Eiriksdóttir, Dagbjört Helga

Hansen, John

Plocharski, Maciej

Riknagel, Diana

Schmidt, Samuel Emil

Struijk, Johannes Jan

Sæderup, Rasmus Gundorff

Zimmermann, Henrik

English

    Aalborg UniversitetQuality Assessment of Maternal and Fetal Cardiovascular Sounds Recorded from theSkin near the Uterine Arteries During PregnancyEiriksdóttir, Dagbjört Helga; Sæderup, Rasmus Gundorff; Riknagel, Diana; Zimmermann,Henrik; Plocharski, Maciej; Hansen, John; Struijk, Johannes Jan; Schmidt, Samuel EmilPublished in:Computing in CardiologyDOI (link to publication from Publisher):10.22489/CinC.2019.093Creative Commons LicenseCC BY 4.0Publication date:2019Document VersionPublisher's PDF, also known as Version of recordLink to publication from Aalborg UniversityCitation for published version (APA):Eiriksdóttir, D. H., Sæderup, R. G., Riknagel, D., Zimmermann, H., Plocharski, M., Hansen, J., Struijk, J. J., &Schmidt, S. E. (2019). Quality Assessment of Maternal and Fetal Cardiovascular Sounds Recorded from theSkin near the Uterine Arteries During Pregnancy. Computing in Cardiology, 46.https://doi.org/10.22489/CinC.2019.093General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.            ? Users may download and print one copy of any publication from the public portal for the purpose of private study or research.            ? You may not further distribute the material or use it for any profit-making activity or commercial gain            ? You may freely distribute the URL identifying the publication in the public portal ?Take down policyIf you believe that this document breaches copyright please contact us at vbn@aub.aau.dk providing details, and we will remove access tothe work immediately and investigate your claim.Quality Assessment of Maternal and Fetal Cardiovascular SoundsRecorded From the Skin Near the Uterine Arteries During PregnancyDagbjört H Eirı́ksdóttir1, Rasmus G Sæderup2, Diana Riknagel2,3, Henrik Zimmermann2,Maciej Plocharski1, John Hansen1, Johannes J Struijk1, Samuel E Schmidt11 Department of Health Science and Technology, Aalborg University, Aalborg, Denmark2 Viewcare A/S, Søborg, Denmark3 Department of Obstetrics and Gynaecology, Regional Hospital Viborg, Viborg, DenmarkAbstractMonitoring cardiovascular activity during pregnancy isof high importance for identifying abnormal developmentof the fetus. Automated cardiovascular auscultation of theabdomen in both infrasonic and audible frequencies is anon-invasive method for monitoring the maternal and fetalhealth, including blood flow to the placenta. However, thequality of such recordings is often compromised by arti-facts. The purpose of this study was to automatically iden-tify high-quality auscultation signals. 324 recordings wereobtained with two microphones placed bilaterally on theabdomen of 90 pregnant women (gestational age of 28-41weeks), with signal duration of 30 s - 180 s. The signalswere band-pass filtered to infrasonic frequencies (2.5 Hz -25 Hz) and audible low frequencies (25 Hz - 125 Hz), di-vided into 10 s segments, and areas with unwanted tran-sients were removed. Five features were calculated forsegments of at least five continuous seconds. A logisticregression model was trained and tested using the identi-fied features, obtaining a maximum classification accuracyof 92.8% for the infrasonic frequencies (81.6% sensitivityand 97.0% specificity), and 96.1% accuracy for the audi-ble frequencies (90.4% sensitivity and 97.2% specificity).These results demonstrate the feasibility of automaticalidentification of high-quality segments at infrasonic andaudible frequencies.1. IntroductionCardiovascular complications during pregnancy pose asignificant threat to both fetal and maternal health. A com-promise in the balance of the vascular supply between themother and the fetus leads to adverse health effects [1].Accurate identification and frequent monitoring of suchabnormalities during pregnancy is therefore of high impor-tance. Early intervention could reduce maternal and fetalmorbidity and mortality, and facilitate clinical decision-making before the onset of irreversible consequences tothe fetus [2]. Automated cardiovascular auscultation of thepregnant abdomen in both audible and infrasonic frequen-cies is a non-invasive procedure for obtaining informationon the maternal and fetal health, including blood flow tothe placenta [3–5]. However, the quality of auscultationrecordings