A. I., Fleureau, J., & Carrault, G. (2008). Modelling temporal evolution of cardiac electrophysiological features using hidden semi-markov models. In 30th Annual International Conference of the IEEE on Engineering in Medicine and Biology Society, 2008. EMBS 2008 (pp. 165–168). IEEE.Google Scholar
  45. 45.
    Yang, H., Negishi, K., Otahal, P., & Marwick, T. H. (2015). Clinical prediction of incident heart failure risk: A systematic review and meta-analysis. Open Heart, 2(1), e000222.CrossRefGoogle Scholar
  46. 46.
    Luz, E. J. D. S., Schwartz, W. R., Cámara-Chávez, G., & Menotti, D. (2016). ECG-based heartbeat classification for arrhythmia detection: A survey. Computer Methods and Programs in Biomedicine, 127, 144–164.CrossRefGoogle Scholar
  47. 47.
    Tanaka, H., Monahan, K. D., & Seals, D. R. (2001). Age-predicted maximal heart rate revisited. Journal of the American College of Cardiology, 37(1), 153–156.CrossRefGoogle Scholar
  48. 48.
    Wilmore, J. H., & Costill, D. L. (2004). Fisiología del esfuerzo y del deporte. Editorial Paidotribo.Google Scholar
  49. 49.
    Álvarez Cosmea, A. (2001). Las tablas de riesgo cardiovascular. una revisión crítica. Medifam, 11, 122–139.CrossRefGoogle Scholar
  50. 50.
    Alcocer, L. A., Lozada, O., Fanghänel, G., Sánchez-Reyes, L., & Campos-Franco, E. (2011). Estratificación del riesgo cardiovascular global. comparación de los métodos framingham y score en población mexicana del estudio prit. Cir Cir, 79(1), 168–174.Google Scholar
  51. 51.
    Association of American Medical Colleges. (2015). Careers in medicine.Google Scholar
  52. 52.
    Peters, B., & OBI Consortium (2009). Ontology for biomedical investigations.Google Scholar
  53. 53.
    Gene Ontology Consortium. (2006). The gene ontology (go) project in 2006. Nucleic Acids Research, 34(suppl 1), D322–D326.Google Scholar
  54. 54.
    Rector, A., & Rogers, J. (2006). Ontological and practical issues in using a description logic to represent medical concept systems: Experience from galen. Reasoning Web (pp. 197–231).CrossRefGoogle Scholar
  55. 55.
    Fujita, H., Hakura, J., & Kurematsu, M. (2010). Multiviews ontologies alignment for medical based reasoning ontology based reasoning for VDS. In 2010 11th International Symposium on Computational Intelligence and Informatics (CINTI) (pp. 15–22). IEEE.Google Scholar
  56. 56.
    Cornet, R., & de Keizer, N. (2008). Forty years of snomed: A literature review. BMC Medical Informatics and Decision Making, 8(1), S2.CrossRefGoogle Scholar
  57. 57.
    Resnik, P. (2011). Semantic similarity in a taxonomy: An information-based measure and its application to problems of ambiguity in natural language. arXiv preprint arXiv: 1105.5444.
  58. 58.
    Rada, R., Mili, H., Bicknell, E., & Blettner, M. (1989). Development and application of a metric on semantic nets. IEEE Transactions on Systems, Man and Cybernetics, 19(1), 17–30.CrossRefGoogle Scholar
  59. 59.
    Jiang, J. J., & Conrath, D. W. (1997). Semantic similarity based on corpus statistics and lexical taxonomy. In Proceedings of the international conference on research in computational linguistics (pp. 19–33).Google Scholar
  60. 60.
    Wu, Z., & Palmer, M. (1994). Verbs semantics and lexical selection. In Proceedings of the 32nd annual meeting on Association for Computational Linguistics (pp. 133–138). Association for Computational Linguistics.Google Scholar
  61. 61.
    Sinnott, R. W. (1984). Virtues of the haversine. Sky and Telescope, 68, 158.Google Scholar

Copyright information

© Taiwanese Society of Biomedical Engineering 2018

Authors and Affiliations

  • Giovanni Guzmán
    • 1
  • Miguel Torres-Ruiz
    • 1
  • Vianney Tambonero
    • 1
  • Miltiadis D. Lytras
    • 2
    • 3
  • Blanca López-Ramírez
    • 4
  • Rolando Quintero
    • 1
  • Marco Moreno-Ibarra
    • 1
  • Wadee Alhalabi
    • 5
  1. 1.Instituto Politécnico Nacional, CICUPALM ZacatencoMexico CityMexico
  2. 2.The American College of GreeceAthensGreece
  3. 3.King Abdulaziz UniversityJeddahSaudi Arabia
  4. 4.Instituto Tecnolégico de Roque - Tecnológico Nacional de MéxicoCelayaMexico
  5. 5.Virtual Reality Research CenterEffat UniversityJeddahSaudi Arabia

Personalised recommendations