Skip to main content

Advertisement

SpringerLink
Log in

Differential diagnosis of asthma and restrictive respiratory diseases by combining forced oscillation measurements, machine learning and neuro-fuzzy classifiers

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

To design machine learning classifiers to facilitate the clinical use and increase the accuracy of the forced oscillation technique (FOT) in the differential diagnosis of patients with asthma and restrictive respiratory diseases. FOT and spirometric exams were performed in 97 individuals, including controls (n = 20), asthmatic patients (n = 38), and restrictive (n = 39) patients. The first experiment of this study showed that the best FOT parameter was the resonance frequency, providing moderate accuracy (AUC = 0.87). In the second experiment, a neuro-fuzzy classifier and different supervised machine learning techniques were investigated, including k-nearest neighbors, random forests, AdaBoost with decision trees, and support vector machines with a radial basis kernel. All classifiers achieved high accuracy (AUC ≥ 0.9) in the differentiation between patient groups. In the third and fourth experiments, the use of different feature selection techniques allowed us to achieve high accuracy with only three FOT parameters. In addition, the neuro-fuzzy classifier also provided rules to explain the classification. Neuro-fuzzy and machine learning classifiers can aid in the differential diagnosis of patients with asthma and restrictive respiratory diseases. They can assist clinicians as a support system providing accurate diagnostic options.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Abbreviations

ADAB:

AdaBoost classifier with decision trees. It is a ML algorithm that employs an ensemble strategy called boosting. Each base estimator (decision tree) is designed to correctly classify the instances misclassified by previous base estimators. The final output is a weighted combination of all base estimators (decision trees)

A ij :

The fuzzy set of the jth feature in the ith rule

AUC:

Area under the receiver operating characteristic curve

BFP :

Best FOT parameter, the most accurate parameter obtained using only the FOT method

c :

Number of classes

C :

Regularization parameter in SVM

Cdyn:

Dynamic compliance, reflecting the total compliance of the respiratory system, also being related to the respiratory homogeneity, expressed as mL/cmH2O

C dyn A :

Fuzzy set (Gaussian membership function) related to dynamic compliance Cdyn for Asthma class

C dyn R :

Fuzzy Set (Gaussian membership function) related to dynamic compliance Cdyn for Restrictive class

CG:

Control group, a group of healthy subjects used as a reference for comparisons with the studied diseased groups

C k :

the kth label of class

COPD:

Chronic obstructive pulmonary disease, a lung disease characterized by chronic obstruction of lung airflow that interferes with normal breathing

FOT:

Forced oscillation technique, a method to evaluate respiratory mechanics using sinusoidal system identification techniques

frA :

Fuzzy set (Gaussian membership function) related to resonant frequency fr for Asthma class

FEV1 :

The forced expiratory volume in the first second, obtained from a maximal expiratory effort maneuver, expressed in L

FVC:

Forced vital capacity, the total amount of air exhaled during the espirometric exams, expressed in L

fr:

Resonant frequency, the frequency at which Xrs becomes zero, associated with respiratory inhomogeneity and expressed as Hz

frR :

Fuzzy set (Gaussian membership function) related to resonant frequency fr for Restrictive class

K :

Number of nearest neighbor

k :

Number of folds in k-fold validation procedure

KNN:

K-nearest neighbor. It is a ML algorithm. When it is used for classification, the class of the query is determined by the majority vote among the class K-nearest neighbors found in the training set

KS:

Kyphoscoliosis, an abnormal curvature of the spine in both a coronal and sagittal plane. It is a combination of kyphosis and scoliosis

m :

Number of rules

ML :

Machine learning, field of artificial intelligence whose scope is the investigation of algorithms that can recognize patterns and learn different relationships present in a set of data

n :

Number of features (variables)

NFC:

Neuro-fuzzy classifier. It is a fuzzy rule-based system, which is encoded as a neural network. Hence, it is possible to apply neural network learning algorithms to determine the parameters of the fuzzy systems, such as the fuzzy rules and fuzzy membership functions

r :

