Continuous and Noninvasive Estimation of Right Ventricle Systolic Blood Pressure Using Heart Sound Signal by Deep Bidirectional LSTM Network

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Article Continuous and Noninvasive Estimation of Right Ventricle Systolic Blood Pressure Using Heart Sound Signal by Deep Bidirectional LSTM Network

Miao Wang, Hong Tang * , Tengfei Feng and Binbin Guo

School of Biomedical Engineering, Dalian University of Technology, Dalian 116024, China; [email protected] (M.W.); [email protected] (T.F.); [email protected] (B.G.) * Correspondence: [email protected]

Received: 11 July 2020; Accepted: 5 August 2020; Published: 7 August 2020

Abstract: Objective: Timely monitoring right ventricular systolic blood pressure (RVSBP) is helpful in the early detection of pulmonary hypertension (PH). However, it is not easy to monitor RVSBP directly. The objective of this paper is to develop a deep learning technique for RVSBP noninvasive estimation using heart sound (HS) signals supported by (electrocardiography) ECG signals without complex features extraction. Methods: Five beagle dog subjects were used. The medicine U-44069 was injected into the subjects to induce a wide range of RVSBP variation. The blood pressure in right ventricle, ECG of lead I and HS signals were recorded simultaneously. Thirty-two records were collected. The relations between RVSBP and cyclic HS signals were modeled by the Bidirectional Long Short-Term Memory (Bi-LSTM) network. Results: The mean absolute error (MAE) standard ± deviation (SD) inside record was 1.85 1.82 mmHg. It was 4.37 2.49 mmHg across record but ± ± within subject. The corrective factors were added after training the Bi-LSTM network across subjects. Finally, the MAE SD from 12.46 6.56 mmHg dropped to 6.37 4.90 mmHg across subjects. ± ± ± Signiﬁcance: Our work was the ﬁrst to apply the Bi-LSTM network to build relations between the HS signal and RVSBP. This work suggested a noninvasive and continuous RVSBP estimation using the HS signal supported by the ECG signal by deep learning architecture without the need of healthcare professionals.

Keywords: right ventricular systolic blood pressure; heart sound signal; bidirectional LSTM (Bi-LSTM) network; corrective factors; detection of pulmonary hypertension

1. Introduction Information about the role of the right ventricle (RV) in health and disease historically had fallen behind that of the left ventricle (LV) [1,2]. Comparatively, little attention had been paid on how RV dysfunction may be detected and measured efficiently. RV function may be impaired in pulmonary hypertension (PH), congenital heart disease, and coronary artery disease and in patients with left-sided heart failure or valvular heart disease [3–6]. Previous studies had shown that the potential estimated value of right ventricular blood pressure (RVBP) for patients with right heart failure was needed [7]. Right ventricular systolic blood pressure (RVSBP) is a critical and indispensable indicator for cardiovascular diseases, especially for early discovery and postoperative observation of PH. In the early disease, the symptoms of PH were often non-specific but progressed over time to functionally limiting dyspnea and fatigue. The non-specific symptoms in the early disease were some of the obstacles to establishing an early diagnosis [8]. Although the natural history varied according to the etiology of the condition, PH was usually a progressive disease characterized by increased

Appl. Sci. 2020, 10, 5466; doi:10.3390/app10165466 www.mdpi.com/journal/applsci Appl. Sci. 2020, 10, 5466 2 of 17 pulmonary vascular resistance and diminished RV function due to increased RV afterload. The normal RV is a thin-walled flow generator able to accommodate large changes in venous return but unable to maintain flow output when a fast increase in pulmonary artery pressure (PAP). Therefore, RVBP increased with the development of PH. In this paper, the authors extracted the RVBP instead of PAP. As far as the author’s knowledge, there were no literatures about the prediction of RVBP. The authors compared the prediction of RVSBP with the prediction of PAP. Right heart catheterization, an invasive procedure, is usually mandatory to confirm the diagnosis of right ventricular disease [9]. However, the financial expense and physical risk of right heart catheterization discourages patients from diagnosis and treatment. Moreover, the right heart catheterization is placed into the heart through a catheter. It is not fit to monitor RVBP or PAP over a long period of time. In clinical practice, there are two possible solutions to diagnose RV diseases non-invasively: Doppler echocardiography and phonocardiography [10]. Doppler echocardiography evaluates the speed of tricuspid regurgitation to calculate PAP [11]. However, some recent studies had suggested that Doppler echocardiographic may frequently be inaccurate [12]. Usually, patients’ diagnosis began with a detailed medical history collection and series of physical examinations in hospital. Systematic chest auscultation was crucial for early screening. Heart sound (HS) auscultation was widely used in routine examinations as a noninvasive and low-cost method. In earlier years, researchers found there were relations between the A2–P2 time split interval (TSI), which was further used to estimate PAP based on signal processing of the second heart sounds (S2s) [13–17]. However, the error of the TSI detection affected PAP estimation directly. This limitation prevented the accuracy of PAP estimation. Subsequently, researchers extracted feature sets from the heart sounds and used machine learning algorithms to setup predictive models for PAP estimation [18–20]. Features could be extracted from a wide variety of signal transforms, such as short time Fourier transform (STFT), wavelet transform, S-transform, etc. However, feature extraction was a challenging task. These handpicked features had limitations because they were selected based on a human being’s subjective analysis. Dennis and Smith, et al., built the models on human data. The standard deviation (SD) of the best general model were, respectively, 11.7 mmHg and 8.28 mmHg for PAP estimation using heart sounds [18,19]. As technology advances, new approaches need to be tried. The problem is still open. Deep learning had shown strong ability in modelling nonlinear relations from input to output [21]. The authors selected the improved version of recurrent neural network (RNN): Bidirectional Long Short-Term Memory (Bi-LSTM), which was used to model the relations from heart sound signal to RVSBP, automatically. It is known heart sounds are the acoustic vibrations generated by the interaction between heart hemodynamics and heart valves, chamber walls, and great vessels [22,23]. People know little about the complex relations, such as a black box, even though the relations had been noticed for many years. Medical researchers had shown that PAP influenced the characteristics of heart sounds. This suggested that heart sound analysis was a potential method for the noninvasive diagnosis of right ventricular disease [18]. In this paper, the Bi-LSTM network was used to model the black box to link heart sound signal to RVSBP directly. They were validated by experimental data collected from five beagle dog subjects.

2. Methods

2.1. Data Collection The experiments were carried out with five healthy adult beagles weighing 10–13 kg. The subjects were anesthetized with pentobarbital sodium (30 mg/Kg) firstly, then laid down in the supine position for surgical operations. A catheter (Swan-Ganz F7, Edwards, Irvine, CA, USA) filled with a heparinized solution (500 units/mL) was inserted into the RV via the jugular vein. A calibrated catheter and high-fidelity blood pressure transducer (MLTO699, ADInstruments, Sydney, Australia) was used to record the intraventricular blood pressure signal. U-44069 (40 µg/mL) was injected into the upper limb vein of the subjects by a path formed by an intravenous infusion with an uncontrolled flow speed. Appl. Sci. 2020, 10, x FOR PEER REVIEW 3 of 16

