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An analysis of ambulatory blood pressure monitoring using multi-label classification

  • Khalida Douibi
  • Nesma Settouti
  • Mohammed Amine Chikh
  • Jesse Read
  • Mohamed Malik Benabid
Scientific Paper

Abstract

Ambulatory blood pressure monitoring (ABPM) involves measuring blood pressure by means of a tensiometer carried by the patient for a duration of 24 h, it currently occupies a central place in the diagnosis and follow-up of hypertensive patients, it provides crucial information which allows to make a specific diagnosis and adapt therapeutic attitude accordingly. The traditional analysis process suffers from different problems: it requires a lot of time and expertise, and several calculations should be performed manually by the expert, who is generally very busy. In this work, we attempt to improve the analysis of ABPM data using multi-label classification methods, where a record is associated with more than one label (class) at the same time. Seven algorithms are experimentally compared on a new multi-label ABPM-dataset. Experiments are conducted on 270 hypertensive patient records characterized by 40 attributes and associated with six labels. Results show that the multi-label modeling of ABPM data helps to investigate label dependencies and provide interesting insights, which can be integrated into the ABPM devices to dispense automatically detailed reports with possible future complications.

Keywords

Ambulatory blood pressure monitoring (ABPM) Multi-label classification High blood pressure (HBP) Meka Medical dataset Medical diagnosis 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. 1.
    Aldrees A, Chikh A (2016) Comparative evaluation of four multi-label classification algorithms in classifying learning objects. Comput Appl Eng Educ 24(4):651–660CrossRefGoogle Scholar
  2. 2.
    Batista V, Pintado F, Gil AB, Rodriguez V, Moreno M (2011) A system for multi-label classification of learning objects. In: 6th international conference SOCO 2011 advances in intelligent and soft computing, vol 87, pp 523–531Google Scholar
  3. 3.
    Cheng W, Hllermeier E, Dembczynski KJ (2010) Bayes optimal multilabel classification via probabilistic classifier chains. In: Proceedings of the 27th international conference on machine learning (ICML-10) pp 279–286Google Scholar
  4. 4.
    Clement DL, Buyzere DL, Bacquer DA, Leeuw PW, Duprez DA, Fagard RH, Niepen PVD (2003) Pronostic value of ambulatory blood-pressure recordings in patients with hypertension. N Engl J Med 348:2407–2415CrossRefGoogle Scholar
  5. 5.
    Copetti A, Loques O, Leite J, Barbosa TP, da Nobrega AC (2009) Intelligent context-aware monitoring of hypertensive patients. In: 2009 3rd international conference on pervasive computing technologies for healthcare IEEE pp 1–6Google Scholar
  6. 6.
    Douibi K, Benabid M, Settouti N, Chikh MA (2017) Data for: an analysis of ambulatory blood pressure monitoring (abpm). Mendeley Data, v1.  https://doi.org/10.17632/y4dh3b3tfx.1
  7. 7.
    Furnkranz J, Hullermeier E, Mencia EL, Brinker K (2008) Multilabel classification via calibrated label ranking. Mach Learn 73(2):133–153CrossRefGoogle Scholar
  8. 8.
    Gibaja E, Ventura S (2015) A tutorial on multi-label learning. ACM Comput Surv 9(4):39Google Scholar
  9. 9.
    Gobin N, Wuerzener G, Waeber B, Burnier M (2012) Mesure ambulatoire de la pression artérielle sur 24 heures. Forum Med Suisse 12(3132):600–607CrossRefGoogle Scholar