Standard deviation of the radial basis function

R0:

Intercept resistance, obtained using linear regression in the 4–16 Hz range, a representative of the resistance in the low-frequency spectra, expressed as cmH2O/L/s

R0A :

Fuzzy set (Gaussian membership function) related to intercept resistance R0 for Asthma class

R0R :

Fuzzy set (Gaussian membership function) related to intercept resistance R0 for Restrictive class

R4:

Respiratory resistance in 4 Hz, expressed as cmH2O/L/s

RF:

Random forests. It is a ML algorithm that employs an ensemble of decision trees. In classification problems, the final output class is obtained by the majority of the class’s output by the individual decision trees

Rm:

Mean resistance in the 4–16 Hz range, reflecting mid-frequency spectra, that is related to the resistance in the central airways, expressed as cmH2O/L/s

ROC:

Receiver operating characteristic curve

Rrs:

Respiratory resistance, including airways, lung, and thoracic wall resistance, expressed as cmH2O/L/s

S :

Angular coefficient of resistance, the resistance change with frequency in the 4–16 Hz range, which is associated with respiratory nonhomogeneities, expressed as cmH2O/L/s2

Se:

Sensitivity, proportion of actual positives that are correctly identified as such

Sm :

Width matrix of the Gaussian membership functions

Sp:

Specificity, proportion of actual negatives that are correctly identified as such

SSCG:

Speeding up Scaled Conjugate Gradient

SVM:

Support vector machines. It is a ML algorithm that uses support vectors to determine a decision boundary that is a hyperplane with optimal geometric margin from the classes, which, in turn, presents the highest generalization capacity

SVMR:

support vector machines with radial basis function kernel. It is a SVM that employs the “kernel trick” to allow SVM to be employed in nonlinear separable problems. The “kernel trick” transforms the data in a new high-dimensional space where it is easier to separate the classes

U :

Center matrix of the Gaussian membership functions

W :

The weight matrix among the rules and the classes

X4:

Respiratory reactance at 4 Hz, expressed as cmH2O/L/s

Xm:

Mean reactance evaluated considering the 4 to 32 Hz frequency range, associated with respiratory inhomogeneity and expressed as cmH2O/L/s

Xrs:

Respiratory reactance, including airways, lung, and thoracic wall, expressed as cmH2O/L/s

x j :

jth input variable in the fuzzy classifier

Z4:

Impedance module in 4 Hz, The total mechanical load including resistance and elastic effects, expressed as cmH2O/L/s

References

  1. Abe S (2009) Support vector machines for pattern classification, advances in computer vision and pattern recognition, 2nd edn. Springer, New York

    Google Scholar 

  2. Abraham A (2005) Adaptation of fuzzy inference system using neural learning. In: Nedjah N, Macedo Mourelle Ld (eds) Fuzzy systems engineering, vol 181. Springer Berlin Heidelberg Berlin, pp. 53–83

  3. Abu-Mostafa YS, Magdon-Ismail M, Lin H-T (2012) Learning from data: a short course. AMLbook.com, S.l.

  4. Amaral JL, Faria AC, Lopes AJ, Jansen JM, Melo PL (2010) Automatic identification of chronic obstructive pulmonary disease based on forced oscillation measurements and artificial neural networks. Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society Conference 2010:1394–1397. https://doi.org/10.1109/IEMBS.2010.5626727

    Article  Google Scholar 

  5. Amaral JL, Lopes AJ, Faria AC, Melo PL (2015) Machine learning algorithms and forced oscillation measurements to categorise the airway obstruction severity in chronic obstructive pulmonary disease. Comput Methods Prog Biomed 118:186–197. https://doi.org/10.1016/j.cmpb.2014.11.002

    Article  Google Scholar 

  6. Amaral JL, Lopes AJ, Jansen JM, Faria AC, Melo PL (2012) Machine learning algorithms and forced oscillation measurements applied to the automatic identification of chronic obstructive pulmonary disease. Comput Methods Prog Biomed 105:183–193. https://doi.org/10.1016/j.cmpb.2011.09.009