Appl. Sci. 2020, 10, 5466 3 of 17 flow speed. This path remained open during the signal recording so that a new dose of U-44069 could be injected. At the same time, a microphone transducer (MLT201, ADInstruments, Sydney, Australia) wasThis used path remainedto record openexternal during HS the signals signal at recording the apex so thatof the a new heart. dose The of U-44069RVBP, couldHS signal, be injected. and (electrocardiographyAt the same time, a microphone) ECG signal transducer of lead I (MLT201, were simultaneously ADInstruments, digitalized Sydney, at Australia) samplingwas frequency used to 1record KHz (PL3508, external HSPowerLab signals at 8/35, the apex ADInstruments, of the heart. TheSydney, RVBP, Australia) HS signal, with and (electrocardiography) a 16-bit resolution. The ECG microphone,signal of lead blood I were pressure simultaneously transducer, digitalized ECG electrical at sampling node,frequency and associated 1 KHz electrical (PL3508, lines PowerLab were kept 8/35, inADInstruments, fixed positions. Sydney, The experiment Australia)al with protocol a 16-bit was resolution. approved The by microphone,the ethics committee blood pressure of the transducer,School of BiomedicalECG electrical Engineering, node, and Dalian associated University electrical of Technology. lines were kept in fixed positions. The experimental protocolThe schematic was approved diagram by and the experimental ethics committee procedur of thee of School the measurement of Biomedical system Engineering, were shown Dalian in FigureUniversity 1. The of experimental Technology. procedure for each subject was divided into three stages. In the first stage, the signalsThe schematic were collected diagram about and 60 experimentals prior to the U-44069 procedure injection. of the measurementIn the second systemstage, U-44069 were shown was injectedin Figure into1. Thethe experimentalvein to induce procedure vasoconstriction for each via subject activation was divided of the prostaglandin-endoperoxide into three stages. In the first receptor.stage, the The signals RVBP were rose collected rapidly aboutto a high 60 s priorlevel. toIn the the U-44069 third stage, injection. the medicine In the second injection stage, stopped. U-44069 Thewas RVBP injected decreased into the gradually vein to induce and returned vasoconstriction to norm viaal status. activation This of process the prostaglandin-endoperoxide was repeated 3–10 times forreceptor. a subject. The In RVBPtotal, 32 rose records rapidly were to aobtained high level. from In the the five third subjects. stage, Every the medicine subject was injection injected stopped. with theThe same RVBP dose decreased of the medicine gradually to and induce returned the elev to normalation of status.right ventricular This process blood was pressure. repeated However, 3–10 times eachfor asubject subject. absorbed In total, 32and records metabolized were obtained the medicine’s from the ability five subjects. differently, Every which subject lead was to the injected duration with ofthe stage same 2 doseand stage of the 3 medicineto be diffe torent. induce We thecalled elevation this phenomenon of right ventricular the individual blood pressure. difference However, in this paper.each subject In each absorbed trail, the andsignal metabolized analysis was the performed medicine’s prior ability (stage differently, 1), during which (stage lead 2), to and the after duration (stage of 3)stage acute 2 andpulmonary stage 3 toartery be di stenosis.fferent. We The called details this of phenomenon the data were the displayed individual in diTablefference 1, where in this the paper. 32 trailsIn each included trail, the two signal records analysis which was only performed had the norm prioral (stageRVBP 1),(before during U44069 (stage injection), 2), and after such (stage as #1 3) andacute #25. pulmonary There were artery little stenosis. RVBP changes The details in the of reco therd data #1 wereand #25. displayed Other records in Table had1, where a wide the of 32 RVBP trails changes.included The two minimum records which of RVSB onlyP had was the 11.24 normal mmHg RVBP and (before the maximum U44069 injection), was 86.37 such mmHg. as #1 andIt was #25. shownThere werethat the little models RVBP were changes evaluated in the record in such #1 wi andde #25.range Other of RVSBP. records Figure had a 2 wide showed of RVBP an example changes. whichThe minimum gave typical of RVSBP cardiac was cycles 11.24 of mmHg the synchronou and the maximumsly collected was signals. 86.37 mmHg. This example It was shownwas from that the the #7models record were of Table evaluated 1. in such wide range of RVSBP. Figure2 showed an example which gave typical cardiac cycles of the synchronously collected signals. This example was from the #7 record of Table1.

RVBP (a) RVSBP

Data Collection， Analysis ECGECG Storage

HSHSs

(b) Repeat U-44069U44069 U-44069U-44069 start injectionejection metabolismmetabolism

Stage 1 Stage 2 Stage 3

ECG, HSs, and RVSBP signals were collected synchronously in 12 – 71 mins ECG, HS and RVBP signals were collected文本 synchronously in 12-71 minutes.

Figure 1. Schematic diagram of the measurement system and experimental procedure; (a) experiment setup; (b) experimental procedure. ECG: Electrocardiography; HS: Heart Sound; RVBP: Right Ventricular Blood Pressure. Appl. Sci. 2020, 10, x FOR PEER REVIEW 4 of 16

Figure 1. Schematic diagram of the measurement system and experimental procedure; (a) Appl. Sci.experiment2020, 10, 5466 setup; (b) experimental procedure. ECG: Electrocardiography; HS: Heart 4 of 17 Sound; RVBP: Right Ventricular Blood Pressure.

(a) 1

0 ECG [mv] -1 0~1.5 100~101.2 150~151.2 260.1~261.2 500~501.6 1040.2~1041.8 (b) 1

0 HS [mv] HS -1 0~1.5 100~101.2 150~151.2 260.1~261.2 500~501.6 1040.2~1041.8 (c) 60 25 0 RVBP [mmHg]RVBP 0~1.5 100~101.2 150~151.2 260.1~261.2 500~501.6 1040.2~1041.8 Stage 1 Time in Seconds FigureFigure 2. 2.An An example example of synchronouslyof synchronously collected collected signals sign in aals record. in a Onlyrecord. typical Only segments typical weresegments shown here;were ( ashown) were thehere; ECG (a) signals;were the (b )ECG were signals; the heart (b sound) were (HS)the heart signals; sound (c) were (HS) the signals; right ventricular (c) were bloodthe right pressure ventricular (RVBP) blood signals. pressure (RVBP) signals. Table 1. Summary of the data. Table 1. Summary of the data. Subject No. Record Time Length (s) Num. of Cardiac Cycles RVSBP Range (mmHg) Subject No. Record Time Length (s) Num. of Cardiac Cycles RVSBP Range (mmHg) No. 1 (12.5No. 1 kg) (12.5 kg) #1 #1 1177.451177.45 955955 23.07–24.5423.07–24.54 #2 1763.37 1193 22.38–45.55 #2 #3 1763.372202.64 14331193 22.04–48.0922.38–45.55 #3 #4 2202.641028.04 8781433 23.14–57.4722.04–48.09 #4 #5 1028.04910.35 861878 23.68–56.3423.14–57.47 2214.81 #5 #6 910.35 1965861 11.24–50.9623.68–56.34 #7 1637.72 1176 21.14–55.73 #6 #8 2214.812084.92 13011965 21.46–54.1211.24–50.96 #7 #9 1637.722202.44 13811176 21.58–53.4721.14–55.73 #8 #10 2084.921144.53 9271301 22.00–54.4821.46–54.12 #11 1541.69 1163 21.65–50.65 #9 2202.44 1381 21.58–53.47 No. 2 (11 kg)#10 #12 1144.531128.16 914927 24.80–61.3122.00–54.48 #13 1844.88 1266 32.91–56.82 #11 #14 1541.692204.93 17821163 29.95–57.0621.65–50.65 No. 2 (11 kg) #12 #15 1128.163315.28 2271914 29.81–76.0024.80–61.31 #13 #16 1844.882698.97 20551266 31.11–68.6632.91–56.82 #17 4039.60 2719 22.95–61.70 #14 2204.93 1782 29.95–57.06 #18 3445.08 2307 27.56–65.42 #15 #19 3315.282282.87 20192271 27.60–53.3629.81–76.00 No. 3 (12.5 kg)#16 #20 2698.971180.79 9512055 35.59–45.8131.11–68.66 #17 #21 4039.603457.12 23232719 43.48–75.1322.95–61.70 #18 #22 3445.083523.08 25292307 32.42–86.3727.56–65.42 #19 #23 2282.871362.07 9882019 41.81–58.1927.60–53.36 #24 1169.0 939 42.08–69.02 No. 3 (12.5 kg) #20 1180.79 951 35.59–45.81 No. 4 (10.2 kg) #25 524.42 449 14.57–19.30 #21 3457.12 2323 43.48–75.13 #26 1508.84 1161 18.56–44.57 #22 #27 3523.081099.17 7522529 20.43–44.6032.42–86.37 #23 #28 1362.07520.34 355988 20.83–44.8841.81–58.19 #24 #29 1169.01432.89 1140939 15.72–48.7142.08–69.02 No. 5 (13 kg) #30 914.75 646 21.82–47.91 #31 1345.00 967 22.09–49.24 #32 841.89 515 22.60–51.17 Appl. Sci. 2020, 10, x FOR PEER REVIEW 5 of 16

No. 4 (10.2 kg) #25 524.42 449 14.57–19.30 #26 1508.84 1161 18.56–44.57 #27 1099.17 752 20.43–44.60 #28 520.34 355 20.83–44.88 #29 1432.89 1140 15.72–48.71 No. 5 (13 kg) #30 914.75 646 21.82–47.91 #31 1345.00 967 22.09–49.24 Appl. Sci. 2020, 10, 5466 5 of 17 #32 841.89 515 22.60–51.17