  10. 10.
    Gosse P, Lasserre R, Minifie C, Lemetayer P, Clementy J (2004) Blood pressure surge on rising. J Hypertens 22(6):1113–1118CrossRefGoogle Scholar
  11. 11.
    Guo-Zheng L, Zehui H, Feng-Feng S (2015) Patient classification of hypertension in traditional chinese medicine using multi-label learning techniques. BMC Med Genom 8(3):1Google Scholar
  12. 12.
    Herrera F, Charte F, Rivera AJ, Del Jesus MJ (2016) Multilabel classification: problem analysis, metrics and techniques. Springer, ChamGoogle Scholar
  13. 13.
    Jiang M, Lu C, Zhang C, Yang J, Tan Y, Lu A, Chan K (2012) Syndrome differentiation in modern research of traditional chinese medicine. J Ethnopharmacol 140(3):634–642CrossRefGoogle Scholar
  14. 14.
    Kanoun F, Alaya NB, driss S, Sayem N, Chihaoui M, Harzallah F, Slimane H (2010) Appréciation du profil tensionnel par mesure ambulatoire de la pression artérielle chez les diabétiques hypertendus traités. La tunisie Médicale 88(12):885–889PubMedGoogle Scholar
  15. 15.
    Kario K, Pickering TG, Umeda Y, Hoshide S, Hoshide Y, Morinari M, Murata M, Kuroda T, Schwartz JE, Shimada K (2003) Morning surge in blood pressure as a predictor of silent and clinical cerebrovascular disease in elderly hypertensives. Circulation 107(10):1401–1406CrossRefGoogle Scholar
  16. 16.
    Lee J, Kim H, Kim NR, Lee JH (2016) An approach for multi-label classification by directed acyclic graph with label correlation maximization. Inf Sci 351:101–114.  https://doi.org/10.1016/j.ins.2016.02.037 CrossRefGoogle Scholar
  17. 17.
    Madin K, Iqbal P (2006) Twenty four hour ambulatory blood pressure monitoring: a new tool for determining cardiovascular prognosis. Postgrad Med J 82(971):548–551CrossRefGoogle Scholar
  18. 18.
    Madjarov G, Kocev D, Gjorgjevikj D, Dzeroski S (2012) An extensive experimental comparison of methods for multi-label learning. Pattern Recognit 45(9):3084–3104CrossRefGoogle Scholar
  19. 19.
    Modi H, Panchal M (2012) Experimental comparison of different problem transformation methods for multi-label classification using meka. Int J Comput Appl 59:15Google Scholar
  20. 20.
    Motamed S, Pechére-Bertschi A (2013) Hypertension artérielle. Department of Primary Care, HUG Arterial Hypertension Unit, SMPR, HUG, GenevaGoogle Scholar
  21. 21.
    Ngendakumana E, Hattaoui ME (2014) Evaluation du controle de lhypertension artérielle par la mapa chez les patients diabètiques hypertendus. PhD thesis, Cardiology Department: Ibn Tofail Hospital. CHU Mohammed VI. MarrakechGoogle Scholar
  22. 22.
    OBrien E, Coats A, Owens P (2000) Use and interpretation of ambulatory blood pressure monitoring: recommendations of the british hypertension society. BMJ 320:1128–34CrossRefGoogle Scholar
  23. 23.
    OBrien E, Waeber B, Parati G (2001) European society of hypertension recommendations on blood pressure measuring devices. BMJ 322:532–6Google Scholar
  24. 24.
    OBrien E, Asmar R, Beilin L (2003) European society of hypertension recommendations for conventional, ambulatory and home blood pressure measurement. J Hypertens 21:821–48CrossRefGoogle Scholar
  25. 25.
    Papadopoulos D, Makris T (2007) Masked hypertension definition, impact, outcomes: a critical review. J Clin Hypertens 9:956–963CrossRefGoogle Scholar
  26. 26.