    Article  Google Scholar 

  7. Amaral JL, Lopes AJ, Jansen JM, Faria AC, Melo PL (2013) An improved method of early diagnosis of smoking-induced respiratory changes using machine learning algorithms. Comput Methods Prog Biomed 112:441–454. https://doi.org/10.1016/j.cmpb.2013.08.004

    Article  Google Scholar 

  8. Amaral JL, Lopes AJ, Veiga J, Faria AC, Melo PL (2017) High-accuracy detection of airway obstruction in asthma using machine learning algorithms and forced oscillation measurements Computer methods and programs in biomedicine. 144:113–125. https://doi.org/10.1016/j.cmpb.2017.03.023

  9. Amaral JLM, Melo PL (2020) Clinical decision support systems to improve the diagnosis and management of respiratory diseases. In: Barh D (ed) Artificial intelligence in precision health. Elsevier, USA

    Google Scholar 

  10. Azar AT, Hassanien AE (2015) Dimensionality reduction of medical big data using neural-fuzzy classifier. Soft Comput 19:1115–1127. https://doi.org/10.1007/s00500-014-1327-4

    Article  Google Scholar 

  11. Bates JHT, Irvin CG, Farré R, Hantos Z (2011) Oscillation mechanics of the respiratory system. In: Terjung R (ed) Comprehensive physiology. John Wiley & Sons, Inc., Hoboken

    Google Scholar 

  12. Bit A, Chattyopadhay H, Nag D (2009) Study of airflow in the trachea of a bronchopulmonary patient using CT data. Indian Journal of Biomechanics:31–36

  13. Bousquet J, Tanasescu CC, Camuzat T, Anto JM, Blasi F, Neou A, Palkonen S, Papadopoulos NG, Antunes JP, Samolinski B, Yiallouros P, Zuberbier T (2013) Impact of early diagnosis and control of chronic respiratory diseases on active and healthy ageing. A debate at the European Union Parliament. Allergy 68:555–561. doi:https://doi.org/10.1111/all.12115

  14. Breiman L (2001) Random forests. Mach Learn 45:5–32

    Article  Google Scholar 

  15. Brochard L, Pelle G, de Palmas J, Brochard P, Carre A, Lorino H, Harf A (1987) Density and frequency dependence of resistance in early airway obstruction. Am Rev Respir Dis 135:579–584. https://doi.org/10.1164/arrd.1987.135.3.579

    Article  CAS  PubMed  Google Scholar 

  16. Brusasco V, Barisione G, Crimi E (2015) Pulmonary physiology: future directions for lung function testing in COPD. Respirology 20:209–218. https://doi.org/10.1111/resp.12388

    Article  PubMed  Google Scholar 

  17. Busse WW, Erzurum SC, Blaisdell CJ, Noel P (2014) Executive summary: NHLBI workshop on the primary prevention of chronic lung diseases. Annals of the American Thoracic Society 11(Suppl 3):S123–S124. https://doi.org/10.1513/AnnalsATS.201312-421LD

    Article  PubMed  PubMed Central  Google Scholar 

  18. Cavalcanti JV, Lopes AJ, Jansen JM, Melo PL (2006) Detection of changes in respiratory mechanics due to increasing degrees of airway obstruction in asthma by the forced oscillation technique. Respir Med 100:2207–2219. https://doi.org/10.1016/j.rmed.2006.03.009

    Article  PubMed  Google Scholar 

  19. Cawley GC, Talbot NLC (2010) On over-fitting in model selection and subsequent selection bias in performance evaluation. J Mach Learn Res 11:2079–2107

    Google Scholar 

  20. Cetişli B, Barkana A (2010) Speeding up the scaled conjugate gradient algorithm and its application in neuro-fuzzy classifier training. Soft Comput 14:365–378. https://doi.org/10.1007/s00500-009-0410-8