2.2.2.2. Data Preprocessing Preprocessing The HS signals were were filtered filtered with with a azero-delay zero-delay third third order order butter-worth butter-worth bandpass bandpass filter filter in the in the rangerange of 20–200 Hz Hz to to remove remove nois noisee outside outside the the frequency frequency band band of ofheart heart sounds. sounds. The The R-waves R-waves of ECG of ECG signalssignals were recognized recognized by by the the Pan Pan Tomkins Tomkins algorith algorithmm [24]. [24 In]. Inthis this paper, paper, the the ECG ECG signal signal was was used used asas aa referencereference for HS signal signal segmentation. segmentation. It Itwas was essential essential for for this this research. research. Heart Heart sounds sounds recording recording withwith ECG provide insights insights into into the the electrical–mecha electrical–mechanicalnical activity activity of ofthe the heart heart in an in anunsupervised, unsupervised, non-invasivenon-invasive and inexpensive inexpensive manner. manner. The The first first hear heartt sound sound (S1) (S1) occurs occurs immediately immediately after after the theR-peak R-peak (ventricular depolarization) of the ECG. The interval between the current R-peak and next R-peak (ventricular depolarization) of the ECG. The interval between the current R-peak and next R-peak was called the cardiac cycle. The R-wave of each cardiac cycle indicated the fiducial time of each was called the cardiac cycle. The R-wave of each cardiac cycle indicated the fiducial time of each cardiac cycle of the simultaneous HS signal. The heart sound signals were segmented into consecutive cardiac cycle of the simultaneous HS signal. The heart sound signals were segmented into consecutive cardiac cycles based on the R-wave positions. Each cardiac cycle of ECG signal corresponded the HS cardiac cycles based on the R-wave positions. Each cardiac cycle of ECG signal corresponded the HS signal which included S1 and the second heart sound (S2), sometimes included the third heart sounds signal which included S1 and the second heart sound (S2), sometimes included the third heart sounds (S3) and the fourth heart sound (S4). It normalized the heart sound signals of all cycles to the same (S3)time-scaling and the of fourth one second. heart sound The RV (S4).SBP Itvalues normalized were detected the heart by soundthe maximum signals of the all cyclesRVBP signal to the in same time-scalingeach cardiac cycle. of one The second. stretched The cyclic RVSBP heart values sound were signals detected were used by the as maximuminputs to the of model the RVBP and the signal incorresponding each cardiac RVSBP cycle. Thevalues stretched were considered cyclic heart as soundthe outputs. signals The were process used was as inputs shown to in the Figure model 3. and theOutliers corresponding detection and RVSBP replacement values were for RVSBP considered values as were the outputs. sometimes The needed process because was shown of the inmotion Figure 3. Outliersartifacts or detection man-made and interference replacement [25]. for RVSBP values were sometimes needed because of the motion artifacts or man-made interference [25].

1st cycle 2nd cycle 3rd cycle ECG signal ...

inputs ... HS signal ...

35.37 mmHg 36.26 mmHg 37.89 mmHg 35.37 36.26 outputs RVBP signal ... 37.89 ...

(a) (b)

FigureFigure 3. Constructed 3. Constructed the the inputs inputs and and outputs outputs of ofthe the model; model; (a ()a segmentation) segmentation of of heart heart sound sound signal signal basedbased on on the the R-wave R-wave positions. positions. The The pink circlescirclesindicated indicated R-waves. R-waves. The The red red diamonds diamonds indicated indicated right rightventricular ventricular systolic systolic blood blood pressure pressure (RVSBP) values;(RVSBP) (b )values; inputs and(b) inputs outputs and to the outputs model. to the model.

2.3.2.3. Establishing the the Computation Computation Model Model The Long Long Short-Term MemoryMemory (LSTM) (LSTM) unitsunits were first first proposed proposed by by Hochreiter Hochreiter S et S al. et [26], al. [ 26], basedbased on recurrent neural neural networks networks.. The The gating gating mechanism mechanism was was used used to solve to solve thethe problem problem of gradient of gradient vanishing.vanishing. Subsequent Subsequently,ly, due due to to their their good good capability capability for for sequence sequence memory, memory, LSTM LSTM units units were were widely widely used in the field of natural language processing. An LSTM maintains a memory based on history information, which enables the model to predict the current output conditioned on long distance features. Figure4 illustrated a single LSTM memory cell. The LSTM memory cell was implemented as the following. The LSTM cell, mainly consisted of the forget gate, input gate, and output gate. Via these gate functions, the LSTM network can effectively capture complicated features within time series in both short and long terms.

it = σ(Wxixt + Whiht 1 + Wcict 1 + bi) (1) − − ft = σ Wx f xt + Wh f ht 1 + Wc f ct 1 + b f (2) − − Appl. Sci. 2020, 10, 5466 6 of 17

ct = ftct 1 + ittanh(Wxcxt + Whcht 1 + bc) (3) − − ot = σ(Wxoxt + Whoht 1 + Wcoct 1 + bo) (4) − − ht = ottanh(ct) (5) Appl. Sci. 2020, 10, x FOR PEER REVIEW 6 of 16 1 σ(x) = (6) 1 + e x used in the field of natural language processing. An LSTM− maintains a memory based on history ex e x information, which enables the model totanh predict(x) = the− current− output conditioned on long distance(7) ex + e x features. Figure 4 illustrated a single LSTM memory cell.− The LSTM memory cell was implemented where σ is the logistic sigmoid function, and i, f , o and c are the input gate, forget gate, output gate as the following. The LSTM cell, mainly consisted of the forget gate, input gate, and output gate. Via and cell vectors, all of which are the same size as the hidden vector. The weight matrix subscripts these gate functions, the LSTM network can effectively capture complicated features within time have the meaning as the name suggests. For example, Whi is the hidden-input gate matrix, Wxo is the series in both short and long terms. input-output gate matrix. The weight matrices from the cell to gate vectors (e.g., Wci) are diagonal.

x t x t

input gate output gate i t o t

x × × t Ct h t

f t forget gate

x t Figure 4. A Long Short-Term Memory Cell. Figure 4. A Long Short-Term Memory Cell. In this paper, we applied Bi-LSTM [27] to sequence prediction. Bi-LSTM networks were based on the basic LSTM units. The Bi-LSTM model includes forward and backward LSTM channels, i = σ(W x +W h − +W c − +b ) (1) which simultaneously capturedt past andxi futuret information.hi t 1 ci Itt 1 can extracti deep and robust features regarding prediction tasks of time= series. The+ bidirectional+ LSTM+ network was showed as Figure5. ft σ(Wxf xt Whf ht−1 Wcf ct−1 b f ) (2) The input layer represents input signals and the output layer represents output tags. The inputs and outputs corresponded to the HS signals and the RVSBP values, respectively, as shown in the c = f c − +i tanh(W x +W h − +b ) (3) purple dash line. The purple gradientt t t boxes1 t with roundedxc t cornershc t 1 indicatedc the LSTM memory cells. The information was processed in both directions with two different hidden layers and fed forward o = σ(W x +W h − +W c − +b ) (4) to an output layer. We can efficientlyt makexo t use ofho thet 1 past inputsco t 1 (viao forward states) and the future inputs (via backward states) for a specific time= frame. ht ottanh(ct ) (5) 1 σ ()x = (6) 1+ e− x ex − e− x tanh(x) = (7) ex + e−x where σ is the logistic sigmoid function, and i, f ,o and c are the input gate, forget gate, output gate and cell vectors, all of which are the same size as the hidden vector. The weight matrix subscripts have the meaning as the name suggests. For example, Whi is the hidden-input gate matrix, Wxo is the input-output gate matrix. The weight matrices from the cell to gate vectors (e.g., Wci ) are diagonal. In this paper, we applied Bi-LSTM [27] to sequence prediction. Bi-LSTM networks were based on the basic LSTM units. The Bi-LSTM model includes forward and backward LSTM channels, which simultaneously captured past and future information. It can extract deep and robust features regarding prediction tasks of time series. The bidirectional LSTM network was showed as Figure 5. The input layer represents input signals and the output layer represents output tags. The inputs and outputs corresponded to the HS signals and the RVSBP values, respectively, as shown in the purple Appl. Sci. 2020, 10, x FOR PEER REVIEW 7 of 16

dash line. The purple gradient boxes with rounded corners indicated the LSTM memory cells. The information was processed in both directions with two different hidden layers and fed forward to an output layer. We can efficiently make use of the past inputs (via forward states) and the future inputs Appl. Sci. 2020, 10, 5466 7 of 17 (via backward states) for a specific time frame.

