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

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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%.

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References

  1. United Nations. (2010). Department of Economic. World population ageing 2009. New York: United Nations Publications.

  2. World Health Organization. (2009). Global status report on road safety: time for action. Geneva: World Health Organization.

  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.

    Article  Google Scholar 

  4. World Health Organization et al. (2011) The top 10 causes of death. Geneva: World Health Organization.

  5. Kendall, K. E., Kendall, J. E. (2007). Systems analysis and design, (Vol. 7). New Jersey: Prentice-Hall.

  6. Mihailidis, A., & Bardram, J. E. (2010). Pervasive computing in healthcare. Boca Raton: CRC Press.

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

  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.

    Article  Google Scholar 

  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.

  12. Tamura, T., Maeda, Y., Sekine, M., & Yoshida, M. (2014). Wearable photoplethysmographic sensors-past and present. Electronics, 3(2), 282–302.

    Article  Google Scholar 

  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.

  14. Temko, A. (2017). Accurate heart rate monitoring during physical exercises using PPG. IEEE Transactions on Biomedical Engineering, 64(9), 2016–2024.

    Article  Google Scholar 

  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.

  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.

  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.

  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. 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. 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.

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. 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).

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. Appventive LLC. (2014). In case of emergency (ICE).

  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.

    Article  Google Scholar 

  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.

    Article  MathSciNet  Google Scholar 

  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.

  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. 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.

  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.

  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.

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

  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.

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  48. Wilmore, J. H., & Costill, D. L. (2004). Fisiología del esfuerzo y del deporte. Editorial Paidotribo.

  49. Álvarez Cosmea, A. (2001). Las tablas de riesgo cardiovascular. una revisión crítica. Medifam, 11, 122–139.

    Article  Google Scholar 

  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. Association of American Medical Colleges. (2015). Careers in medicine.

  52. Peters, B., & OBI Consortium (2009). Ontology for biomedical investigations.

  53. Gene Ontology Consortium. (2006). The gene ontology (go) project in 2006. Nucleic Acids Research, 34(suppl 1), D322–D326.

  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).

    Chapter  Google Scholar 

  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.

  56. Cornet, R., & de Keizer, N. (2008). Forty years of snomed: A literature review. BMC Medical Informatics and Decision Making, 8(1), S2.

    Article  Google Scholar 

  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. 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.

    Article  Google Scholar 

  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).

  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.

  61. Sinnott, R. W. (1984). Virtues of the haversine. Sky and Telescope, 68, 158.

    Google Scholar 

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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.

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Authors and Affiliations

  1. Instituto Politécnico Nacional, CIC, UPALM Zacatenco, 07320, Mexico City, Mexico

    Giovanni Guzmán, Miguel Torres-Ruiz, Vianney Tambonero, Rolando Quintero & Marco Moreno-Ibarra

  2. The American College of Greece, 6 Gravias Street GR-153 42 Aghia Paraskevi, Athens, Greece

    Miltiadis D. Lytras

  3. King Abdulaziz University, 21589, Jeddah, 80200, Saudi Arabia

    Miltiadis D. Lytras

  4. Instituto Tecnolégico de Roque - Tecnológico Nacional de México, Km.8 Carretera Celaya-Juventino Rosas, 38110, Celaya, Mexico

    Blanca López-Ramírez

  5. Virtual Reality Research Center, Effat University, 21478, Jeddah, 34689, Saudi Arabia

    Wadee Alhalabi

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  1. Giovanni Guzmán
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  2. Miguel Torres-Ruiz
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Correspondence to Miguel Torres-Ruiz.

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Guzmán, G., Torres-Ruiz, M., Tambonero, V. et al. A Collaborative Framework for Sensing Abnormal Heart Rate Based on a Recommender System: Semantic Recommender System for Healthcare. J. Med. Biol. Eng. 38, 1026–1045 (2018). https://doi.org/10.1007/s40846-018-0421-y

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