    Article  Google Scholar 

  21. Cordón O (2011) A historical review of evolutionary learning methods for Mamdani-type fuzzy rule-based systems: designing interpretable genetic fuzzy systems. Int J Approx Reason 52:894–913. https://doi.org/10.1016/j.ijar.2011.03.004

    Article  Google Scholar 

  22. Croxton TL, Weinmann GG, Senior RM, Hoidal JR (2002) Future research directions in chronic obstructive pulmonary disease. Am J Respir Crit Care Med 165:838–844. https://doi.org/10.1164/ajrccm.165.6.2108036

    Article  PubMed  Google Scholar 

  23. Das N, Topalovic M, Janssens W (2018) Artificial intelligence in diagnosis of obstructive lung disease: current status and future potential. Curr Opin Pulm Med 24:117–123. https://doi.org/10.1097/MCP.0000000000000459

    Article  PubMed  Google Scholar 

  24. de Melo PL, Werneck MM, Giannella-Neto A (2000) New impedance spectrometer for scientific and clinical studies of the respiratory system. Rev Sci Instrum 71:2867–2872

    Article  Google Scholar 

  25. de Sá PM, Lopes AJ, Jansen JM, de Melo PL (2013) Oscillation mechanics of the respiratory system in never-smoking patients with silicosis: pathophysiological study and evaluation of diagnostic accuracy. In: Clinics (Sao Paulo), 68. 5. pp 644-651. doi:https://doi.org/10.6061/clinics/2013(05)11

    Chapter  Google Scholar 

  26. Dellaca RL, Duffy N, Pompilio PP, Aliverti A, Koulouris NG, Pedotti A, Calverley PM (2007) Expiratory flow limitation detected by forced oscillation and negative expiratory pressure. Eur Respir J 29:363–374. https://doi.org/10.1183/09031936.00038006

    Article  CAS  PubMed  Google Scholar 

  27. DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics:837–845

  28. Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30

    Google Scholar 

  29. Dietterich TG (1998) Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput 10:1895–1923

    Article  CAS  Google Scholar 

  30. Drummond MB, Buist AS, Crapo JD, Wise RA, Rennard SI (2014) Chronic obstructive pulmonary disease: NHLBI workshop on the primary prevention of chronic lung diseases. Annals of the American Thoracic Society 11(Suppl 3):S154–S160. https://doi.org/10.1513/AnnalsATS.201312-432LD

    Article  PubMed  PubMed Central  Google Scholar 

  31. Dubois AB, Brody AW, Lewis DH, Burgess BF Jr (1956) Oscillation mechanics of lungs and chest in man. J Appl Physiol 8:587–594

    Article  CAS  Google Scholar 

  32. Eswari JS, Majdoubi J, Naik S, Gupta S, Bit A, Rahimi-Gorji M, Saleem A (2020) Prediction of stenosis behaviour in artery by neural network and multiple linear regressions. Biomech Model Mechanobiol. https://doi.org/10.1007/s10237-020-01300-z

  33. Faria AC, Barbosa WR, Lopes AJ, Pinheiro Gda R, Melo PL (2012) Contrasting diagnosis performance of forced oscillation and spirometry in patients with rheumatoid arthritis and respiratory symptoms. Clinics 67:987–994

    Article  Google Scholar 

  34. Faria AC, Lopes AJ, Jansen JM, Melo PL (2009) Assessment of respiratory mechanics in patients with sarcoidosis using forced oscillation: correlations with spirometric and volumetric measurements and diagnostic accuracy. Respiration; international review of thoracic diseases 78:93–104. https://doi.org/10.1159/000213756

    Article  PubMed  Google Scholar 

  35. Faria AC, Lopes AJ, Jansen JM, Melo PL (2009) Evaluating the forced oscillation technique in the detection of early smoking-induced respiratory changes. Biomed Eng Online 8:22. https://doi.org/10.1186/1475-925X-8-22

    Article  PubMed  PubMed Central  Google Scholar 

  36. Faria ACD, Lopes AJ, Jansen JM, PLd M (2009) Assessment of respiratory mechanics in patients with sarcoidosis using forced oscillations. Respiration 78:93–104