34.2 mmHg 47.5 mmHg 45.1 mmHg RVSBP values

output layer

forward

backward

input layer

HS signals

FigureFigure 5. A 5. BidirectionalA Bidirectional Long Long Short-Term Short-Term Memory (BI-LSTM) (BI-LSTM) network.network.

FigureFigure6 6 was was the the proposed proposed signal signal processing processing framework framework by by the the authors. authors. This This model model was was based based on supervisedon supervised learning. learning. The The heart heart sound sound recordings recordings were were segmented segmented into into consecutive consecutive cardiac cardiac cycles cycles based onbased the R-waveon the R-wave peak locations; peak locations; at the same at the time same the ti HSme signals the HS were signals uniﬁed were into unified the lengthinto the of length one second of time-scaled.one second Intime-scaled. the training In stage,the training the Bi-LSTM stage, modelthe Bi-LSTM was trained model to constructwas trained the to relations construct from the the inputsrelations (the from cyclic the heart inputs sound (the cyclic signals) heart to sound the outputs signals) (RVSBP to the values).outputs (RVSBP In the testing values). stage, In the the testing trained modelstage, the was trained to estimate model RVSBP was to values estimate with RVSBP new inputs.values with Figure new6 illustrated inputs. Figure a RVSBP 6 illustrated estimation a RVSBP system inestimation which each system RVSBP in valuewhich was each tagged RVSBP with value diﬀ erentwas tagged measured with values. different For measured example, invalues. the training For set,example, the Figure in the6 showed training the set, HS the signals Figure were 6 showed tagged asthe 24.2 HS mmHg, signals 58.4were mmHg, tagged ...as ,24.2 24.7 mmHg, mmHg. 58.4 In the testingmmHg, set, …, the 24.7 new mmHg. input HSIn the signals testing were set, tagged the new as the input estimated HS signals results: were 34.2 tagged mmHg, as 47.5the estimated mmHg, ... , 45results: mmHg. 34.2 The mmHg, input 47.5 layers mmHg, had the…, same45 mmHg. dimension The input with layers the HS had signal the size.same Thedimension output with layers the had theHS samesignal dimension size. The output with the layers size had of RVSBP. the same It meantdimension when with the the data size was of inputtedRVSBP. It into meant the when model, the the sizedata of was the inputted HS signals into were the model,1000 Nthe, wheresize ofN themeant HS signals the number were 1000 of cardiac× N , cycles where in N each meant HS record the × andnumber 1000 of meant cardiac the cycles uniform in each length HS of record each HSand signal. 1000 meant For the the output uniform layers, length it alsoof each must HS have signal. the For same dimensionthe output withlayers, the it sizealso must of the have RVSBP. the Insame this dimension paper, the with size the of RVSBP size of the was RVSBP.1 N, whereIn this Npaper,meant the the Appl. Sci. 2020, 10, x FOR PEER REVIEW × 8 of 16 numbersize of RVSBP of cardiac was cycles1× N in, where each RVSBPN meant record, the andnumber 1 meant of cardiac the signal cycles was in each one RVSBP dimension. record, and 1 meant the signal was one dimension. Train

Cyclic HSs Inputs for train . . LSTM /Bi-LSTM 24.2 mmHg network 58.4 mmHg RVSBP Targets for train . . . . 24.7 mmHg

Test

34.2 mmHg LSTM Estimated Cyclic HSs RVSBP 47.5 mmHg /Bi-LSTM Outputs Inputs for test ...... network . 45.1 mmHg

FigureFigure 6. 6. FrameworkFramework of the proposed technique.

2.4. Training Procedure In this study, the model contained two bidirectional LSTMs and was briefly called TBLSTM. The TBLSTM networks can automatically select features similar to convolutional neural networks. The TBLSTM model was comprehensively evaluated by three schemes to validate the performance of the proposed technique. The three schemes are defined in the following.

2.4.1. Evaluation Scheme I: Cross Validation Inside Record Scheme I was designed to evaluate the generalization ability of the model inside the record. We evaluated the prediction performance using a five-fold cross validation. A five-fold cross validation was performed by randomly dividing the dataset into equal-sized five partitions. A single partition was used as the validation data for testing the model, and the remaining four partitions were used as training data. This process was repeated five times. Each partition was used once as the validation data. The advantage of five-fold cross validation was that all observations were used for training and testing, and each observation was used for validation once. Parameters were set for the TBLSTM model as follows: the two Bi-LSTM hidden layers both had 80 neurons. The max Epochs was 128, the min Batch Size was 64 and the initial learn rate was 0.01. Cross Entropy was employed to compute the loss function and stochastic gradient descent algorithm was imported to minimize the loss cost in training. The parameters of this group were the best choose based on computer simulations to the authors’ knowledge.

2.4.2. Evaluation Scheme II: Cross Validation Across Records but within Subject Scheme II was designed to evaluate the generalization ability across records but within a subject. For example, eleven records were collected from No. 1 subject. The model was trained by ten of these records, and the other was used to test the model. We repeated the process eleven times until each in the subject had experienced a test. For the other subjects, the process was the same as the No.1 subject. Parameters setting for the TBLSTM model were same as those in scheme I.

2.4. Training Procedure In this study, the model contained two bidirectional LSTMs and was brieﬂy called TBLSTM. The TBLSTM networks can automatically select features similar to convolutional neural networks. The TBLSTM model was comprehensively evaluated by three schemes to validate the performance of the proposed technique. The three schemes are deﬁned in the following.

2.4.1. Evaluation Scheme I: Cross Validation Inside Record Scheme I was designed to evaluate the generalization ability of the model inside the record. We evaluated the prediction performance using a five-fold cross validation. A five-fold cross validation was performed by randomly dividing the dataset into equal-sized five partitions. A single partition was used as the validation data for testing the model, and the remaining four partitions were used as training data. This process was repeated five times. Each partition was used once as the validation data. The advantage of five-fold cross validation was that all observations were used for training and testing, and each observation was used for validation once. Parameters were set for the TBLSTM model as follows: the two Bi-LSTM hidden layers both had 80 neurons. The max Epochs was 128, the min Batch Size was 64 and the initial learn rate was 0.01. Cross Entropy was employed to compute the loss function and stochastic gradient descent algorithm was imported to minimize the loss cost in training. The parameters of this group were the best choose based on computer simulations to the authors’ knowledge.

2.4.3. Evaluation Scheme III: Cross Validation Across Subjects Scheme III was designed to evaluate the generalization ability across subjects. That was, separate the subjects into two non-overlapping groups. One group contained four subjects. All the records of this group were used to train the model. The other group contained the other subject. This subject was used to test the model. We repeated the group separation until each subject experienced testing. The parameters set for the TBLSTM model were the same as those in the Scheme I. In addition, in order to solve the problem of individual differences among different subjects, after the TBLSTM model was trained, corrective factors between individuals were proposed. Firstly, the measured RVSBP subtract the estimated RVSBP, then each subject’s errors were recorded. We used one group’s features as corrective factors. The group contained the first heart sound’s maximum amplitude of each cardiac cycle (S1_max) and the second heart sound’s maximum amplitude of each cardiac cycle (S2_max). Features functions were obtained by fitting every subject’s errors with the binary linear function. The fitting formulas were as follows:

errors = a S1_max + b S2_max + c (8) · · RVSBP = estimate_RVSBP + errors (9) where a, b, c represented features combinations’ coefficient factors. Coefficients were fitted automaticly. estimate_RVSBP was the estimated result by the TBLSTM model in the scheme III. Finally, the predicted results were the TBLSTM model’s predicted outcomes (estimate_RVSBP) added the fitting outcomes (errors) in Scheme III. Appl. Sci. 2020, 10, 5466 9 of 17

2.5. Performance Indicators Three indicators were deﬁned to evaluate the performance of the models in the three schemes, which were the mean absolute error (MAE) and the standard deviation (SD).

XN 1 MAE = Pi P∗ (10) N − i i=1

N 1 X ME = (P P∗) (11) N i − i i=1 v tu N 1 X 2 SD = P P ME (12) N 1 i − i∗ − − i=1 where P was the estimated RVSBP value, P* was the measured RVSBP value, and N was the total number of the cardiac cycles in consideration.

