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.
Similar content being viewed by others
Notes
Syndrome differentiation in Traditional Chinese Medicine (TCM) [13] is the comprehensive analysis of clinical information gained by the four main diagnostic TCM procedures: observation, listening, questioning, and pulse analysis, and it is used to guide the choice of treatment either by acupuncture and/or TCM herbal formulae.
Reflexes to control blood pressure.
References
Aldrees A, Chikh A (2016) Comparative evaluation of four multi-label classification algorithms in classifying learning objects. Comput Appl Eng Educ 24(4):651–660
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 523–531
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 279–286
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 348:2407–2415
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 1–6
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
Furnkranz J, Hullermeier E, Mencia EL, Brinker K (2008) Multilabel classification via calibrated label ranking. Mach Learn 73(2):133–153
Gibaja E, Ventura S (2015) A tutorial on multi-label learning. ACM Comput Surv 9(4):39
Gobin N, Wuerzener G, Waeber B, Burnier M (2012) Mesure ambulatoire de la pression artérielle sur 24 heures. Forum Med Suisse 12(3132):600–607
Gosse P, Lasserre R, Minifie C, Lemetayer P, Clementy J (2004) Blood pressure surge on rising. J Hypertens 22(6):1113–1118
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):1
Herrera F, Charte F, Rivera AJ, Del Jesus MJ (2016) Multilabel classification: problem analysis, metrics and techniques. Springer, Cham
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):634–642
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):885–889
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):1401–1406
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
Madin K, Iqbal P (2006) Twenty four hour ambulatory blood pressure monitoring: a new tool for determining cardiovascular prognosis. Postgrad Med J 82(971):548–551
Madjarov G, Kocev D, Gjorgjevikj D, Dzeroski S (2012) An extensive experimental comparison of methods for multi-label learning. Pattern Recognit 45(9):3084–3104
Modi H, Panchal M (2012) Experimental comparison of different problem transformation methods for multi-label classification using meka. Int J Comput Appl 59:15
Motamed S, Pechére-Bertschi A (2013) Hypertension artérielle. Department of Primary Care, HUG Arterial Hypertension Unit, SMPR, HUG, Geneva
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. Marrakech
OBrien E, Coats A, Owens P (2000) Use and interpretation of ambulatory blood pressure monitoring: recommendations of the british hypertension society. BMJ 320:1128–34
OBrien E, Waeber B, Parati G (2001) European society of hypertension recommendations on blood pressure measuring devices. BMJ 322:532–6
OBrien E, Asmar R, Beilin L (2003) European society of hypertension recommendations for conventional, ambulatory and home blood pressure measurement. J Hypertens 21:821–48
Papadopoulos D, Makris T (2007) Masked hypertension definition, impact, outcomes: a critical review. J Clin Hypertens 9:956–963
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 5:1876–1880
Pierdomenico S, Cuccurullo F (2010) Ambulatory blood pressure monitoring in type 2 diabetes and metabolic syndrome: a review. Blood Press Monit 15(1):1–7
Read J (2010) Scalable multi-label classification. PhD thesis, University of Waikato
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 995–1000
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 254–269
Read J, Reutemann P, Pfahringer B, Holmes G (2016) Meka: a multi-label/multi-target extension to weka. J Mach Learn Res 17(21):1–5
Trohidis K, Tsoumakas G, Kalliris G, Vlahavas I (2011) Multi-label classification of music by emotion. EURASIP J Audio Speech Music Process 1:1
Tsoumakas G, Katakis I (2007) Multi label classification: an overview. Int J Data Wareh Min 3(3):1–13
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 667–685
Tsoumakas G, Katakis I, Vlahavas I (2011) Random k-labelsets for multi-label classification. IEEE Trans Knowl Data Eng 23(7):1079–1089
Vilela-Martin J, de Melo RV, Kuniyoshi C, Abdo A, Yugar-Toledo J (2011) Hypertensive crisis: clinical-epidemiological profile. Hypertens Res 34(3):367–371
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):1983–1992
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):1085–1091
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
Zhang M, Zhou Z (2006) Multi-label neural networks with applications to functional genomics and text categorization. IEEE Trans Knowl Data Eng 18(10):1338–1351
Zhang M, Zhou Z (2007) Ml-knn: a lazy learning approach to multi-label learning. Pattern Recognit 40(7):2038–2048
Zhang M, Zhou Z (2014) A review on multi-label learning algorithms. IEEE Trans Knowl Data Eng 26(8):1819–1837
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
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.
Rights and permissions
About this article
Cite this article
Douibi, K., Settouti, N., Chikh, M.A. et al. An analysis of ambulatory blood pressure monitoring using multi-label classification. Australas Phys Eng Sci Med 42, 65–81 (2019). https://doi.org/10.1007/s13246-018-0713-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13246-018-0713-0