Chagas disease American trypanosomiasis is caused by a flagellated parasite: Trypanosoma cruzi, transmitted by an insect of the genus Triatoma and also by blood transfusions. In Latin America, the number of infected people is approximately 6 million, with a population exposed to the risk of infection of 550000. It is our interest to develop a non-invasive and low-cost methodology, capable of detecting any early cardiac alteration that also allows us to see dysautonomia or dysfunction within 24 hours and with this it could be used to detect any cardiac alteration caused by T early Cruzi. For this, we analyzed the 24-hour Holter ECG records in 107 patients with ECG abnormalities (CH2), 102 patients without ECG alterations (CH1) who had positive serological results for Chagas disease and 83 volunteers without positive serological results for Chagas disease (CONTROL). Approximate entropy was used to quantify the regularity of electrocardiograms (ECG) in the three groups. We analyzed 288 ECG segments per patient. Significant differences were found between the CONTROL-CH1, CONTROL-CH2 and CH1-CH2 groups.Postprint (published version

Gomis Román, Pedro

Manrique, Miriam

Ravelo, Antonio

Vizcardo, Miguel

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Chagas disease American trypanosomiasis is caused by a flagellated parasite: Trypanosoma cruzi, transmitted by an insect of the genus Triatoma and also by blood transfusions. In Latin America, the number of infected people is approximately 6 million, with a population exposed to the risk of infection of 550000. It is our interest to develop a non-invasive and low-cost methodology, capable of detecting any early cardiac alteration that also allows us to see dysautonomia or dysfunction within 24 hours and with this it could be used to detect any cardiac alteration caused by T early Cruzi. For this, we analyzed the 24-hour Holter ECG records in 107 patients with ECG abnormalities (CH2), 102 patients without ECG alterations (CH1) who had positive serological results for Chagas disease and 83 volunteers without positive serological results for Chagas disease (CONTROL). Approximate entropy was used to quantify the regularity of electrocardiograms (ECG) in the three groups. We analyzed 288 ECG segments per patient. Significant differences were found between the CONTROL-CH1, CONTROL-CH2 and CH1-CH2 groups

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Application of the entropy of approximation for the nonlinear characterizationin patients with Chagas disease

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Application of the Entropy of Approximation for the Nonlinear Characterization in Patients with Chagas Disease

