sensors

Article Unobtrusive Estimation of Cardiac Contractility and Changes Using Ballistocardiogram Measurements on a High Bandwidth Force Plate

Hazar Ashouri *, Lara Orlandic and Omer T. Inan

School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; [email protected] (L.O.); [email protected] (O.T.I.) * Correspondence: [email protected]; Tel.: +1-404-385-1724

Academic Editors: Daniel Teichmann and Steffen Leonhardt Received: 27 February 2016; Accepted: 26 May 2016; Published: 28 May 2016

Abstract: Unobtrusive and inexpensive technologies for monitoring the cardiovascular health of failure (HF) patients outside the clinic can potentially improve their continuity of care by enabling therapies to be adjusted dynamically based on the changing needs of the patients. Specifically, cardiac contractility and stroke volume (SV) are two key aspects of cardiovascular health that change significantly for HF patients as their condition worsens, yet these parameters are typically measured only in hospital/clinical settings, or with implantable sensors. In this work, we demonstrate accurate measurement of cardiac contractility (based on pre-ejection period, PEP, timings) and SV changes in subjects using ballistocardiogram (BCG) signals detected via a high bandwidth force plate. The measurement is unobtrusive, as it simply requires the subject to stand still on the force plate while holding electrodes in the hands for simultaneous electrocardiogram (ECG) detection. Specifically, we aimed to assess whether the high bandwidth force plate can provide accuracy beyond what is achieved using modified weighing scales we have developed in prior studies, based on timing intervals, as well as signal-to-noise ratio (SNR) estimates. Our results indicate that the force plate BCG measurement provides more accurate timing information and allows for better estimation of PEP than the scale BCG (r2 = 0.85 vs. r2 = 0.81) during resting conditions. This correlation is stronger during recovery after exercise due to more significant changes in PEP (r2 = 0.92). The improvement in accuracy can be attributed to the wider bandwidth of the force plate. ∆SV (i.e., changes in stroke volume) estimations from the force plate BCG resulted in an average error percentage of 5.3% with a standard deviation of ˘4.2% across all subjects. Finally, SNR calculations showed slightly better SNR in the force plate measurements among all subjects but the small difference confirmed that SNR is limited by motion artifacts rather than instrumentation.

Keywords: (BCG); heart failure; unobtrusive cardiovascular monitoring; stroke volume; cardiac contractility

1. Introduction Cardiovascular diseases (CVD) are the leading cause of death in the United States, according to the American Heart Association. A particularly prevalent CVD is Heart Failure (HF), which affects 5.7 million Americans and causes one in nine national deaths [1]. HF is characterized by the heart’s inability to provide sufficient blood to the organs and tissues [2]. The annual health care costs of HF in the United States are $30.7 billion, most of which relate to hospitalization costs [3]. Readmission rates to the hospital after a discharge for HF patients are 25% within 30 days. Often, readmissions result from cardiovascular events, such as when ejection fraction is insufficient, and ventricular filling pressures are subsequently elevated; these elevated pressures are observed at least 6

Sensors 2016, 16, 787; doi:10.3390/s16060787 www.mdpi.com/journal/sensors Sensors 2016, 16, 787 2 of 11 days prior to the onset of symptoms such as shortness of breath or edema [4]. Studies have shown that out-of-clinic monitoring of HF patients can (1) reduce hospital and emergency room visits; (2) reduce healthcare costs associated with the disorder; and (3) lead to a decrease in mortality [5–7]. Although the CardioMEMS Implantable Hemodynamic Monitor (IHM) has recently received U.S. Food and Drug Administration (FDA) approval for this purpose of enabling home monitoring of HF patients, the cost per patient of implantation exceeds $25,000. Hence, providing a noninvasive, unobtrusive, and inexpensive alternative for outside-of-clinic monitoring of HF patients is imperative to diminishing the financial burden of the disease and improving the patients’ quality of life [8]. HF is associated with a weakened myocardium with impaired contractility. The time elapsed between the electrical depolarization of the ventricular muscle and the ensuing opening of the has been shown to be a surrogate for cardiac contractility that can be measured noninvasively as the interval between the electrocardiogram (ECG) Q-wave and the opening of the aortic valve [9]. Specifically, an increased pre-ejection period (PEP) is an indication of decreased contractility [10]. Moreover, because the heart cannot contract efficiently, stroke volume (SV), which is the volume of blood ejected by the left in one contraction, decreases in HF patients prior to exacerbation. Therefore, measuring a patient’s PEP and SV changes outside the clinic can provide insight into the severity of his/her condition and can potentially allow prediction of exacerbations, and physicians to intervene and amend medications to prevent such exacerbations and hospitalizations [11]. Currently, one reference standard noninvasive means of measuring PEP and SV is impedance cardiography (ICG) [12]. However, ICG is impractical in an out-of-clinic setting as it requires a trained medical professional to apply eight electrodes to the subject and operate the measuring device [13]. In order to empower HF patients to monitor their condition outside the clinic, there needs to be an affordable, unobtrusive method for measuring PEP and SV changes. Ballistocardiography (BCG), a measurement of the recoil forces of the body in response to the ejection of blood from the heart and movement of the blood through the vasculature [14,15] has been demonstrated as an alternative to ICG for measuring PEP [13]. In that prior study, BCG signals were measured using a modified electronic weighing scale, and we showed that this scale could be modeled as a second-order mechanical system. We characterized the spring constant and associated mechanical bandwidth for a range of bodyweights for typical adults (>15 Hz bandwidth for bodyweights of 150 kg or less) [16]. This mechanical bandwidth, while sufficient for not attenuating any of the frequency components of interest for BCG recordings, may potentially distort the BCG waveform and reduce the accuracy of timing interval measurements due to uneven group delay in the pass-band. Accordingly, in this paper, we compare the modified weighing scale to a high bandwidth, reference standard force plate to evaluate the accuracy in PEP estimation. We also estimate per subject ∆SV (i.e., relative changes in SV) using the force plate BCG and verify the accuracy against simultaneous ICG measurements. Finally, we provide signal-to-noise ratio (SNR) calculations of scale-based BCG and force plate BCG measurements to understand whether the higher resolution of the force plate is beneficial.