is often compromised by artifacts from inter-ference of respiration, from sounds of the digestive system,or from unrelated ambient sounds [3]. Consequently, theplacental and fetal heart sounds are often obscured, and itis therefore imperative to automatically identify and selectthe high-quality segments during the recording session foranalysis. The aim of this study was to define and automat-ically identify such high-quality signals.2. Methods2.1. Study PopulationThe data in this study was obtained from a cross-sectional study of women aged 18 years or older with a sin-gleton pregnancy at 28 - 41 weeks (median of 33 weeks),conducted at Regional Hospital Viborg, Denmark, betweenMay 1st and August 1st, 2013 [5]. A set of 324 recordingswas obtained from two microphones placed bilaterally onthe abdomen of 90 pregnant women, with signal durationvarying from 30 s to 180 s (median of 30 s), detailed in[5]. The women had body mass indices (BMI) of 26.1 ±3.3 kg/m2, and maternal pulse of 80.0 ± 10.1 bpm. 58 outof the 90 pregnancies were non-pathological.2.2. Pre-processingThe recordings were divided into 10 s non-overlappingsegments (in total 3076 segments). First, the mean wassubtracted to obtain zero-mean and the segments filteredwith a 50 Hz notch filter. The signal segments were fil-tered with two 4th order Butterworth band-pass filters withthe following cut-off frequencies: infrasonic (2.5 - 25 Hz),Computing in Cardiology 2019; Vol 46 Page 1 ISSN: 2325-887X DOI: 10.22489/CinC.2019.093and audible (25 - 125 Hz). In the infrasonic frequencies,the heart rhythm of the mother can be detected, and in theaudible spectrum sounds from the fetal heart can be heard(Fig. 1).An envelope was generated from each segment using theHilbert transform. The envelope was then low-pass fil-tered with a 4th. order Butterworth filter with a cut-offfrequency of 15 Hz. Since the segments could contain un-wanted transients or interruptions of higher amplitude thanthe heart sounds, such as speech, abdominal sounds, orpossibly friction spikes, an iterative moving average peakdetector [6] was used to remove areas with these transients.Usable segments were selected based on a criterionof a sub-segment of at least 5s being present after tran-sient removal, after which sample autocorrelation func-tions (ACF) of their envelopes were calculated. Shortersegments were eliminated, leaving 2457 segments at in-frasonic frequency, and 2213 at audible low frequency forfurther analysis.In presence of a heart rhythm, the ACF would show highrepetitive peaks, and the time lag to the first highest peakafter zero lag represents the duration of one cardiac cycle.If distinctive valve sounds were present in the signal, theACF would show one or two smaller peaks in front of thefirst high peak. These peaks represent duration of systoleand diastole (Fig. 2). To eliminate the influence of theACF zero lag peak on the calculated features, the signalwas truncated at the first local minimum and lasting 5 sfrom that point.2.3. Manual annotationsThe signals segments were manually annotated by vi-sual inspection of each segment and its ACF after transientremoval on a scale from 1 - 5 (Table 1). Fig. 3 shows thedistribution of the quality of the segments after the manualannotation. Segments with scores 1 and 2 were labeled asAcceptable signal while the rest was labeled as Unaccept-able signal. This resulted in 827 signal segments at theinfrasonic frequencies and 472 at the audible low frequen-cies.2.4. Feature calculationFive features were computed from the signal segments:sample entropy, variance, maximum peak, relative maxi-mum peak, all calculated from the truncated ACF, and kur-tosis, calculated from the time signal segment. Sample en-tropy is a complexity measurement, where a lower sampleentropy indicates more repetition in the signal segment. Itwas calculated from a downsampled (80 Hz) truncated au-tocorrelation signal with phase state m = 2 dimensionsand a tolerance ε = 0.2 times the standard deviation of theautocorrelation signal. A good quality signal was expectedFrequencies: 2.5 - 25 Hz0 1 2 3 4 5Time (s)                  Amplitude (a.u.)Frequencies: 25 - 125 HzFigure 1. A 5 s sub-segment of a recording filtered at infra-sonic (upper panel) and audible low (lower panel) frequen-cies. Pulsatile wave pattern can be seen, showing the moth-ers (upper panel) and fetus’ (lower panel) heart rhythm.