3. Results and Discussions The experiment was studied based on ﬁve subjects. There were 32 trials in all. The three schemes’ results were as follows:

3.1. Outcomes of Scheme I In scheme I, a five-fold cross validation method was used in each record. So, there were five outcomes in every record in the scheme I. To facilitate marking, we used indices to mark the five parts. Table2 showed the outcomes of the fivefold cross validation. When indices was 1, the mean MAE SD equaled 1.79 1.73 mmHg; when indices was 2, the mean MAE SD equaled ± ± ± 1.79 1.76 mmHg; when indices was 3, the mean MAE SD equaled 1.91 1.84 mmHg; when indices ± ± ± was 4, the mean MAE SD equaled 1.81 1.79 mmHg; whenindices was 5, the mean MAE SD ± ± ± equaled 1.99 2.00 mmHg. The scheme I’s mean MAE SD was 1.85 1.82 mmHg. In the scheme I ± ± ± both MAE and SD were low.

3.2. Outcomes of Scheme II In scheme II, the model was evaluated within subject. The TBLSTM model’s performance of the ﬁve subjects was shown in Figure7. Where the blue solid curve was the measured RVSBP values, and the red solid curve was the estimated RVSBP values. It was seen that the estimated RVSBP followed the measured RVSBP very well. The MAEs and the SDs were also listed in Table2 for further analysis. The outcomes showed that although the features were not extracted by hand, we used the Bi-LSTM model which can track changes of the RVSBP with low MAE and SD. Hence, RVSBP can be accurately predicted even when not using the HS features. Appl. Sci. 2020, 10, x FOR PEER REVIEW 10 of 16

In scheme II, the model was evaluated within subject. The TBLSTM model’s performance of the five subjects was shown in Figure 7. Where the blue solid curve was the measured RVSBP values, and the red solid curve was the estimated RVSBP values. It was seen that the estimated RVSBP followed the measured RVSBP very well. The MAEs and the SDs were also listed in Table 2 for further analysis. The outcomes showed that although the features were not extracted by hand, we used the Bi-LSTM model which can track changes of the RVSBP with low MAE and SD. Hence, RVSBP can be accuratelyAppl. Sci. predicted2020, 10, 5466 even when not using the HS features. 10 of 17

#1#1 MAE MAE=2.15 = 2.15 SD=1.68SD = 1.68 #2#2 MAEMAE=1.66 = 1.66 SD=1.62SD = 1.62 #3#3 MAE MAE=2.07 = 2.07 SDSD=1.89 = 1.89 #4#4 MAE MAE=2.38 = 2.38 SD=2.35SD = 2.35 35 50 50 70

15 20 20 20 0 955 0 1193 0 1433 0 878 #5#5 MAE MAE=4.90 = 4.90 SD=3.88SD = 3.88 #6#6 MAE MAE=6.24 = 6.24 SD=4.71SD = 4.71 #7#7 MAE MAE=1.54 = 1.54 SD=2.22SD = 2.22 #8#8 MAE MAE=1.95 = 1.95 SD=2.04SD = 2.04 60 60 60 60

20 5 20 20 0 861 0 1965 0 1176 0 1301 #9#9 MAE MAE=2.02 = 2.02 SD=1.58SD = 1.58 #10#10 MAE MAE=6.53 = 6.53 SDSD=5.08 = 5.08 #11#11 MAE MAE=1.24 = 1.24 SDSD=0.90 = 0.90 #12#12 MAE MAE=8.22 = 8.22 SDSD=5.06 = 5.06 60 60 60 70

20 20 20 20 0 1381 0 927 0 1163 0 914 #13#13 MAE MAE=5.52 = 5.52 SDSD=3.73 = 3.73 #14#14 MAE MAE=4.86 = 4.86 SD=3.69SD = 3.69 #15#15 MAE MAE=3.93 = 3.93 SD=4.80SD = 4.80 #16#16 MAE MAE=4.66 = 4.66 SDSD=3.05 = 3.05 70 70 80 75

30 20 25 25 0 1266 0 1782 0 2271 0 2055 #17#17 MAE MAE=5.49 = 5.49 SDSD=3.93 = 3.93 #18#18 MAE MAE=4.69 = 4.69 SDSD=4.87 = 4.87 #19#19 MAE MAE=4.87 = 4.87 SD=4.09SD = 4.09 #20#20 MAE MAE=4.47 = 4.47 SDSD=2.86 = 2.86 70 70 70 57

20 20 20 33 0 2719 0 2307 0 2019 0 951 #21#21 MAE MAE=6.59 = 6.59 SDSD=4.44 = 4.44 #22#22 MAE MAE=5.31 = 5.31 SD=4.18SD = 4.18 #23#23 MAE MAE=8.01 = 8.01 SD=4.75SD = 4.75 #24#24 MAE MAE=7.20 = 7.20 SDSD=5.55 = 5.55 80 95 70 80

40 35 35 35 0 2323 0 2529 0 988 0 939 #25#25 MAE MAE=5.48 = 5.48 SDSD=3.67 = 3.67 #26#26 MAE MAE=7.01 = 7.01 SDSD=3.87 = 3.87 #27#27 MAE MAE=4.65 = 4.65 SDSD=3.12 = 3.12 #28#28 MAE MAE=3.31 = 3.31 SDSD=2.35 = 2.35 40 60 50 50

20 5 20 20 0 449 0 1161 0 752 0 355 #29#29 MAE MAE=4.66 = 4.66 SDSD=3.21 = 3.21 #30#30 MAE MAE=2.15 = 2.15 SDSD=1.92 = 1.92 #31#31 MAE MAE=3.96 = 3.96 SD=2.42SD = 2.42 #32#32 MAE MAE=2.76 = 2.76 SDSD=2.16 = 2.16 50 52 60 60

18 20 20 20 0 1140 0 646 0 967 0 515 Cycles (Num) Cycles (Num) Cycles (Num) Cycles (Num)

Figure 7. Performance of scheme II. Figure 7. Performance of scheme II. 3.3. Outcomes of Scheme III 3.3. OutcomesWe had of Scheme validated III that the performances of the scheme I and scheme II were good. Both of the WeMAE had and validated SD inside that trials the and performances across trials were of the low. scheme The objective I and ofscheme the scheme II were III wasgood. to evaluateBoth of the the TBLSTM model’s generalization ability among different subjects. The MAE and SD were given in MAE and SD inside trials and across trials were low. The objective of the scheme III was to evaluate column “Scheme III Bi-LSTM” in Table2. The “corrections” way showed corrective factors were added the TBLSTM model’s generalization ability among different subjects. The MAE and SD were given in after training the TBLSTM model. The final results of the scheme III were calculated as the formula in column(8)–(9). “Scheme The results III Bi-LSTM” of the “corrections” in Table way 2. The was showed“corrections” in column way “scheme showed III corrections”corrective infactors Table 2were. addedFigure after8 wastraining the five the subjects’ TBLSTM estimated model. result. The Thefinal blue results solid of curve the wasscheme the measured III were RVSBP. calculated The red as the formulasolid in curve (8)–(9). was theThe TBLSTM results model’sof the estimated“corrections” RVSBP. way The pinkwas solidshowed curve in was column the outcomes “scheme of III corrections”adding thein Table corrective 2. Figure factors. 8 was the five subjects’ estimated result. The blue solid curve was the measured RVSBP. The red solid curve was the TBLSTM model’s estimated RVSBP. The pink solid curve was the outcomes of adding the corrective factors. Appl. Sci. 2020, 10, 5466 11 of 17 Appl. Sci. 2020, 10, x FOR PEER REVIEW 11 of 16

