Blaber, Andrew

Hansen, Rolf

Jensen, Martin

Khosrow-Khavar, Farzad

Schmidt, Samuel Emil

Sørensen, Kasper

Tavakolian, Kouhyar

Ulrich, Christian

English

Crossref

Determining the Respiratory State From a Seismocardiographic Signal--A Machine Learning Approach

    Aalborg UniversitetDetermining the Respiratory State From a Seismocardiographic SignalA Machine Learning ApproachUlrich, Christian; Jensen, Martin; Hansen, Rolf; Tavakolian, Kouhyar; Khosrow-Khavar,Farzad; Blaber, Andrew; Sørensen, Kasper; Schmidt, Samuel EmilPublished in:Computing in CardiologyDOI (link to publication from Publisher):10.22489/CinC.2018.203Creative Commons LicenseCC BY 3.0Publication date:2018Document VersionPublisher's PDF, also known as Version of recordLink to publication from Aalborg UniversityCitation for published version (APA):Ulrich, C., Jensen, M., Hansen, R., Tavakolian, K., Khosrow-Khavar, F., Blaber, A., Sørensen, K., & Schmidt, S.E. (2018). Determining the Respiratory State From a Seismocardiographic Signal: A Machine LearningApproach. Computing in Cardiology, 45. https://doi.org/10.22489/CinC.2018.203General 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.Downloaded from vbn.aau.dk on: November 24, 2020Determining the Respiratory State From a Seismocardiographic Signal- A Machine Learning ApproachChristian Ulrich, Martin Jensen, Rolf Oberlin, Kouhyar Tavakolian*, Farzad Khosrow-Khavar**,Andrew Blaber**, Kasper Sørensen and Samuel SchmidtBiomedical Engineering and Informatics, Aalborg University, Denmark* The School of Engineering Science, Simon Fraser University, BC, Canada** Department of Biomedical Physiology and Kinesiology, Simon Fraser University, BC, CanadaAbstractSeismocardiography (SCG) is a non-invasive method formeasurement of vibrations on the chest wall originatingfrom the heart. Respiration changes the morphology of theSCG-signal and analyzing these changes could improvethe diagnostic value of SCG. This study aimed to deter-mine the nasal respiration signal amplitude at mitral clo-sure (MC) and aortic opening (AO) using SCG features.The three proposed methods for this were multiple regres-sion analysis (MRA), support vector regression (SVR), anda neural network (NN). SCG, Electrocardiography andnasal-catheter flow signals were acquired from 18 healthysubjects (age 29 ± 6). SCG-signal fiducial points wereused as features and were found using an automatic algo-rithm followed by manual verification. Fiducial points am-plitudes, timings between these and frequency componentsformed 12 features. All models were trained on 80% of thedata, underwent 10-fold cross-validation and were testedon the remaining 20% of the data. Predictions on test datafor MC and AO time points, the Pearson correlations co-efficient, and sum of squared errors of prediction were:(rMC , rAO, SSEMC , SSEAO) for the following models:NN (0.908, 0.904, 11.71, 12.05), SVR (0.881, 0.833, 18.95,19.76) and MRA (0.450, 0.437, 51.21, 51.48). These pre-dictive models show a strong correlation between the SCG-signal and respiration.1. IntroductionThe clinical applications for seismocardiography (SCG)are promising, providing a simple method for measuringthe mechanical properties of the heart [1]. SCG-signals areobtained by measuring vibrations on the chest wall, usingan accelerometer placed on the sternum [2]. The morphol-ogy of the SCG-signal changes in relation to respiration,and understanding these changes is needed to correctly di-agnose diseases [3]. A negative pressure under inspirationresults in an increase in preload and increased right ven-tricle systolic volume, while the left ventricle end diastolicvolume and left ventricle systolic volume decreases, andvice versa during expiration[3]. Current solutions for mea-suring mechanical properties of the heart include: echocar-diography (Echo), computed tomography (CT), magneticresonance imaging (MRI) and catheter coronary angiog-raphy (CCA). A common denominator for these methodsis that they are time-consuming and expensive, but theiradvantage is their diagnostic precision [4–7]. SCG couldtherefore contribute as a cheaper and less time-consumingalternative. In this present effort, the relationship betweenrespiration