Békési József

Galambos Gábor

Kelemen András

Papp Imre

Tolnai József

English

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A Neural Network Based Classification Algorithm forAsthma Using Capnography[Extended Abstract]József BékésiInstitute of InformaticsUniversity of SzegedÁrpád tér 2.H-6720 Szeged, Hungarybekesi@inf.u-szeged.huGábor GalambosJuhász Gyula Faculty ofEducation, Department ofApplied InformaticsUniversity of SzegedBoldogasszony sgt. 6.H-6725 Szeged, HungaryGalambosGabor@szte.huAndrás KelemenJuhász Gyula Faculty ofEducation, Department ofApplied InformaticsUniversity of SzegedBoldogasszony sgt. 6.H-6725 Szeged, Hungarykelemen.andras.felix@szte.huImre PappJuhász Gyula Faculty ofEducation, Department ofApplied InformaticsUniversity of SzegedBoldogasszony sgt. 6.H-6725 Szeged, Hungarypap.imre@szte.huJózsef TolnaiAlbert Szent-Györgyi MedicalSchool, Department ofMedical Physics andInformaticsUniversity of SzegedKorányi fasor 9.H-6720 Szeged, Hungarytolnai.jozsef@med.u-szeged.huABSTRACTThis article presents a neural network-based method to helpphysicians diagnose and monitor asthma and other chronicrespiratory diseases. The method is based on capnography,using measurement data from a specially developed hand-held device.After proper preparation, various parameters are calculatedon the capnographic curve from which healthcare profession-als can conclude the condition of the patient’s respiratorysystem.Another purpose of using the calculated parameters is toserve as a learning base for an artificial intelligence applica-tion that can be used in the decision support of physicians.The shape of the capnogram obtained from the gas sampleexhaled by the patient and thus the parameters calculatedfrom it are different for healthy people and those with res-piratory diseases.Categories and Subject DescriptorsH.4 [Information Systems Applications]: Decision Support;J.3 [Computer Applications]: LIFE AND MEDICAL SCI-ENCES—CapnographyGeneral TermsApplicationsKeywordsDecision support, Neural networks, Capnography1. INTRODUCTIONCapnography is a non-invasive method for the numericaland graphical analysis of exhaled CO2 concentration. Time-based capnography is part of routine daily patient monitor-ing during mechanical ventilation and anesthesia. For spon-taneously breathing patients, the method has the advantagethat it does not require the patient to carry out any specialbreathing maneuvers, the measurement is easy to perform,and therefore requires minimal cooperation. It also holdsthe potential for the diagnosis of obstructive airway disease,as bronchospasm severity can be quantitatively assessed [4,6]. The feasibility of non-invasive examinations is essentialin pediatrics, so it also opens up new areas of application forcapnography [7, 9, 10]. Although the analysis of capnogramshape parameters is not yet a standard part of patient mon-itoring, it appears promising in the monitoring of chronicrespiratory diseases, as it provides useful information on thepathophysiological processes of pulmonary ventilation, suchas airway patency and lung recoil tendency.In capnographic studies, the carbon dioxide content of ex-haled air can be considered as a function of time or plottedagainst the exhaled gas volume. In the former case, we aretalking about time-based, while in the latter case we aretalking about volumetric capnography.In the first part of the article, we examine the formal prop-erties of time-based capnograms. Possible parameters de-scribing the shape of the curve are presented. In the secondpart we introduce a neural network based method that usesthese parameters to help physicians in diagnosing patients.2. THE CAPNOGRAMS AND THEIR PARAM-ETERSThe capnogram curve plots the partial pressure of the CO2content of the exhaled gas against time or volume. The par-tial pressure of a given gas in a gas mixture is the pressurethat a gas in question would create alone if it filled the avail-able space alone. The partial pressure of CO2 is denoted byPCO2.The capnogram consists of an exhalation segment and aninhalation segment. In this study we focused only on theshape indices of the exhalation section. The three phases ofthe exhalation segment (Phases 1-3) contain different slopes,angles and other parameters which are described in manyarticles and textbooks (e.g. [2, 3, 8]).Figure 1: General form of time-based capnogramsFigure 2: Phases of a capnogram with End-tidalCO2(ETCO2)2.1 The calculated parametersThe various