Figure 8. Performance of scheme III. Figure 8. Performance of scheme III. 3.4. Summary of the Evaluation 3.4. Summary of the Evaluation Table2 showed the MAE and SD of the three schemes. The color coding gave a good overview of theTable model’s 2 showed performance. the MAE The and white SD of color the meantthree sche lowermes. errors The than color the coding pink color. gave Thea good average overview of ofMAE the model’s and SD wereperformance. calculated The in thewhite last color row in meant Table 2lower, where errors there than are the the results pink color. of MAE1 The to average MAE5 of MAEand and SD1 SD to SD5 were for calculated scheme I, thein the results last ofrow MAE6 in Tabl ande SD62, where for scheme there II,are the the results results of of MAE7 MAE1 to MAE8,to MAE5 andand SD1 SD7 to to SD5 SD8 for for scheme scheme III.I, the The results average of ofMAE6 MAE and and SD6 SD were for scheme 1.85 mmHg II, the and results 1.82 mmHg of MAE7 for to MAE8,scheme and I, andSD7 4.37to SD8 mmHg for scheme and 2.49 III. mmHg The averag for schemee of MAE II. The and MAE SD were and SD1.85 became mmHg a and little 1.82 great mmHg in for“scheme scheme III I, Bi-LSTM”,and 4.37 mmHg because and diﬀ erent2.49 mmHg subjects for had scheme individual II. The diﬀ MAEerences. and However, SD became the correctivea little great infactors “scheme were III added Bi-LSTM”, after the because “scheme different III Bi-LSTM”. subjec Bothts had of theindividual MAE and differences. SD dropped However, in “scheme the III corrections”. The MAE SD from 12.46 6.56 mmHg downed to 6.37 4.90 mmHg. Figure9a–c corrective factors were added± after the “scheme± III Bi-LSTM”. Both of the± MAE and SD dropped in “schemeshowed III the corrections”. box plots of theThe three MAE schemes, ± SD from where 12 the.46 high± 6.56 MAE mmHg and SDdowned values to were 6.37 shown ± 4.90 by mmHg. red Figurecrosses 9a,b “+ ”.and We c canshowed see the the errors box plots were controlledof the three below schemes, 3 mmHg where in the the scheme high MAE I, 5 mmHg and SD in values the werescheme shown II, andby red 7 mmHg crosses after “+”. addingWe can the see corrective the errors factors were controlled in the scheme below III. 3 Generally mmHg in speaking, the scheme I, the5 mmHg three schemes in the scheme showed II, that and the 7 proposed mmHg methodafter adding was a the convenient corrective method factors to estimatein the scheme RVSBP III. non-invasively. The model was suitable for estimating RVSBP even in the situation of very high and Generally speaking, the three schemes showed that the proposed method was a convenient method very low RVSBP values. The method proved that the model had stable generalization ability within to estimate RVSBP non-invasively. The model was suitable for estimating RVSBP even in the situation trials, across trials, and across subjects, with the addition of correction factors. Overall, continuous of very high and very low RVSBP values. The method proved that the model had stable RVSBP tracking based on HS signals, no matter short or long sequences, using the proposed method generalization ability within trials, across trials, and across subjects, with the addition of correction could get good results. factors. Overall, continuous RVSBP tracking based on HS signals, no matter short or long sequences, using the proposed method could get good results.

Table 2. The estimation performances of Bi-LSTM model for three schemes.

Scheme I Scheme II Scheme III No. Subjec Indices = 1 Indices = 2 Indices = 3 Indices = 4 Indices = 5 LSTM Bi-LSTM corrections Recor t MA MAE SD MAE SD MAE SD MAE SD MAE SD MAE SD MAE SD d SD1 E 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 No. 1 #1 0.21 0.21 0.24 0.19 0.25 0.16 0.19 0.13 0.28 0.15 2.15 1.68 #2 1.01 1.06 0.92 0.99 0.69 0.72 0.70 0.79 1.40 1.20 1.66 1.62 #3 0.88 1.23 1.05 0.91 0.98 1.22 1.14 0.96 0.89 1.10 2.07 1.89 #4 1.26 1.81 1.00 1.18 1.20 1.53 0.95 1.13 1.06 1.38 2.39 2.35 #5 1.39 1.08 1.45 2.08 2.23 2.58 1.21 1.12 1.79 2.11 4.86 3.75 #6 1.11 1.19 1.02 1.02 1.06 1.45 1.16 1.88 1.23 1.73 6.21 4.67 11.77 5.88 5.85 4.83 #7 1.53 1.23 1.18 1.43 1.73 1.71 1.36 1.73 1.58 1.11 1.53 2.22 #8 1.12 1.15 1.74 1.05 2.05 1.24 1.42 1.20 1.42 1.97 1.94 2.01 #9 1.20 1.13 1.25 1.57 1.23 1.38 1.53 2.12 1.16 1.00 2.01 1.55 #10 2.36 1.44 1.76 2.52 1.82 1.89 1.71 2.03 1.93 1.25 6.47 5.07 #11 1.26 1.74 0.88 0.91 1.21 1.09 1.42 1.05 0.62 0.55 1.24 0.90 No. 2 #12 4.00 4.67 3.58 3.66 4.20 3.35 3.43 2.87 3.32 2.64 8.23 5.05 #13 4.00 2.80 3.28 2.28 3.24 2.52 3.45 2.62 3.90 2.77 5.50 3.73 #14 2.50 2.31 2.80 2.38 2.63 2.25 2.77 2.15 2.77 2.27 4.86 3.69 #15 1.78 1.75 2.05 3.01 2.38 3.28 2.77 2.83 2.86 6.78 3.81 3.85 9.80 7.37 8.94 6.93 #16 1.26 1.08 1.47 1.32 1.55 1.31 1.15 1.15 1.66 1.32 4.65 3.05 #17 0.89 1.11 0.93 0.98 1.12 1.20 1.01 1.09 1.28 1.29 5.48 3.93 #18 1.40 1.78 1.32 1.57 1.22 1.40 1.40 1.69 1.37 1.66 4.66 4.88 Appl. Sci. 2020, 10, 5466 12 of 17

Table 2. The estimation performances of Bi-LSTM model for three schemes.

Scheme I Scheme II Scheme III Subject No. Record Indices = 1 Indices = 2 Indices = 3 Indices = 4 Indices = 5 LSTM Bi-LSTM Corrections MAE 1 SD1 MAE2 SD2 MAE3 SD3 MAE4 SD4 MAE5 SD5 MAE6 SD6 MAE7 SD7 MAE8 SD8 No. 1 #1 0.21 0.21 0.24 0.19 0.25 0.16 0.19 0.13 0.28 0.15 2.15 1.68 #2 1.01 1.06 0.92 0.99 0.69 0.72 0.70 0.79 1.40 1.20 1.66 1.62 #3 0.88 1.23 1.05 0.91 0.98 1.22 1.14 0.96 0.89 1.10 2.07 1.89 #4 1.26 1.81 1.00 1.18 1.20 1.53 0.95 1.13 1.06 1.38 2.39 2.35 #5 1.39 1.08 1.45 2.08 2.23 2.58 1.21 1.12 1.79 2.11 4.86 3.75 #6 1.11 1.19 1.02 1.02 1.06 1.45 1.16 1.88 1.23 1.73 6.21 4.67 11.77 5.88 5.85 4.83 #7 1.53 1.23 1.18 1.43 1.73 1.71 1.36 1.73 1.58 1.11 1.53 2.22 #8 1.12 1.15 1.74 1.05 2.05 1.24 1.42 1.20 1.42 1.97 1.94 2.01 #9 1.20 1.13 1.25 1.57 1.23 1.38 1.53 2.12 1.16 1.00 2.01 1.55 #10 2.36 1.44 1.76 2.52 1.82 1.89 1.71 2.03 1.93 1.25 6.47 5.07 #11 1.26 1.74 0.88 0.91 1.21 1.09 1.42 1.05 0.62 0.55 1.24 0.90 No. 2 #12 4.00 4.67 3.58 3.66 4.20 3.35 3.43 2.87 3.32 2.64 8.23 5.05 #13 4.00 2.80 3.28 2.28 3.24 2.52 3.45 2.62 3.90 2.77 5.50 3.73 #14 2.50 2.31 2.80 2.38 2.63 2.25 2.77 2.15 2.77 2.27 4.86 3.69 #15 1.78 1.75 2.05 3.01 2.38 3.28 2.77 2.83 2.86 6.78 3.81 3.85 #16 1.26 1.08 1.47 1.32 1.55 1.31 1.15 1.15 1.66 1.32 4.65 3.05 9.80 7.37 8.94 6.93 #17 0.89 1.11 0.93 0.98 1.12 1.20 1.01 1.09 1.28 1.29 5.48 3.93 #18 1.40 1.78 1.32 1.57 1.22 1.40 1.40 1.69 1.37 1.66 4.66 4.88 #19 1.44 1.30 1.37 1.13 1.37 1.30 1.69 1.79 1.35 1.27 4.85 4.08 No. 3 #20 0.80 0.87 1.67 1.58 0.84 1.13 1.01 1.64 0.71 0.55 4.47 2.86 #21 0.61 0.62 0.72 0.68 0.79 0.57 0.98 1.08 0.81 0.84 6.59 4.44 #22 1.15 1.40 1.23 1.25 1.16 1.61 1.07 1.20 1.31 1.52 5.30 4.16 24.95 8.97 6.39 5.22 #23 0.62 0.48 0.67 0.57 0.59 0.45 0.71 0.62 0.78 0.59 8.01 4.75 #24 1.57 1.61 1.91 1.55 2.25 3.18 1.93 2.11 2.01 2.43 7.20 5.55 No. 4 #25 0.94 1.14 0.88 1.09 0.69 0.47 0.89 1.82 0.89 1.05 5.48 3.67 #26 2.07 2.31 1.96 2.41 2.42 2.95 2.27 1.97 2.15 4.16 7.00 3.87 #27 2.60 2.44 2.12 2.08 2.28 1.86 2.46 2.05 2.66 2.42 4.65 3.12 9.68 6.65 5.38 3.81 #28 4.84 5.06 4.50 3.58 5.56 5.04 4.79 4.41 7.02 5.39 3.25 2.35 #29 2.56 2.01 2.92 2.36 2.82 1.98 2.68 2.38 3.18 2.09 4.66 3.21 No. 5 #30 3.57 2.67 3.48 2.38 2.62 2.48 2.81 2.24 2.96 3.07 2.09 1.70 #31 2.61 2.24 3.33 4.27 2.72 1.85 2.64 2.53 2.74 3.06 3.89 2.24 6.09 3.93 5.29 3.73 #32 2.59 2.34 2.58 2.21 4.09 3.87 3.08 2.73 4.55 3.26 2.69 2.00 mean 1.79 1.73 1.79 1.76 1.91 1.84 1.81 1.79 1.99 2.00 4.37 2.94 12.46 6.56 6.37 4.90 Note: The depth of the color represented the degree of the error. The white color meant lower errors than the pink color. Appl. Sci. 2020, 10, x FOR PEER REVIEW 12 of 16