and fiducial points in the SCG-signal is mod-eled in an attempt to investigate the relationship betweenrespiration and the SCG-signal.2. MethodsRespiration affects the time between fiducial points andthe length of cardiac cycles [8], and amplitudes of thesealso change during respiration [3]. The fiducial points dur-ing the systolic complex decrease in amplitude during in-spiration and the fiducial points in the diastolic complexincrease during inspiration and vice versa during expira-tion [3].2.1. Feature selectionThe features selected for prediction of respiration dur-ing cardiac cycles are listed in Table 1. The interpreta-tion and labeling of SCG-signals used in this paper is illus-trated in Figure 1. Timings and amplitudes were includedas features since these changes during respiration [3, 8].The power, mean- and median frequencies of the signalbetween MC and MO are also used as features, since thefrequencies change as a result of respiration [3, 9].Computing in Cardiology 2018; Vol 45 Page 1 ISSN: 2325-887X DOI: 10.22489/CinC.2018.203AOACMOMCIMTimeAccelerationAnnotation of SCG-morphologyFigure 1. The recording of a dorso-ventral axis SCG-signal, on the Xiphoid Process of a healthy subject. Thesignal is annotated with: MC (mitral clossure), IM (isovo-lumic moment), AO (aortic opening), AC (aortic clossure)and MO (mitral opening).Amplitude of Timing between MC and FrequencyMC IM Mean FreqIM AO Median FreqAO AC PowerAC MOMOTable 1. Overview of the selected features for each car-diac cycle, which include five amplitudes, four timings,and three frequency measures.2.2. Subject dataThe SCG-, electrocardiography (ECG)-, nasal spirome-ter signals were collected from 18 healthy subjects (age:29± 6 years, height: 171± 8 cm and weight: 69± 12 kg).Data acquisition was performed in the Aerospace Physiol-ogy lab in the Department of Biomedical Physiology andKinesiology. The experimental protocol was approved bythe research ethics board of Simon Fraser University as aminimum risk. All participants signed the informed con-sent form before any experimentation.2.3. Data acquisition and pre-processingSCG was recorded on the Xiphoid Process using an ac-celerometer, respiration flow signals were collected usinga nasal-catheter and the ECG was acquired in a lead IIconfiguration using Lifepak8 system (Medtronic Inc, MN,USA). All signals were obtained using NI 9205 analog in-put module (National Instruments Inc, TX, USA) at a sam-pling rate of 1000 Hz. An algorithm was implemented inMATLAB to locate the fiducial points of the SCG-signalautomatically. The implemented algorithm was inspiredby the work of Jafari et al. [10]. The SCG-signal was fil-tered with low-pass and high-pass filters with cutoff fre-quencies at 10 and 60 Hz, respectively. Filter frequen-cies were based on the performance of the peak findingalgorithm since the algorithm performed better when find-ing fiducial points in higher frequency bands. The algo-rithm found MC, IM, and AO by using local peak detectionwithin windows, which were based on the timing of theR-peak from the ECG-signal. When these fiducial pointswere found, the AC and MO were found by using searchwindows which started 300 ms after AO. The annotationof all data was manually verified to ensure correct annota-tion. Periods of apnea and unrecognisable fiducial pointswere discarded if the respiration or the SCG-signal wasdistorted. Only cardiac cycles with all five fiducial pointsincluded were used in the data analysis. Out of 12.000,6.010 cardiac cycles and their corresponding respirationmeasurements remained, and were normalised using theminimum and maximum values for each subject to makethe samples comparable across different subjects.2.4. ModelsThree methods were proposed; multiple regression anal-ysis (MRA), support vector regression (SVR) and neuralnetwork (NN). These methods were chosen from the per-spective of starting with simple models moving towardsmore complex models. The data used for the models wasdivided into training and test sets, of 80% and 20% respec-tively. Cross-validation with 10 folds was used to validatethe models.The MRA model was applied by using the least squaremethod. No hyperparameters were