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Application of the Entropy of Approximation for the nonlinear characterizationin patients with Chagas DiseaseM. Vizcardo†, A. Ravelo‡, M. Manrique†, P. Gomis§†Escuela Profesional de Fı́sica, Universidad Nacional de San Agustı́n de Arequipa, Perú‡Instituto for Technological Develoment and Innovation in Communications, Universidad de LasPalmas de Gran Canaria, Spain§ Universitat Politècnica de Catalunya-Barcelona Tech (UPC), SpainAbstractChagas disease American trypanosomiasis is caused bya flagellated parasite: Trypanosoma cruzi, transmitted byan insect of the genus Triatoma and also by blood trans-fusions. In Latin America, the number of infected peopleis approximately 6 million, with a population exposed tothe risk of infection of 550000. It is our interest to de-velop a non-invasive and low-cost methodology, capableof detecting any early cardiac alteration that also allowsus to see dysautononia or dysfunction within 24 hours andwith this it could be used to detect any cardiac alterationcaused by T early Cruzi. For this, we analyzed the 24-hour Holter ECG records in 107 patients with ECG ab-normalities (CH2), 102 patients without ECG alterations(CH1) who had positive serological results for Chagas dis-ease and 83 volunteers without positive serological re-sults for Chagas disease (CONTROL). Approximate en-tropy was used to quantify the regularity of electrocardio-grams (ECG) in the three groups. We analyzed 288 ECGsegments per patient. Significant differences were foundbetween the CONTROL-CH1, CONTROL-CH2 and CH1-CH2 groups.1. IntroductionChagas disease is an endemic disease, which is causedby a flagellated parasite: Trypanosoma cruzi (T. cruzi),transmitted by an insect of the genus Triatoma, Chagasdisease can also be acquired by blood transfusions. Ac-cording to WHO in Latin America, the number of infectedpeople is approximately 6 million, with a population ex-posed to the risk of infection of 568,000[1]. Previous stud-ies show that 40% of the population infected with T. cruzihave cardiac compromise[2, 3, 11]. In Chagas disease wecan distinguish two phases: an initial acute phase of in-fection and a prolonged intermediate chronic phase, wherethe disease is often clinically silent and asymptomatic. It isour interest to develop a low-cost non-invasive methodol-ogy that allows to distinguish dysautononia or dysfunctionin the course of 24 hours and with this it could be used asan early marker that shows cardiac alterations produced byT. cruzi2. DatabaseFor this work, we have used the electrocardiogram(ECG) database of the Instituto de Medicina Tropical(IMT) of the Universidad Central de Venezuela. Thepatients and volunteers underwent the following tests:clinical evaluation, positive Machado-Gerreiro serologi-cal test, chest x-rays, echocardiogram, electrocardiogramand Holter recording (24 hours). They were classifiedinto three groups: 83 healthy volunteers called the Controlgroup; 102 patients infected with only the positive serolog-ical test of Machado-Gerreiro (clinical evaluation, chest x-rays, echocardiogram, electrocardiogram and Holter werenormal) called group CH1 and 107 seropositive patientswith incipient heart disease, atrioventricular block (BAV)involvement first grade, sinus bradycardia (BS) or rightbundle branch block of His (BRDHH), all of which werenot being treated with medications, called the CH2 group.All were outpatients and informed consent was obtainedfrom all of them.3. Method3.1. PreprocessedWe will use the 24-hour ECG holter register (Rozzinmodel 151 with two leads), which we will divide into 288segments (framers) corresponding to 5 minutes of ECG(approximately 300 beats) for each volunteer or patient.ECG signals were recorded at 500 Hz with 12 bits of res-olution, one channel was recorded. Because we will usea method based on the regularity of the data, no filter wasused.Computing in Cardiology 2019; Vol 46 Page 1 ISSN: 2325-887X DOI: 10.22489/CinC.2019.1123.2. Feature extractionIn statistics, an approximate entropy (ApEn) is a tech-nique used to quantify the amount of regularity and theunpredictability of fluctuations over time-series data[4, 5].The approximate entropy (ApEn) was developed bySteve Pincus[4, 5] based on the entropy of Kolmogorov-Sinai KS, proposed by Grassberger and Procaccia[6] andmodified by Takens[7]. Later Richman[8] made a mod-ification of ApEn, which is the sampling entropy (Sam-pEn). Both ApEn and SampEn are based on comparisonsof component-to-component embedment vectors (m = 2)and with a threshold of 20% of the standard deviation ofthe ECG (r = 0.20). The difference between the entropiesis that ApEn does not take into account the parisons of theembedment vectors with itself and the way to calculate thelogarithm[9]. To calculate ApEn:ApEn(m, r,N) = −1N −mN−m∑i=1log (AiBi) (1)where Bi are the vectors of embedding dimension m andAi are the vectors of dimension of embendding m + 1, ris the threshold that is typically 20% of the value of thestandard deviation and N is the number of elements.SampEn is calculated as:SampEn(m, r,N) = − log(∑N−mi=1Ai∑N−mi=1Bi) = − log(AB)(2)similar a ApEn m, r and N . The ApEn and SampEn wasapplied at 288 framers each frame corresponds to 5 min-utes per patient3.3. Kruskal Wallis test and logistic regres-sionThe Kruskal Wallis test will be used to find signifi-cant differences (p − value < 0.05) between the groups:Control-CH1, Control-CH2 and CH1-CH2 in the 288framer respectively. A logistic regression is also used toevaluate the circadian profiles of the average values ofApEn, SampEn.4. ResultsIn Figure 1 we can see that, on average, the three groupshave an increase in the values of ApEn and SampEn offrame 50 that would correspond to 4:00 hours and then adecrease of frame 250 corresponding to 20:00 hours, Thisresult coincides with[9] where tachograms are used.4.1. Approximate Entropy (ApEn)• The average values of the circadian profiles of the ApEnof group CH1 (red) always remain below the average val-ues of the control group (blue)• We observe that the average values of the circadian pro-files of ApEn of the control and CH1 groups show thegreatest difference in framer 50 and 175 corresponding to5:00 and 14:00 hours.• As for the average values of the circadian ApEn profilesof the CH2 group (black) they remain below the averagevalues of the control group (blue) after the framer 90• We also observe that the average values of the circadianApEn profiles of the control and CH2 groups show thegreatest difference in framer 100 and 175 correspondingto 8:00 and 14:00 hours.0 50 100 150 200 250Circadian profile of the average ApEn d=2 r=0.2 in groups of 5 min0.20.250.3ApEnControl (blue), CH1 (red), CH2(black)0 50 100 150 200 250Circadian profile of the average SampEn d=2 r=0.2 in groups of 5 min0.10.120.140.16SampEnControl (blue), CH1 (red), CH2(black)Figure 1. The average values of ApEn and SampEn of 24hours (288 frames)As for the Kruskal Wallis test figure 2• We found significant differences between the Controland CH1 groups in several frames, but we can highlight inframes 50, 100 and 150-180 corresponding to 4:00, 8:0012:00-15:00 hours.• With respect to the control and the CH2 group, there aresignificant differences in several frames, we can highlightthe 100 and 175 frames corresponding to 8:00 and 14:00hours.• For CH1-CH2 there are significant differences in theframe of 50 corresponding to 4:00 hoursWe have used logistic regression to discriminate betweenthe Control and CH2 groups of ApEn for this, 50% of theframes are chosen at random (144) for training and theother 50% for validation, finding 80% specificity and 60%sensitivity in framers 100 and 175 corresponding to 8:00and 14:00 hours respectively figura 4Page 20 50 100 150 200 250 300framer00.51p-valueKruskal-Wallis test ApEn Groups: Control-CH10 50 100 150 200 250 300framer00.51p-valueKruskal-Wallis test ApEn Groups: Control-CH20 50 100 150 200 250 300framer00.51p-valueKruskal-Wallis test ApEn Groups: CH1-CH2Figure 2. Kruskal-Wallis test of ApEn of the 288 framer4.2. Sample Entropy (SampEn)• The average values of the circadian profiles of the Sam-pEn of group CH1 (red) always remain below the averagevalues of the control group (blue)• We observe that the average values of the circadian pro-files of SampEn of the control and CH1 groups show thegreatest difference in framer 50 and 175 corresponding to5:00 and 14:00 hours.• As for the average values of the circadian SampEn pro-files of the CH2 group (black) they remain below the av-erage values of the control group (blue) after the framer75• We also observe that the average values of the circadianSampEn profiles of the control and CH2 groups show thegreatest difference in framer 100 and 175 corresponding to8:00 and 14:00 hours.As for the Kruskal Wallis test figure 3• We found significant differences between the Controland CH1 groups in several frames, but we can highlight inframes 50, 100 and 150-180 corresponding to 4:00, 8:0012:00-15:00 hours.