morphological parameters are calculated usingmathematical methods, which are presented in this subsec-tion. The resulting capnographic indices - in the knowledgeof the patients’ condition - provide an opportunity to assessthe characteristics of healthy and chronic respiratory pa-tients (see [12] for more details). We aim to calculate theseparameters as accurately and objectively as possible. Thiscreates the opportunity to apply learning algorithms and au-tomatically determine the condition of the patients studied.As a first step, faulty respiratory cycles were filtered outbased on physiological rules that were supported by mea-surement techniques.The parameter calculator smoothed thepoints of the raw curve using the moving average method. Inthis case, each point was replaced by an average calculatedfrom a specified number of adjacent points. For the 100Hzsampling frequency used for recording, we found the 9-pointmoving average to be the most suitable. Then, for eachpoint of the smoothed curve, we calculated the first-orderderivatives using the standard differential quotient. Sincethe curve containing the first derivatives can also be slightlynoisy, we performed the previous smoothing algorithm forthis as well. Then, following the same method, we calculatedthe curve containing the second derivative and its smoothedversion. Finally, using the smoothed derivative 2 curve, thestarting point of Phase 2 (local maximum) and the end pointof Phase 3, i.e. the end of exhalation (local minimum) canbe determined. It should be noted that the starting point ofthe exhalation cannot be precisely determined only from thetime capnogram curve. However, before the start of Phase2, we can find the point where the curve still takes approxi-mately a value of 0, and then this point can be considered asthe starting point of the fitting algorithm described below.We then fit a function to the exhalation sections obtained aspreviously described using the method introduced by Tus-man et al. in [11]. The beginning of Phase 2 and the end ofPhase 3 have already been determined as described above,and its post-fitting correction is not necessary. However, af-ter fitting, the first, second, and third derivatives must bere-determined (now on the fitted curve). The end point ofPhase 2 (the starting point of Phase 3) is obtained from thelocal maximum of the calculated third derivative.2.1.1 The slopes of Phases 2 and 3 (S2, S3)To determine the inflection point of Phase 2, we use the first-order derivative values, which mathematically represent theslope of the line drawn at a given point on the curve. Theslope at the inflection point will be the largest. The slope ofPhase 2 (S2) is the maximum slope that can be read at thisinflection point [11]. The slope of Phase 3 (S3) is the slopeof the line fitted to the middle third of Phase 3, which is asimplified but not significantly different modification of themethod used by Tusman et al [11].Figure 3: The slopes of time-based capnograms2.1.2 End-tidal CO2 (ETCO2)The carbon dioxide concentration increases throughout Phase3, so it normally peaks at the end of the phase. This is thefinal exhalation CO2 concentration (ETCO2, PETCO2),which is equal to the carbon dioxide partial pressure read atthe end of Phase 3.2.1.3 The normalized slopes of Phases 2 and 3 (Sn2,Sn3)The normalized slopes of Phase 2 (Sn2) and Phase 3 (Sn3)are obtained by dividing the slopes of the second and thirdphases (S2, S3) by the value of ETCO2.2.1.4 Sn3/Sn2The quotient of the Sn3 and Sn2 values.2.1.5 D2min and D2maxThe maximum and minimum of the second derivative, therate of change of the start and end points of Phase 2 (thelower and upper curves).2.1.6 The α angle (Q)The angle enclosed by the slopes of Phases 2 and 3.2.1.7 The area ratio (AR)The area ratio in the section between the inflection pointand the beginning of Phase 3 is the quotient of the areaunder the curve and the area of the entire rectangle. It ispractically the shape of the transition from Phase 2 to Phase3.2.1.8 Squared difference (R2SUM)The sum of the squares of the differences between the pointsof the raw, original curve and the fitted one. As previouslydescribed, the original capnogram curve contains higher fre-quency noises, which may have physiological reasons. There-fore, these sums of squares are used to examine the differ-ences in the curves of the patients in each group.2.1.9 Respiratory rates (RR)In the absence of flow data, the exact length of respiratorycycles