    Article  Google Scholar 

  37. Ferguson GT, Enright PL, Buist AS, MW H (2000) Office spirometry for lung health assessment in adults: a consensus statement from the National Lung Health Education Program. Chest 117:1146–1161

    Article  CAS  Google Scholar 

  38. Freund Y, Schapire R, Abe N (1999) A short introduction to boosting. Journal-Japanese Society For Artificial Intelligence 14:1612

    Google Scholar 

  39. Gacto MJ, Alcalá R, Herrera F (2011) Interpretability of linguistic fuzzy rule-based systems: an overview of interpretability measures. Inf Sci 181:4340–4360. https://doi.org/10.1016/j.ins.2011.02.021

    Article  Google Scholar 

  40. GOLD (2013) Global Initiative For Chronic Obstructive Lung Disease – UPDATE (2013). In: Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease. NHLBI/WHO

  41. Golpe R, Jimenez A, Carpizo R, Cifrian JM (1999) Utility of home oximetry as a screening test for patients with moderate to severe symptoms of obstructive sleep apnea. Sleep 22:932–937

    CAS  PubMed  Google Scholar 

  42. Guyon I, Lisseff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182

    Google Scholar 

  43. Hajian-Tilaki K (2013) Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation. Caspian J Intern Med 4:627–635

    PubMed  PubMed Central  Google Scholar 

  44. Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning, 2nd edn. Springer-Verlag

  45. Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning. Springer Series in Statistics, New York

    Book  Google Scholar 

  46. Haykin SS (2009) Neural networks and learning machines. 3rd ed edn. Prentice Hall, New York

  47. Hüllermeier E (2005) Fuzzy methods in machine learning and data mining: status and prospects. Fuzzy Sets Syst 156:387–406. https://doi.org/10.1016/j.fss.2005.05.036

    Article  Google Scholar 

  48. Ionescu CM, Machado JT, De Keyser R (2011) Is multidimensional scaling suitable for mapping the input respiratory impedance in subjects and patients. Comput Methods Prog Biomed 2011:189–200

    Article  Google Scholar 

  49. Jain AK (2010) Data clustering: 50 years beyond K-means. Pattern Recogn Lett 31:651–666. https://doi.org/10.1016/j.patrec.2009.09.011

    Article  Google Scholar 

  50. Jang J-SR, others Fuzzy modeling using generalized neural networks and Kalman filter algorithm. In, 1991 1991. pp 762–767

  51. Jang J-SR, Sun C-T, Mizutani E (1997) Neuro-fuzzy and soft computing; a computational approach to learning and machine intelligence

  52. Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics 23:665–685. https://doi.org/10.1109/21.256541

    Article  Google Scholar 

  53. Japkowicz N, Shah M (2011) Evaluating learning algorithms: a classification perspective. Cambridge University Press, Cambridge, New York

    Book  Google Scholar 

  54. Jornal Brasileiro de Pneumologia - Diretrizes para Testes de Função Pulmonar. (2002). http://www.jornaldepneumologia.com.br/detalhe_suplemento.asp?id=45

  55. King GG, Bates J, Berger KI, Calverley P, de Melo PL, Dellaca RL, Farre R, Hall GL, Ioan I, Irvin CG, Kaczka DW, Kaminsky DA, Kurosawa H, Lombardi E, Maksym GN, Marchal F, Oppenheimer BW, Simpson SJ, Thamrin C, van den Berge M, Oostveen E (2019) Technical standards for respiratory oscillometry. Eur Respir J 55:1900753. https://doi.org/10.1183/13993003.00753-2019

    Article  Google Scholar 

  56. King GG, Bates J, Berger KI, Calverley P, de Melo PL, Dellaca RL, Farre R, Hall GL, Ioan I, Irvin CG, Kaczka DW, Kaminsky DA, Kurosawa H, Lombardi E, Maksym GN, Marchal F, Oppenheimer BW, Simpson SJ, Thamrin C, van den Berge M, Oostveen E (2020) Technical standards for respiratory oscillometry. Eur Respir J 55:1900753. https://doi.org/10.1183/13993003.00753-2019