-0.500.51Truncated autocorrelation Frequencies: 2.5 - 25 Hz0 1 2 3 4 5Lag (s)-0.500.51                       Normalized AmplitudeTruncated autocorrelation Frequencies: 25 - 125 HzFigure 2. Autocorrelation function from an envelope of a10 s signal segment filtered at the infrasonic (upper panel)and at the audible low frequencies (lower panel). Repeti-tive peaks can be seen.1: Excellent2: Good3: Borderline4: Poor5: No signalSignal Quality Score02040Percent segments (%)Acceptable Unacceptable 2.5 - 25 Hz25 - 125 HzFigure 3. Quality distribution of signal segments aftermanual annotation. Blue bars show percentage of seg-ments at infrasonic frequencies and red bars show percent-age of segments at audible low frequencies.to have a high peak after a lag corresponding to the intervalbetween heart beats.The relative maximum peak of the truncated ACF wascalculated as the difference between the maximum peakand the minimum valley in the samples with a lag less thanthe lag of the maximum peak. This was done as visualPage 2Table 1. Signal quality score descriptions for manual annotations.Score SignalQualityTime signaldescriptionAutocorrelation signaldescription1 ExcellentClear pulsed pattern without noise(either maternal or fetal)High repetitive peaks present(either maternal or fetal)2 GoodNoisy pulsed pattern(either maternal or fetal)Repetitive peaks present(either maternal or fetal)3 BorderlineVery noisy pulsed pattern(either maternal or fetal)Low repetitive peaks(either maternal or fetal)4 Poor Noisy signal without any patterns No repetitive peaks5 No signal No signal or pure noise No repetitive peaksinspection showed that a false maximum peak could oc-cur if the ACF has a gradual decrease throughout the sig-nal. Kurtosis is a statistical measure of the tailedness ofthe probability function. Distributions that follow a nor-mal distribution have a kurtosis of 3. The sound signalsare expected to have peaks repeating for every heartbeat,and their distribution is thus expected to have a tailed dis-tribution and a kurtosis larger than 3 [7].2.5. Post-processingTwo thirds of the patients were randomly selected fortraining of the classifier while the rest was used for testing.Means and standard deviations of all features were calcu-lated across all recordings in the training set. Both setswere then normalized by subtracting the means and divid-ing by the standard deviations that were calculated fromthe training set. This resulted in 232 recordings analyzedfor training (with 1675 and 1571 processed segments forinfrasonic and audible low frequencies, respectively), and92 recordings for testing (with respectively 782 and 692segments for infrasonic and audible low frequencies).2.6. ClassificationLogistic regression was used as a classification model.As the signal classification was binary, the logisticfunction-derived a probability equal to the conditionalprobability of a positive class label (Acceptable signal) andconsequently the probability of a negative class label, 1 -positive class (Unacceptable signal). The set of trainingdata was used to calculate the conditional log-likelihoodof the labeled classes. Two classifiers were trained andtested, one for each frequency range. Five-fold cross vali-dation was performed on the training data to prevent over-fitting. The classifier was generated using the Classifica-tion Learner application in MATLAB 2018b.3. ResultsTesting of the infrasonic classifier resulted in a maxi-mum accuracy of 92.8%, correctly classifying 218 out of297 Acceptable signal segments in the testing set, corre-sponding to 81.6% sensitivity and 97.0% specificity. Test-ing of the audible classifier resulted in a maximum accu-racy of 96.1%, where it correctly classified 151 out of 167Acceptable signal segments, corresponding to 90.4% sen-sitivity and 97.2% specificity (Table 2). Fig. 4 illustratesthe ROC curves for both classifiers, with an AUC of 0.978and 0.986 for infrasonic and audible low frequencies, re-spectively.The probability of each sample belonging to the Ac-ceptable signal class in relation to the manual annotationscores is shown in Fig. 5. Segments with quality scores1 and 2 have higher probability for belonging to the Ac-ceptable signal class with segments of score 1 very closeto probability of 1. Segments with quality scores 3 and4 have a lower probability of belonging to the Acceptablesignal class. Of the 92 recordings used