cannot be determined from the time capnogram alone.Thus, the length of the given respiratory cycle can be esti-mated from the combined length of Phases 2 and 3. Exam-ining the measurements in parallel with the flow measure-ment, we found that the combined length of Phases 2 and 3is about 65 percent of the respiratory cycle. Currently, weuse this ratio to estimate respiratory length, from which wecalculate the actual respiratory rate.3. THE INPUT DATA AND THE STRUCTUREOF THE NETWORKThe data used for teaching the network were as follows:• All time-based parameters calculated from mainstreammeasurements: S2T, S3T, ETCO2, Sn2, Sn3,Sn3/Sn2, D2min, D2max, Q, AR, R2SUM, RR (Sep-arate records for each breathing cycle).• Gender of the patient.• Class of the patient’s age at the time of examina-tion. (The patient’s age was divided into 10-year-longclasses. For example: 13 years old, 17 years old ->class: 1, 33 years old -> class: 3, 60 years old, 62years old -> class: 6, etc. This was necessary becausewithout classification only a few measurements wouldbelong to some ages, which would impair the effective-ness of learning.)• Class of the patient’s body weight at the time of exam-ination. (The patient’s body weight was divided intoclasses of 10 kilograms, in the same way as for age.)We used one label for teaching, which was a manual medicaldiagnosis of the patient for the test. (One test could includeseveral measurements. One measurement could only belongto one test. One test could only have one diagnosis.) Weonly used measurements with a ”healthy” or ”asthmatic” di-agnosis. We omitted from teaching the load measurementsand the measurements marked as incorrect.The method was implemented in Java and relied on theDeeplearning4j library [1]. The training of the neural net-work and the diagnosis prediction with the trained neuralnetwork ran on the following configuration: Intel Core i710700K CPU, 32GB DDR4 RAM, 256GB SSD, 2TB HDD,Nvidia GeForce 8500 GT video card.The neural network had 3 hidden layers, each with 50 neu-rons. For each hidden layer, the activation function was theTANH function. The activation function of the output layerwas the SIGMOID function. We gave 6000 epochs for teach-ing, but according to the log files, no significant learning tookplace after the 652nd epoch. The training was performed ona record of 3141 healthy and 16670 asthmatic breathing cy-cles, which lasted 2169 seconds on the configuration givenabove.4. RESULTSSince the training was done per respiratory cycle (the pa-rameters are also calculated separately for each cycle), thediagnosis prediction with the trained neural network is alsodone per respiratory cycle. For each measurement, we cal-culated how many cycles of the measurement were ”healthy”and how many cycles were ”asthmatic”. (The prediction isnot performed for cycles marked as incorrect.) If the num-ber of healthy predictions is lower than the number of asth-matic predictions, then the entire measurement is consideredasthmatic. Otherwise, the entire measurement is consideredhealthy. The number of measurements used in the predic-tion was 648. Considering the ”asthmatic” diagnosis as pos-itive and the ”healthy” diagnosis as negative we found thefollowings:• True positive (TP): 517 (79.78%)• True negative (TN): 107 (16.51%)• False positive (FP): 23 (3.55%)• False negative (FN): 1 (0.15%)TP: The number of measurements for which the manualdiagnosis of the test is ”asthmatic” and the diagnosis ob-tained with the neural network is also ”asthmatic”. TN:The number of measurements for which the manual diagno-sis of the test is ”healthy” and the diagnosis obtained withthe neural network is also ”healthy”. FP: The number ofmeasurements for which the manual diagnosis of the test is”healthy”, but the diagnosis obtained with the neural net-work is ”asthmatic”. FN: The number of measurements forwhich the manual diagnosis of the test is ”asthmatic”, butthe diagnosis obtained with the neural network is ”healthy”.The metrics calculated from these are:• Accuracy: 0.96,• Precision: 0.96,• Recall: 1.00,• F1 Score: 0.98.Here we used the usual metrics of classifiers, based on thefollowing formulas [5]: Accuracy: (TP + TN) / (TP + FP+ TN + FN) Precision: TP / (TP + FP) Recall: TP / (TP+ FN) F1 score: 2* precision * recall / (precision + recall)All of the above metrics must fall within the interval [0.0,1.0]. The closer the value is to 1.0, the better the result.The total running time of the diagnosis