    Article  PubMed  Google Scholar 

  57. Kuncheva LI (2004) Combining pattern classifiers: methods and algorithms. John Wiley & Sons

  58. Lappas AS, Tzortzi AS, Behrakis BK (2014) Forced oscillations in applied respiratory physiology: clinical applications. Clin Res Pulmonol 2:1016–1033

    Google Scholar 

  59. Lima AN, Faria AC, Lopes AJ, Jansen JM, Melo PL (2015) Forced oscillations and respiratory system modeling in adults with cystic fibrosis. Biomed Eng Online 14:11. https://doi.org/10.1186/s12938-015-0007-7

    Article  PubMed  PubMed Central  Google Scholar 

  60. Lorino AM, Zerah F, Mariette C, Harf A, Lorino H (1997) Respiratory resistive impedance in obstructive patients: linear regression analysis vs viscoelastic modelling. Eur Respir J 10:150–155

    Article  CAS  Google Scholar 

  61. Lungu A, Swift AJ, Capener D, Kiely D, Hose R, Wild JM (2016) Diagnosis of pulmonary hypertension from magnetic resonance imaging-based computational models and decision tree analysis. Pulmonary circulation 6:181–190. https://doi.org/10.1086/686020

    Article  PubMed  PubMed Central  Google Scholar 

  62. Ma Y, Guo G (2014) Support vector machines applications. Springer

  63. MacIntyre NR (2012) The future of pulmonary function testing. Respir Care 57:154–161; discussion 161-154. doi:https://doi.org/10.4187/respcare.01422

  64. MacLeod D, Birch M (2001) Respiratory input impedance measurement: forced oscillation methods. Medical & biological engineering & computing 39:505–516

    Article  CAS  Google Scholar 

  65. Madero Orozco H, Vergara Villegas OO, Cruz Sánchez VG, Ochoa Domínguez HdJ, Nandayapa Alfaro MdJ (2015) Automated system for lung nodules classification based on wavelet feature descriptor and support vector machine. BioMedical Engineering OnLine 14. doi:https://doi.org/10.1186/s12938-015-0003-y

  66. Majid A, Ali S, Iqbal M, Kausar N (2014) Prediction of human breast and colon cancers from imbalanced data using nearest neighbor and support vector machines. Comput Methods Prog Biomed 113:792–808. https://doi.org/10.1016/j.cmpb.2014.01.001

    Article  Google Scholar 

  67. Manoharan SC, Veezhinathan M, Ramakrishnan S (2008) Comparison of two ANN methods for classification of spirometer data. MEASUREMENT SCIENCE REVIEW 8:53–57

    Article  Google Scholar 

  68. Marinho CL, Maioli MCP, Amaral JLM, LA J, PL M (2018) Respiratory resistance and reactance in adults with sickle cell anemia: part 2 - fractional-order modeling and a clinical decision support system for the diagnostic of respiratory disorders. PLoS One 14:e0213257. https://doi.org/10.1371/journal.pone.0213257

    Article  CAS  Google Scholar 

  69. Marinho CL, MCP M, do JLM A, AJ L, PL M (2017) Respiratory resistance and reactance in adults with sickle cell anemia: correlation with functional exercise capacity and diagnostic use. PLoS One 12:e0187833. https://doi.org/10.1371/journal.pone.0187833

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Miller MR, Hankinson J, Brusasco V, Burgos F, Casaburi R, Coates A, Crapo R, Enright P, CPMvd G, Gustafsson P, Jensen R, DC J, MacIntyre N, McKay R, Navajas D, Pedersen OF, Pellegrino R, Viegi G, Wanger J (2005) Standardisation of spirometry. https://doi.org/10.1183/09031936.05.00034805

  71. Miranda IA, Dias Faria AC, Lopes AJ, Jansen JM, Lopes de Melo P (2013) On the respiratory mechanics measured by forced oscillation technique in patients with systemic sclerosis. PLoS One 8:e61657. https://doi.org/10.1371/journal.pone.0061657