#19 1.44 1.30 1.37 1.13 1.37 1.30 1.69 1.79 1.35 1.27 4.85 4.08 No. 3 #20 0.80 0.87 1.67 1.58 0.84 1.13 1.01 1.64 0.71 0.55 4.47 2.86 #21 0.61 0.62 0.72 0.68 0.79 0.57 0.98 1.08 0.81 0.84 6.59 4.44 #22 1.15 1.40 1.23 1.25 1.16 1.61 1.07 1.20 1.31 1.52 5.30 4.16 24.95 8.97 6.39 5.22 #23 0.62 0.48 0.67 0.57 0.59 0.45 0.71 0.62 0.78 0.59 8.01 4.75 #24 1.57 1.61 1.91 1.55 2.25 3.18 1.93 2.11 2.01 2.43 7.20 5.55 No. 4 #25 0.94 1.14 0.88 1.09 0.69 0.47 0.89 1.82 0.89 1.05 5.48 3.67 #26 2.07 2.31 1.96 2.41 2.42 2.95 2.27 1.97 2.15 4.16 7.00 3.87 #27 2.60 2.44 2.12 2.08 2.28 1.86 2.46 2.05 2.66 2.42 4.65 3.12 9.68 6.65 5.38 3.81 #28 4.84 5.06 4.50 3.58 5.56 5.04 4.79 4.41 7.02 5.39 3.25 2.35 #29 2.56 2.01 2.92 2.36 2.82 1.98 2.68 2.38 3.18 2.09 4.66 3.21 No. 5 #30 3.57 2.67 3.48 2.38 2.62 2.48 2.81 2.24 2.96 3.07 2.09 1.70 #31 2.61 2.24 3.33 4.27 2.72 1.85 2.64 2.53 2.74 3.06 3.89 2.24 6.09 3.93 5.29 3.73 #32 2.59 2.34 2.58 2.21 4.09 3.87 3.08 2.73 4.55 3.26 2.69 2.00 mean 1.79 1.73 1.79 1.76 1.91 1.84 1.81 1.79 1.99 2.00 4.37 2.94 12.46 6.56 6.37 4.90 *Note: The depth of the color represented the degree of the error. The white color meant lower errors Appl. Sci. 2020, 10, 5466 13 of 17 than the pink color. Error (mmHg) Error Error (mmHg) Error Error (mmHg)

Figure 9. Box plots of the model performance. (a) MAE1-MAE5 and SD1-SD5 of scheme I, (b) MAE6 andFigure SD6 9. of Box scheme plots II,of (thec) MAE7-MAE9 model performance. and SD7-SD9 (a) MAE1-MAE5 of scheme III. and SD1-SD5 of scheme I, (b) MAE6 and SD6 of scheme II, (c) MAE7-MAE9 and SD7-SD9 of scheme III. 3.5. Discussions 3.5. DiscussionsPrevious studies had shown that PAPcan be estimated by heart sound features [28–33]. Stephen [28] proposedPrevious to use studies neural had network shown tothat estimate PAP can PAP be esti inmated 1999 firstly by heart and sound got standard features estimated[28–33]. Stephen error 1.9[28] mmHg. proposed The to erroruse neural was low. network However, to estimate it was PAP better in 1999 to use firstly cross-validation and got standard to test estimated their model. error After1.9 mmHg. that, Smith The error R [19 was] extracted low. However, 38 features it was on better diff erentto use domainscross-validation and used to test the their feature model. selection After methodthat, Smith to select R [19] good extracted features. 38 Theyfeatures used on cross differ validationent domains methods and andused got the a feature mean error selection of 8.3 mmHg,method whichto select may good be features. the best They performance used cross at validati that time,on methods to the authors’ and got knowledge.a mean error Researchersof 8.3 mmHg, [29 which–33] usuallymay be analyzed the best theperformance relations between at that HStime, signals’ to the time authors’ domain, knowledge. frequency Researchers domain, amplitude [29–33] domain, usually entropyanalyzed domain the relations features, between and PAP. HS Then, signals’ they ti usedme domain, machine frequency learning to domain, estimate amplitude the PAP. However, domain, theirentropy focuses domain were features, on the and feature’s PAP. analysis.Then, they At used the machine same time, learning the left to estimate ventricular the bloodPAP. However, pressure (LVBP)their focuses and aortic were pressure on the werefeature’s also researchedanalysis. At by th thee same investigators time, the [34 left–37 ].ventricular The way toblood predict pressure LVBP or(LVBP) aortic and pressure aortic was pressure similar were with thealso process researched of PAP by prediction. the investigators They always [34–37]. separated The way the to first predict and secondLVBP or heart aortic sounds, pressure known was assimilar S1 and with S2, fromthe proc theess directly of PAP measured prediction. heart They sound always signals. separated However, the precisefirst and automated second heart localization sounds, known of the S1as S1 and and S2 S2, or measurementfrom the directly of themeasured splitting heart interval sound between signals. theHowever, components precise of automated the S2 had localization proved diffi ofcult. the InS1 summary,and S2 or measurement it could be found of the that splitting the estimated interval performancebetween the wascomponents related toof the the extracted S2 had proved features diff directly.icult. In However, summary, feature it could extraction be found or that feature the selectionestimated was performance still a big problem. was related to the extracted features directly. However, feature extraction or featureThis selection paper suggested was still a a big deep problem. learning based on Bi-LSTM to estimate RVSBP using heart sound signalsThis without paper complexsuggested feature a deep extraction. learning based Bi-LSTM on Bi-LSTM is an indirection to estimate ally RVSBP connected using network heart sound with twosignals layers without of LSTM, complex it gives feature full considerationextraction. Bi-LSTM to the relationship is an indirection between ally theconnected current network data and with the datatwo beforelayers andof LSTM, after it.it gives Bi-LSTM full solvesconsideration the gradient to the disappearance relationship between or gradient the explosion current data problem and the in RNN,data before and overcomes and after it. the Bi-LSTM drawback solves that the LSTM gradient only disappearance considers the relationship or gradient explosion between the problem current in dataRNN, and and its overcomes previous data. the drawback In this paper, that weLSTM chose only Bi-LSTM considers rather the thanrelationship LSTM; webetween thought the that current the predictiondata and its of previous the time seriesdata. In not this only paper, needs we to ch considerose Bi-LSTM the previous rather than data LS butTM; also we the thought later data. that So,the weprediction chose the of Bi-LSTM. the time series To illustrate not only the needs performance to consider of the the Bi-LSTM previous network, data but we also performed the later multipledata. So, setswe chose of experiments the Bi-LSTM. in the To RVSBP illustrate estimation the performanc based one theof the HS Bi-LSTM signal. It network, was comprehensively we performed evaluated multiple by three schemes, i.e., inside trial (scheme I), across trials but within subject (scheme II), and across subjects (scheme III). Both scheme I and scheme II could get good estimated results. If we trained and tested the data between individuals directly, the effects were not good (such as “scheme III Bi-LSTM”), because of the individual differences between subjects. Individual difference means that when the basic conditions are the same, there will be significantly different reactions between individuals, such as high sensitivity reaction, low sensitivity reaction, and specific reaction. In this paper, the reasons for individual differences were extensive and complicated, mainly due to the absorption and metabolism of drugs being different for different subjects. The blood concentration of the same dose of drugs in a different individual is different, so that the intensity and duration of the drug action are very different for different individuals. In this paper, each individual was injected with the same dosage of the drug by the experiment operator. However, the subjects had different absorptions and metabolisms. Appl. Sci. 2020, 10, x FOR PEER REVIEW 13 of 16