for testing ofthe classifier, 49 (53.3%) and 46 (50.0%) had at least oneAcceptable signal segment (infrasonic and audible low fre-quencies, respectively).Table 2. Results of automatic quality classification for thetwo frequency bands.Frequencies Acc. Sens. Spec. AUCInfrasonic(2.5 - 25 Hz) 92.8% 81.6% 97.0% 0.978Audible(25 - 125 Hz) 96.1% 90.4% 97.2% 0.9863.1. DiscussionThe current findings show the feasibility of automati-cally identifying high-quality recordings at low frequen-cies. Even though the number of recordings with at leastPage 30 0.2 0.4 0.6 0.8 11 - Specificity00.20.40.60.81Sensitivity2.5 - 25 Hz25 - 125 HzMax accuracyMax accuracyFigure 4. ROC curves for both classifiers. The blue andred marks represent the magnitude of sensitivity and speci-ficity where the classifiers obtained maximum accuracy.1: Excellent2: Good3: Borderline4: PoorSignal Quality Score00.20.40.60.81 Acceptable Class Propability2.5 - 25 Hz25 - 125 HzFigure 5. The probability of belonging to the Accept-able signal class for each of the manual annotation groups.Group 5 (No signal) is not shown as those signals were alleliminated before classification.one Acceptable signal segment did not reflect the perfor-mance of the classifiers, it did show the importance of au-tomatically identifying Acceptable from Unacceptable sig-nal recordings, during a clinical session. Our future workcould include higher frequency bands as well as improve-ment and expansion of the feature selection. Moreover, av-eraging annotations from more than one annotator wouldbe beneficial for training the classifier, to ensure no bias inthe labeling.Investigating the class probability of the segments showsthe possibility of further distinguishing the Excellent qual-ity segments from Good segments, as they generally havea higher and more stable probability (Fig. 5). Thus, theseresults demonstrate the feasibility of automatically identi-fying high-quality sound recordings from lower quality atinfrasonic and audible low frequencies.Conflict of InterestThis project was partly funded by Viewcare A/S. RGSand HZ are in full employment and DR partly employedwith Viewcare A/S.References[1] Mongraw-Chaffin ML, Cirillo PM, Cohn BA. Preeclampsiaand cardiovascular disease death: prospective evidence fromthe child health and development studies cohort. Hyperten-sion 2010;56(1):166–171.[2] Imdad A, Yakoob MY, Siddiqui S, Bhutta ZA. Screening andtriage of intrauterine growth restriction (IUGR) in generalpopulation and high risk pregnancies: a systematic reviewwith a focus on reduction of IUGR related stillbirths. BMCpublic health 2011;11(3):S1.[3] Varady P, Wildt L, Benyó Z, Hein A. An advanced method infetal phonocardiography. Computer Methods and programsin Biomedicine 2003;71(3):283–296.[4] Riknagel D, Dinesen B, Zimmermann H, Farlie R, SchmidtS, Toft E, Struijk JJ. Digital auscultation of the uterine artery:a measure of uteroplacental perfusion. Physiological mea-surement 2016;37(7):1163.[5] Riknagel D, Farlie R, Hedegaard M, Humaidan P, StruijkJJ. Association between maternal vascular murmur andthe small-for-gestational-age fetus with abnormal umbilicalartery Doppler flow. International Journal of GynecologyObstetrics 2017;139(2):211–216.[6] Schmidt SE, Holst-Hansen C, Graff C, Toft E, StruijkJJ. Segmentation of heart sound recordings by a duration-dependent hidden Markov model. Physiological measure-ment 2010;31(4):513.[7] Springer DB, Brennan T, Ntusi N, Abdelrahman HY, ZühlkeLJ, Mayosi BM, Tarassenko L, Clifford GD. Automated sig-nal quality assessment of mobile phone-recorded heart soundsignals. Journal of Medical Engineering Technology 2016;40(7-8):342–355. PMID: 27659352.Address for correspondence:Dagbjört H Eirı́ksdóttirDepartment of Health Science and TechnologyAalborg UniversityFredrik Bajers Vej 7, C1-217, 9220, Aalborg, DenmarkE-mail: dhe@hst.aau.dkPage 4

Quality Assessment of Maternal and Fetal Cardiovascular Sounds Recorded from the Skin near the Uterine Arteries During Pregnancy

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Quality Assessment of Maternal and Fetal Cardiovascular Sounds Recorded From the Skin Near the Uterine Arteries During Pregnancy

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Quality Assessment of Maternal and Fetal Cardiovascular Sounds Recorded from the Skin near the Uterine Arteries During Pregnancy

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