    Pechre-Bertschi A, Michel Y, Brandstatter H, Muggli F, Gaspoz JM (2009) Lecture de la mesure ambulatoire de la pression artrielle (mapa) par le mdecin de premier recours. Rev Med Suisse 5:1876–1880Google Scholar
  27. 27.
    Pierdomenico S, Cuccurullo F (2010) Ambulatory blood pressure monitoring in type 2 diabetes and metabolic syndrome: a review. Blood Press Monit 15(1):1–7CrossRefGoogle Scholar
  28. 28.
    Read J (2010) Scalable multi-label classification. PhD thesis, University of WaikatoGoogle Scholar
  29. 29.
    Read J, Pfahringer B, Holmes G (2008) Multi-label classification using ensembles of pruned sets. In: Proceedings of the 8th IEEE international conference on data mining, pp 995–1000Google Scholar
  30. 30.
    Read J, Pfahringer B, Holmes G, Frank E (2009) Classifier chains for multi-label classification. In: Proceedings of the European conference on machine learning, pp 254–269CrossRefGoogle Scholar
  31. 31.
    Read J, Reutemann P, Pfahringer B, Holmes G (2016) Meka: a multi-label/multi-target extension to weka. J Mach Learn Res 17(21):1–5Google Scholar
  32. 32.
    Trohidis K, Tsoumakas G, Kalliris G, Vlahavas I (2011) Multi-label classification of music by emotion. EURASIP J Audio Speech Music Process 1:1Google Scholar
  33. 33.
    Tsoumakas G, Katakis I (2007) Multi label classification: an overview. Int J Data Wareh Min 3(3):1–13CrossRefGoogle Scholar
  34. 34.
    Tsoumakas G, Katakis I, Vlahavas I (2010) Mining multi-label data. In: Maimon O, Rokach L (eds) Data mining and knowledge discovery handbook. Springer, New York, pp 667–685Google Scholar
  35. 35.
    Tsoumakas G, Katakis I, Vlahavas I (2011) Random k-labelsets for multi-label classification. IEEE Trans Knowl Data Eng 23(7):1079–1089CrossRefGoogle Scholar
  36. 36.
    Vilela-Martin J, de Melo RV, Kuniyoshi C, Abdo A, Yugar-Toledo J (2011) Hypertensive crisis: clinical-epidemiological profile. Hypertens Res 34(3):367–371CrossRefGoogle Scholar
  37. 37.
    Whitworth J, Organization W (2003) International society of hypertension writing group: 2003 world health organization (who)/ international society of hypertension (ish) statement on management of hypertension. J Hypertens 21(11):1983–1992CrossRefGoogle Scholar
  38. 38.
    Zachariah PK, Sheps SG, Ilstrup DM, Long CR, Bailey KR, Wiltgen CM, Carlson CA (1988) Blood pressure load-a better determinant of hypertension. Mayo Clin Proc 63(11):1085–1091CrossRefGoogle Scholar
  39. 39.
    Zanchetti A (1997) The role of ambulatory blood pressure monitoring in clinical practice. Am J Hypertens 10(9):1069–1080.  https://doi.org/10.1016/S0895-7061(97)00270-7 CrossRefPubMedGoogle Scholar
  40. 40.
    Zhang M, Zhou Z (2006) Multi-label neural networks with applications to functional genomics and text categorization. IEEE Trans Knowl Data Eng 18(10):1338–1351CrossRefGoogle Scholar
  41. 41.
    Zhang M, Zhou Z (2007) Ml-knn: a lazy learning approach to multi-label learning. Pattern Recognit 40(7):2038–2048CrossRefGoogle Scholar
  42. 42.
    Zhang M, Zhou Z (2014) A review on multi-label learning algorithms. IEEE Trans Knowl Data Eng 26(8):1819–1837CrossRefGoogle Scholar

Copyright information

© Australasian College of Physical Scientists and Engineers in Medicine 2018

Authors and Affiliations

  1. 1.Biomedical Engineering Laboratory GBMTlemcen UniversityTlemcenAlgeria
  2. 2.LIX Laboratory, École PolytechniquePalaiseauFrance
  3. 3.University Hospital Center (CHU) Sétif, Cardiology departmentSétifAlgeria

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