tuned.Hyperparameters for the SVR models were found usingBayesian optimisation. Two separate SVR models weretrained for predicting the measured respiration amplitudesat MC and AO. For both models, an rbf kernel was used,and the models were applied with an epsilon value of 0.03.The MC model had a C-value of 32.51 and a γ-value of12.71. The AO model had a C-value of 29.07 and a γ-value of 12.07.A dense neural network was implemented in Python(Keras 2.0 - TensorFlow backend). Several hyperparam-eters were found by doing a hyperparameter search, whichadjusted the number of units per layer, the number of hid-den layers, optimisers, learning rates, etc. The networkconsists of one input layer, two hidden layers with 128units using ReLu, two dropout layers with a 40% dropoutrate between the two hidden layers. The last layer con-sisted of two nodes with linear activation functions. Amean squared loss function was used. The model was op-timised using an Adam optimiser. The learning rate waslowered 80% when the loss started to plateau.Page 23. ResultsThe measure of linear correlation between actual andpredicted respiration was assessed using the Pearson cor-relation coefficient and sum of squared errors of predic-tion (SSE). Table 2 shows the mean-values of the cross-validation for all models. According to Table 2, theNN model scored the highest r values (0.908 and 0.904),whereas the SVR model scored lower r values (0.842 and0.833). The linear MRA model scored the lowest r-values(0.438 and 0.450). An example of the predictions is illus-trated in Figure 2. The standard deviations were similar forMC and AO in the cross-validation for SVR and NN, butdifferent for MRA. The SSE for the predicted MC and AOrespiration states can be seen in Table 2.(a) MC-timepointsr mean* SD* SSEMRA 0.438 0.477 0.047 51.21SVR 0.842 0.826 0.030 18.95NN 0.908 0.916 0.006 11.71(b) AO-timepointsr mean* SD* SSEMRA 0.450 0.480 0.043 51.48SVR 0.833 0.838 0.030 19.76NN 0.904 0.910 0.006 12.05Table 2. The table contains (a) mitral closure and (b) aor-tic opening results from validation and tests of the MRA,SVR, and NN models, r is the Pearson correlation coeffi-cients of the test set. Mean r-values from k-folding, thestandard deviation (SD) and SSE scores obtained by thedifferent models. *Validation results.4. DiscussionThe main finding of this study is that respiration ampli-tudes can be accurately predicted using cardiac related fea-tures from an SCG-signal. The predicted was most accu-rate in the NN model with high correlation between actualand predicted respiration amplitudes with r-values of 0.904and 0.908, for the MC and AO time points respectively.Respiration values where not equally distributed when ac-cessing the data. This is because the inspiration and ex-piration phases are very short, which means that most ofthe cardiac cycles reside in an inspirated or expirated state.Figure 2 shows that it was possible to predict the respira-tory amplitudes for a cardiac cycle located in the inspira-tion phase. This is clear since the NN predictions are veryclose to the actual respiratory amplitudes at the MC andAO fiducial points.The NN model scored the highest Pearsons correlationcoefficient (r = 0.908), which means there was an almostlinear relationship between the predicted respiration andthe actual respiration. This suggests that the relationshipbetween SCG features and respiration is not entirely linearsince the MRA model scored much lower r-values than thenon-linear models. The low SSE of the NN model indi-cates that the errors, when predicting the respiration, arerelatively small compared to the other models. The simi-lar r-value and mean-value of the NN model, with a smallstandard deviation, indicates that the model is the most ac-curate of the three. The accuracies of the three modelswere evaluated by cross-validating to investigate how wellthey generalised.There was a slight increase in accuracy when predictingthe respiration amplitude for the MC fiducial point com-pared to AO fiducial points for all models. It was not pos-sible to find an explanation for this in the data nor from aphysiological point of view.Normalised accelerationNormalised nasal flowFigure 2. Segment