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Mohri M, Rostamizadeh A, Talwalkar A (2012) Foundations of machine learning. Adaptive computation and machine learning series. MIT Press, Cambridge, MA

    Google Scholar 

  73. Møller MF (1993) A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw 6:525–533. https://doi.org/10.1016/S0893-6080(05)80056-5

    Article  Google Scholar 

  74. Nagels J, Landser FJ, van der Linden L, Clement J, Van de Woestijne KP (1980) Mechanical properties of lungs and chest wall during spontaneous breathing. J Appl Physiol Respir Environ Exerc Physiol 49:408–416

    CAS  PubMed  Google Scholar 

  75. Nauck D, Kruse R, Klawonn F (1997) Foundations of neuro-fuzzy systems. John Wiley, Chichester ; New York

    Google Scholar 

  76. Nicolai M, Peter B (2010) Stability selection. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 72:417–473

    Article  Google Scholar 

  77. Nilsson AM, Theander E, Hesselstrand R, Piitulainen E, Wollmer P, Mandl T (2014) The forced oscillation technique is a sensitive method for detecting obstructive airway disease in patients with primary Sjogren’s syndrome. Scand J Rheumatol 43:324–328. https://doi.org/10.3109/03009742.2013.856466

    Article  CAS  PubMed  Google Scholar 

  78. Pedregosa F, Varoquaux G, Gramfort A, Bertrand Thirion VM, Grisel O, Blondel M, Müller A, Nothman J, Louppe G, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830

    Google Scholar 

  79. Pereira CAdC, Barreto SdP, Simöes JG, Pereira FWL, Gerstler JG, Nakatani J (1992) Reference values for spirometry in Brazilian adults. doi:lil-123525

  80. Peters U, Hernandez P, Dechman G, Ellsmere J, Maksym G (2016) Early detection of changes in lung mechanics with oscillometry following bariatric surgery in severe obesity. Applied physiology, nutrition, and metabolism = Physiologie appliquee, nutrition et metabolisme 41:538-547. doi:https://doi.org/10.1139/apnm-2015-0473

  81. Raskutti G, Wainwright MJ, Yu B (2014) Early stopping and non-parametric regression: an optimal data-dependent stopping rule. The Journal of Machine Learning Research 15:335–366

    Google Scholar 

  82. Reisch S, Schneider M, Timmer J, Geiger K, Guttmann J (1998) Evaluation of forced oscillation technique for early detection of airway obstruction in sleep apnea: a model study. Technology and health care : official journal of the European Society for Engineering and Medicine 6:245–257

    Article  CAS  Google Scholar 

  83. Reisch S, Steltner H, Timmer J, Renotte C, Guttmann J (1999) Early detection of upper airway obstructions by analysis of acoustical respiratory input impedance. Biol Cybern 81:25-37. doi:DOI https://doi.org/10.1007/s004220050542

  84. PMd S, AJ L, JM J, PLd M (2013) Oscillation mechanics of the respiratory system in never-smoking patients with silicosis: pathophysiological study and evaluation of diagnostic accuracy. Clinics (Sao Paulo) 68:644–651. https://doi.org/10.6061/clinics/2013(05)11

    Article  Google Scholar 

  85. Sahin D, Ubeyli ED, Ilbay G, Sahin M, Yasar AB (2010) Diagnosis of airway obstruction or restrictive spirometric patterns by multiclass support vector machines. J Med Syst 34:967–973. https://doi.org/10.1007/s10916-009-9312-7

    Article  PubMed  Google Scholar 

  86. Sancho AG, Faria ACD, Amaral JLM, Lopes AJ, Melo PL Evaluation of the forced oscillation technique in the differential diagnosis of obstructive and restrictive respiratory diseases. In: IFMBE Proceedings of the XXVI Brazilian Congress on Biomedical Engineering, Búzios, Rio de Janeiro, 2018. Springer, The International Federation for Medical and Biological Engineering (IFMBE) Proceedings book series., p 45 to 50. doi:https://doi.org/10.1007/978-981-13-2119-1_7