prediction was 240seconds for 1361 measurements, so the prediction takes anaverage of 0.1763 seconds per measurement. Comments:1. The evaluation is somewhat distorted by the fact that wehave fewer healthy subjects than asthmatics.2. It is similarly distorted by the fact that we used all themeasurements of all asthmatic and healthy tests from thedatabase for teaching. This is due to the limited numberof measurements. In the case of several measurements, wecould use only a small part of the measurements duringteaching, and test the neural network on the larger part.That way we would get more objective test results.5. CONCLUSIONSIn this research we developed a neural network based ap-plication that uses capnography measurements to help thediagnosis of asthma. Possible future works are the follow-ings:1. Training the neural network with the raw measure-ment data as well, not only with the calculated pa-rameters. This is expected to require more hardwareresources and time. An advantage may be that theneural network can also learn useful information thatis lost during the parameter calculation.2. Training the neural network with the volumetric pa-rameters or together with volumetric and time-basedparameters. The disadvantage here may be that thereare no volumetric parameters for purely time-basedmeasurements without flow data.3. Teaching the neural network for the different severitiesof asthma, and using the trained neural network todistinguish between them.4. Teaching the neural network for other diseases, e.g.COPD (and its sub-conditions), ACOS (and its sub-conditions), COVID, etc. Distinguishing these diseaseswith the help of a trained neural network.6. ACKNOWLEDGMENTSThis study was carried out in cooperation with PROFIT-EXPERT Ltd., University of Szeged, Bay Zoltán NonprofitLtd. for Applied Research, Optin Ltd. in the frameworkof the EU-funded Hungarian project ”CAPNO - researchon the application possibilities of capnography and develop-ment of an instrument for the diagnosis of asthma and otherrespiratory diseases (GINOP-2.2.1-15-2017-00046).”7. REFERENCES[1] Deeplearning4j Suite Overview.https://deeplearning4j.konduit.ai/. [Accessed16-Jul-2022].[2] K. Bhavani-Shankar, A. Y. Kumar, H. S. L. Moseley,and R. Ahyee-Hallsworth. Terminology and thecurrent limitations of time capnography: A briefreview. Journal of Clinical Monitoring, 11(3):175–182,May 1995.[3] K. Bhavani-Shankar and J. H. Philip. Definingsegments and phases of a time capnogram. Anesthesia& Analgesia, 91(4):973–977, Oct. 2000.[4] J. B. Chambers, P. J. Kiff, W. N. Gardner,G. Jackson, and C. Bass. Value of measuring end tidalpartial pressure of carbon dioxide as an adjunct totreadmill exercise testing. BMJ, 296(6632):1281–1285,may 1988.[5] D. Chicco and G. Jurman. The advantages of thematthews correlation coefficient (MCC) over f1 scoreand accuracy in binary classification evaluation. BMCGenomics, 21(1), jan 2020.[6] C. Chopin, P. Fesard, J. Mangalaboyi, P. Lestavel,M. C. Chambrin, F. Fourrier, and A. Rime. Use ofcapnography in diagnosis of pulmonary embolismduring acute respiratory failure of chronic obstructivepulmonary disease. Critical Care Medicine,18(4):353–357, apr 1990.[7] N. Eipe and D. R. Doherty. A review of pediatriccapnography. Journal of Clinical Monitoring andComputing, 24(4):261–268, jul 2010.[8] J. S. Gravenstein, M. B. Jaffe, N. Gravenstein, andD. A. Paulus, editors. Capnography. CambridgeUniversity Press, Mar. 2011.[9] B. D. Guthrie, M. D. Adler, and E. C. Powell.End-tidal carbon dioxide measurements in childrenwith acute asthma. Academic Emergency Medicine,14(12):1135–1140, dec 2007.[10] S. Kunkov, V. Pinedo, E. J. Silver, and E. F. Crain.Predicting the need for hospitalization in acutechildhood asthma using end-tidal capnography.Pediatric Emergency Care, 21(9):574–577, sep 2005.[11] G. Tusman, A. Scandurra, S. H. Böhm,F. Suarez-Sipmann, and F. Clara. Model fitting ofvolumetric capnograms improves calculations of airwaydead space and slope of phase III. Journal of ClinicalMonitoring and Computing, 23(4):197–206, June 2009.[12] B. You, R. Peslin, C. Duvivier, V. D. Vu, andJ. Grilliat. Expiratory capnography in asthma:.European Respiratory Journal, 7(2):318–323, Feb.1994.

A Neural Network Based Classification Algorithm for Asthma Using Capnography

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A Neural Network Based Classification Algorithm for Asthma Using Capnography

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