sets of experiments in the RVSBP estimation based on the HS signal. It was comprehensively evaluated by three schemes, i.e., inside trial (scheme I), across trials but within subject (scheme II), and across subjects (scheme III). Both scheme I and scheme II could get good estimated results. If we trained and tested the data between individuals directly, the effects were not good (such as “scheme III Bi-LSTM”), because of the individual differences between subjects. Individual difference means that when the basic conditions are the same, there will be significantly different reactions between individuals, such as high sensitivity reaction, low sensitivity reaction, and specific reaction. In this paper, the reasons for individual differences were extensive and complicated, mainly due to the absorption and metabolism of drugs being different for different subjects. The blood concentration of the same dose of drugs in a different individual is different, so that the intensity and duration of the drug action are very different for different individuals. In this paper, each individual was injected Appl. Sci. 2020, 10, 5466 14 of 17 with the same dosage of the drug by the experiment operator. However, the subjects had different absorptions and metabolisms. In Figure 10, the histograms of RVSBP distribution between Inindividuals Figure 10 were, the histogramsshown to compare of RVSBP the distributionRVSBP differe betweennces across individuals subjects. wereThe dark shown blue to histogram compare therepresented RVSBP di RVSBPfferences distribution. across subjects. The red The curve dark repr blueesented histogram the fitted represented Gaussian RVSBP distribution distribution. curve Thebased red on curve the RVSBP represented distribution the fitted histogram. Gaussian The distribution fitting parameters curve based (μ and on the σ) RVSBPwere obtained. distribution The histogram.Figure 10 showed The fitting the RVSBP parameters value (µ distributionsand σ) were of obtained. No.1, No.4, The and Figure No.5 10 were showed roughly the RVSBPfrom 20 value to 50 distributionsmmHg. However, of No.1, the No.4, rang andof RVSBP No.5 were value roughly distributions from 20 of to No.2 50 mmHg. and No.3 However, were larger the rangthan ofthe RVSBP range valueof No.1, distributions No.4, and ofNo.5. No.2 Due and to No.3 the individual were larger di thanfferences, the range the authors of No.1, added No.4, andthe corrective No.5. Due factors to the individualto correct errors differences, between the individuals. authors added Finally, the correctivethe estimations factors of toRVSBP correct betw errorseen betweendifferent individuals.individuals Finally,got better the estimated estimations outcomes of RVSBP than between before in di ffschemeerent individuals III. As shown got in better Figure estimated 8, although outcomes the absolute than beforevalue between in scheme the III. measured As shown RVSBP in Figure and8, the although estimate thed absoluteRVSBP differed, value between the relative the measured value difference RVSBP andwas thesmall. estimated The trend RVSBP between diff ered,the estimated the relative RVSBP value and di thefference real RVSBP was small. was very The close. trend Such between as No.1, the estimatedNo.3, No.4, RVSBP and No.5 and thein Figure real RVSBP 8, the wastrends very of close. estimated Such RVSBP as No.1, were No.3, basically No.4, and consistent No.5 in Figurewith the8, thetrends trends of the of estimated measured RVSBP RVSBP. were Table basically 3 showed consistent the regression with the coefficient. trends of the Co measuredmbined the RVSBP. distribution Table3 showedof the RVSBP the regression data and coetheffi regressioncient. Combined coefficient the dist distributionribution of of the the feature’s RVSBP dataanalysis, and thewe regressionfound that coetheffi highercient distributionof the distribution of the feature’s of RVSBP analysis, values, we the found higher that of the higherweight ofof thethe distribution S1_max. This of finding RVSBP values,maybe theused higher as a trend of the to weight predict of RVSBP. the S1_max. In our Thiswork, finding the Bi-LSTM maybe usednetwork as a did trend not to require predict complex RVSBP. Infeature our work, extraction the Bi-LSTM techniques network and didwe could not require estimate complex the trend feature of RVSBP. extraction techniques and we could estimate the trend of RVSBP.

μ = 31.6 μ = 44.35 μ = 52.26 σ = 8.37 σ = 12.28 σ = 10.91

(No.1) RVSBP (mmHg) (No.2) RVSBP (mmHg) (No.3) RVSBP (mmHg)

μ = 27.78 μ = 32.58 σ = 7.43 σ = 7.26

(No.4) RVSBP (mmHg) (No.5) RVSBP (mmHg)

Figure 10. Histogram of RVSBP distribution. Figure 10. Histogram of RVSBP distribution. Table 3. Summary of regression coeﬃcients. Table 3. Summary of regression coefficients. Coeﬃcient: a,b,c Subject Subject aCoefficient: b a,b,c c 1 16.49 0.29 11.52 − − 2 30.42 0.70 4.36 − − 3 35.74 1.72 26.63 − 4 3.10 0.95 1.15 − − − 5 32.60 1.16 3.03 − −

Although our method could estimate the RVSBP non-invasively, we also discovered drawbacks from the wrong estimated samples. As shown in scheme II #20, #23, and #24 from Figure7, the three records were from the subject 3. At the same time, we could see the outcome of the subject 3 in the scheme III which outcome was the worst in five subjects. It is probably due to this sample’s range of RVSBP being large, so that the individual difference of the subject 3 was bigger than other subjects. Eventually, the outcome of the estimation of the subject 3 was worse than others. This problem may Appl. Sci. 2020, 10, 5466 15 of 17 be solved in two ways. On the one hand, the authors found the trends of the predicted results were good, but there was a constant between the measured RVSBP and the predicted RVSBP. If we could find the pathological mechanism or the relationship between the HS features and the RVSBP. Maybe the predicted results could compensate a constant and then the predicted results will be accurate. On the other hand, we need to add the number of the subjects. So that there were enough subjects, the model could learn and validate the relation fully. We need to reduce our gap between the different individuals and elevate the accuracy of the RVSBP forecasts in the future study. Besides, the RVSBP was influenced by breathing. So, the signal was slightly up and down, fluctuating during the process of collecting. Eventually the collected RVSBP signal was not smooth. In the next step, we need to try to solve these problems.

4. Conclusions The authors had proposed the Bi-LSTM model for continuous and noninvasive RVSBP estimation using HS signals supported by ECG signals. The model was able to learn the maps between HS signals and RVSBP. At the same time, RVSBP were estimated without complex features extraction. Three schemes were used to test and verify the Bi-LSTM model. The results indicated that the mean MAE and SD were usually not greater than 5 mmHg in scheme I and scheme II. The model even had good generalization ability across subjects in scheme III. This study suggested an easy operated technique to monitor RVSBP in real-time. It may have potential applications in early detection of right ventricular disease.

Author Contributions: Conceptualization, M.W. and H.T.; methodology, M.W and T.F.; software, M.W.; validation, M.W. and H.T., T.F. and B.G.; formal analysis, H.T.; investigation, M.W. and H.T.; resources, H.T.; data curation, H.T.; writing—original draft preparation, M.W. and H.T.; writing—review and editing, T.F. and B.G.; visualization, M.W. and H.T.; supervision, T.F. and B.G.; project administration, H.T.; funding acquisition, H.T. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported in part by the National Natural Science Foundation of China under Grant No. 61971089, No. 61471081. National Key R&D Program of the Ministry of Science and Technology of China, SQ2019YFC200094. Conﬂicts of Interest: The authors declare no conﬂict of interests.

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