of a normalized respiration signal and the corresponding SCG-signal for a single subject. In the topfigure, the predicted values on the respiration signal from the Multiple Regression Analysis, Support Vector Regression,and Neural Network models. The SCG-signal is annotated with the fiducial points of MC and AO respectively.Page 35. LimitationsThe validity of the features can be questioned, especiallythose that were related to the annotated cardiac fiducialpoints. The annotations of the fiducial points were veri-fied manually, without clinical expertise and with ECG asa reference, as opposed to echocardiography, which is thegold standard. Another limitation was the data size anddistribution. Few samples were located on the ascendingand descending slopes of the respiration, while the bulk ofdata samples were in the peak and valley plateaus. The re-sults of the models could be optimistic since there mighthave been a leak in information between training and testdata. Training data not similar to the test data would givea more realistic prediction. The order of cardiac cycleswas randomised and subsequently divided into training-and test set. This approach may have caused informationto leak from the training sets into the test set and therebylead to a more optimistic result. A validation method to ac-commodate that issue could be ”Leave One Subject Out”cross-validation. This method was not used due to timeconstraints.6. ConclusionThe models based on the proposed methods showed theability to predict the respiratory amplitudes with respectsto MC and AO. The non-linear, NN and SVR, modelsproving the most accurate approaches and it was found thatthe SCG-signal does contain information regarding res-piration. Predicting the respiration from the SCG-signalwith these models, makes it possible to distinguish the res-piratory states and perform segmentation of SCG-signalsbased on respiration.References[1] Inan OT, Migeotte PF, Park KS, Etemadi M, Tavako-lian K, Casanella R, Zanetti J, Tank J, Funtova I,Prisk GK, Di Rienzo M. Ballistocardiography andSeismocardiography: A Review of Recent Advances.IEEE 2015;19(4):1414–1427.[2] Tavakolian K. Characterization and Analysis of Seis-mocardiogram for Estimation of Hemodynamic Pa-rameters. Doctoral dissertation 2010;215.[3] Amit G, Shukha K, Gavriely N, Intrator N. Respi-ratory modulation of heart sound morphology. AJP2009;296(3):H796–H805.[4] Wick CA, Su JJ, McClellan JH, Brand O, Bhatti PT,Buice AL, Stillman AE, Tang X, Tridandapani S. Asystem for seismocardiography-based identificationof quiescent heart phases: Implications for cardiacimaging. IEEE 2012;16(5):869–877.[5] Nikolaou K, Alkadhi H, Bamberg F, Leschka S, Win-tersperger BJ. MRI and CT in the diagnosis of coro-nary artery disease: indications and applications. I2011;2(1):9–24.[6] Mant J, Al-Mohammad A, Davis M. CHRONICHEART FAILURE: National National clinical guide-line for diagnosis and management in primary andsecondary care. NCGC 2010;108(108):1–119.[7] Mertens L, Friedberg MK. The gold standardfor noninvasive imaging in congenital heart dis-ease: echocardiography. Curr Opin Cardiol 2009;24(2):119–24.[8] Inan OT, Pandia K, Giovangrandi L, Zamanian RT,Kovacs GTA. A preliminary study investigating thequantification of beat-to-beat in seismocardiogramsignals. EMBS 2013;7286–7289.[9] Zakeri V, Akhbardeh A, Alamdari N, Fazel-RezaiR, Paukkunen M, Tavakolian K. Analyzing seismo-cardiogram cycles to identify the respiratory phases.IEEE 2017;64(8):1786–1792.[10] Jafari Tadi M, Koivisto T, Pänkäälä M, Paasio A,Knuutila T, Teräs M, Hänninen P. A new algorithmfor segmentation of cardiac quiescent phases and car-diac time intervals using seismocardiography. vol-ume 9443. mar 2015; 94432K.Address for correspondence:Samuel SchmidtBiomedical Engineering and Informatics / Aalborg UniversitySamuel Schimdt / 9000 / Denmarktel.: +45 9940 7456sschmidt@hst.aau.dkPage 4

Determining the Respiratory State From a Seismocardiographic Signal:A Machine Learning Approach

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Determining the Respiratory State From a Seismocardiographic Signal:A Machine Learning Approach

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