  87. Schapire RE (2013) Explaining adaboost. In: Empirical inference. Springer, pp. 37–52

  88. Scornet E, Biau G, Vert J-P (2015) Consistency of random forests. Ann Stat 43:1716–1741. https://doi.org/10.1214/15-AOS1321

    Article  Google Scholar 

  89. Sen I, Saraclar M, Kahya YP (2015) A comparison of SVM and GMM-based classifier configurations for diagnostic classification of pulmonary sounds. IEEE Trans Biomed Eng 62:1768–1776. https://doi.org/10.1109/TBME.2015.2403616

    Article  PubMed  Google Scholar 

  90. Sugiyama A, Hattori N, Haruta Y, Nakamura I, Nakagawa M, Miyamoto S, Onari Y, Iwamoto H, Ishikawa N, Fujitaka K, Murai H, Kohno N (2013) Characteristics of inspiratory and expiratory reactance in interstitial lung disease. Respiratory medicine 107:875-882. doi:DOI https://doi.org/10.1016/j.rmed.2013.03.005

  91. Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240:1285–1293

    Article  CAS  Google Scholar 

  92. Topalovic M, Das N, Burgel PR, Daenen M, Derom E, Haenebalcke C, Janssen R, Kerstjens HAM, Liistro G, Louis R, Ninane V, Pison C, Schlesser M, Vercauter P, Vogelmeier CF, Wouters E, Wynants J, Janssens W, Pulmonary Function Study I, Pulmonary Function Study I (2019) Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests. Eur Respir J 53:1801660. https://doi.org/10.1183/13993003.01660-2018

    Article  PubMed  Google Scholar 

  93. Vapnik VN (2000) The nature of statistical learning theory. Springer New York, New York, NY

    Book  Google Scholar 

  94. Witten IH, Frank E, Hall MA, Pal CJ (2016) Data mining: practical machine learning tools and techniques. Morgan Kaufmann

  95. World Health Organization WHO (2019) GINA – Global Initiative for Asthma

  96. Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353. https://doi.org/10.1016/S0019-9958(65)90241-X

    Article  Google Scholar 

Download references

Funding

This study was supported by the Brazilian Council for Scientific and Technological Development (CNPq) and the Rio de Janeiro State Research Supporting Foundation (FAPERJ) and in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.

Author information

Authors and Affiliations

  1. Department of Electronics and Telecommunications Engineering, State University of Rio de Janeiro, Rio de Janeiro, Brazil

    Jorge L. M. Amaral

  2. Biomedical Instrumentation Laboratory, Institute of Biology Roberto Alcantara Gomes and Laboratory of Clinical and Experimental Research in Vascular Biology, State University of Rio de Janeiro, Rio de Janeiro, Brazil

    Alexandre G. Sancho, Alvaro C. D. Faria & Pedro L. Melo

  3. Pulmonary Function Laboratory, Pedro Ernesto University Hospital, State University of Rio de Janeiro, Rio de Janeiro, Brazil

    Agnaldo J. Lopes

Authors
  1. Jorge L. M. Amaral
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alexandre G. Sancho
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alvaro C. D. Faria
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Agnaldo J. Lopes
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Pedro L. Melo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pedro L. Melo.

Ethics declarations

This research was approved by the research ethics board of the State University of Rio de Janeiro, and the post-informed consent of all volunteers was obtained before inclusion in the study. The study was conducted in accordance with the Declaration of Helsinki.

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

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

Electronic supplementary material

ESM 1

(DOC 56 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Amaral, J.L.M., Sancho, A.G., Faria, A.C.D. et al. Differential diagnosis of asthma and restrictive respiratory diseases by combining forced oscillation measurements, machine learning and neuro-fuzzy classifiers. Med Biol Eng Comput 58, 2455–2473 (2020). https://doi.org/10.1007/s11517-020-02240-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-020-02240-7

Keywords

Search

Navigation