• With respect to the control and the CH2 group, there aresignificant differences in several frames, we can highlightthe 100 and 175 frames corresponding to 8:00 and 14:00hours.• For CH1-CH2 there are significant differences in theframe of 50 corresponding to 4:00 hoursWe have used logistic regression to discriminate betweenthe Control and CH2 groups of SampEn for this, 50% ofthe frames are chosen at random (144) for training and theother 50% for validation, finding 80% specificity and 60%sensitivity in framers 100 and 175 corresponding to 8:000 50 100 150 200 250 300framer00.51p-valueKruskal-Wallis test SampEn Groups: Control-CH10 50 100 150 200 250 300framer00.51p-valueKruskal-Wallis test SampEn Groups: Control-CH20 50 100 150 200 250 300framer00.51p-valueKruskal-Wallis test SampEn Groups: CH1-CH2Figure 3. Kruskal-Wallis test of SampEn of the 288 framer0 50 100 150 200 250Prob. logistic average ApEn in groups 5 min0.20.40.60.8PControl (blue), CH2(black)0 50 100 150 200 250Prob. logistic average SampEn in groups 5 min0.20.40.60.8PControl (blue), CH2(black)Figure 4. Logistic classifier of the average values of ApEnand SampEn of the 288 framerand 14:00 hours respectively figura 55. Discussion and conclusionsThe Apen and SampEn of ECG can be used as a mea-sure of heart rate variability similar to HVR, as well asother entropies[10]The CH1 group, its average values of ApEn and Sam-pEn, show a decrease or enervation in their baroreflectorresponse or autonomic nervous system.The CH2 group its average values of ApEn and SampEn insome framers show an increase with respect to the Controlgroup this would show a dysautonomy or lack of controlof their autonomic nervous system.Page 30 50 100 150 200 25000,010,020,030,040,05p-valuep-value ApEn (black) and SampEn (red)Control-CH10 50 100 150 200 25000,010,020,030,040,05p-valueControl-CH20 50 100 150 200 250Framer00,010,020,030,040,05p-valueCH1-CH2Figure 5. p-value less than 0.05 of ApEn and SampEnFigure 5 shows the p-values less than 0.05 that would bethe framers or moments where the three groups are sig-nificantly different, that is to say the Control group andthe CH1 group are significantly different in 04:00 , 08:00,10:00-14:00 hours. The Control group and CH2 are sig-nificantly different from 08:00, 12:00 and 14:00 hours. Fi-nally there is a significant difference between the groupsCH1 and CH2 only at 04:00 hours.AcknowledgementsUniversidad Nacional de San Agustin de Arequipa Perúand Instituto for Technological Develoment and Innova-tion in Communications, Universidad de Las Palmas deGran Canaria, SpainConflicts of InterestThe authors declare no conflict of interestReferences[1] World Health Organization, The World Health Report, 2017,2:75,9–16, http://www.who.int/wer[2] Moleiro F. and I. Mendoza, Circulation, 1978, 113.[3] Moleiro F, Mendoza I, Miocardiopatia Chagásica Crónica.Estudio Epidemiológico, Acta Cient. Venez. 1980, 31:66[4] Pincus S, Gladston I, A Regularity Statistic for Medical DataAnalisys, Journal of Clinical Monitoring October, 1991.1980, Vol 7 No 4[5] Pincus S Approximate entropy as a measure of system com-plexity, Proc. Nati. Acad. Sci. 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M. and et.al., Dysautonomy in differentdeath risk groups (Rassi score) in patients with Chagas heartdisease, Pacing Clin Electrophysiol, 2018, 41:238245.[20] Caeiro A. , Iosa T., Chronic Chagas’ disease: possi-ble mechanism of sinus bradycardia. Can J Cardiol, 1994,10:765-8[21] Octavio J, et.al., Perfiles circadianos de la frecuenciacardı́aca y de su variabilidad instantánea en una poblaciónde pacientes con infección chagásica crónica. Rev Esp Car-diol., 2004, 57(2):130-7[22] Ravelo-Garcı́a, et.al, Cepstrum feature selection for theclassification of sleep apnea-hypopnea syndrome based onheart rate variability. In Computing in Cardiology Confer-ence (CinC), 2013 (pp. 959-962). IEEE.[23] Ravelo-Garcı́a, et.al, An approach to the enhancement ofsleep apnea detection by means of detrended fluctuationanalysis of RR intervals. In Computing in Cardiology Con-ference (CinC), 2014 (pp. 905-908). 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Application of the entropy of approximation for the nonlinear characterizationin patients with Chagas disease

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