2. Methods and Materials

2.1. Protocol The study was conducted under a protocol reviewed and approved by the Georgia Institute of Technology Institutional Review Board (IRB). All subjects provided written consent before experimentation. Data were collected from 17 healthy subjects (Gender: 10 males, 7 females; Age: 23.6 ˘ 4.5 years, Height: 172.8 ˘ 9.9 cm, Weight: 70.7 ˘ 11.3 kg). Each subject was asked to stand still in an upright position for 60 s on each of the force plate and scale for baseline measurements. After that, each subject performed a stepping exercise for 60 s after which they were asked to stand on the scale as still as possible for 5 min to monitor their full recovery. Half of the subjects stood on the scale first during baseline measurement while the other half stood on the force plate first to account for any bias due to the order in which the subject stood on the force plate versus the scale. In addition to the Sensors 2016, 16, 787 3 of 11

BCG,Sensors we measured 2016, 16, 787 the ECG and ICG. The ECG was used as timing reference to ensemble3 of average 11 the BCG and ICG signals. The B-point of the ICG was used as a reference method for detection of the the BCG, we measured the ECG and ICG. The ECG was used as timing reference to ensemble opening of the aortic valve [17,18]; it was also used for reference ∆SV calculations. average the BCG and ICG signals. The B-point of the ICG was used as a reference method for 2.2. Hardwaredetection Designof the opening of the aortic valve [17,18]; it was also used for reference ∆SV calculations. Two2.2. Hardware different Design instruments were used to measure the BCG; a modified electronic weighing scale and a highTwo bandwidth different instruments multi-component were used force to measure plate. the The BCG; modified a modified weighing electronic scale weighing (BC534, scale Tanita, Tokyo,and Japan) a high was bandwidth developed multi in- previouscomponent work force using plate. an The analog modified amplifier weighing and ascale strain (BC534, gauge Tanita, bridge [16]. The multi-componentTokyo, Japan) was force developed plate (Type in previous 9260AA6, work Kistler using Instrument an analog Corp, amplifier NY, and USA) a strain has a bandwidth gauge in excessbridge of [16]. 200 HzThe andmulti sufficient-component resolution force plate to enable(Type 9260AA6, BCG measurements Kistler Instrument in all threeCorp, axes.NY, USA) The force platehas has a four, bandwidth three-component in excess of force200 Hz sensors. and sufficient Each of resolution the four sensorsto enable has BCG three measurements pairs of quartz in all plates, one sensitivethree axes. to The pressure force plate in the has z-direction four, three (head-to-foot)-component force and sensors. two to Each shear of in the the four xand sensors y-directions has three pairs of quartz plates, one sensitive to pressure in the z-direction (head-to-foot) and two to (dorsoventral and lateral respectively). Out of the 12 output signals, two of the shear forces that have shear in the x and y-directions (dorsoventral and lateral respectively). Out of the 12 output signals, the same line of action can be paralleled so the outputs of the force plate is eight signals instead of 12. two of the shear forces that have the same line of action can be paralleled so the outputs of the force Sinceplate we only is eight process signals head-to-foot instead of 12. BCG Since in we this only work, process we accessed head-to-foot each BCG of the in this 4 signals work, inwe the accessed z-direction separately,each of passedthe 4 signals each in one the of z- themdirection as anseparately, input to passed an amplifier each one and of them filter as circuit an input and to thenan amplifier the outputs of theseand fourfilter componentscircuit and then were the outputs added usingof these an four adder components circuit. Thewere summed,added using amplified an adder andcircuit. filtered head-to-footThe summed, BCG amplified signal was and outputted filtered head into-to the-foot data BCG acquisition signal was systemoutputted (MP150, into the BIOPAC data acquisition System Inc., Goleta,system CA, (MP150, USA). The BIOPAC ECG andSystem ICG Inc., signals Goleta, were CA measured, USA). The using ECG and BN-RSPEC ICG signals and were BN-NICO measured wireless modulesusing (BIOPAC BN-RSPEC Systems, and BN Inc.,-NICO Goleta, wireless CA, modules USA), respectively, (BIOPAC Systems, then transmitted Inc., Goleta, to CA,the MP150. USA), All signalsrespectively were sampled, then transmitted at 2 kHz. Figureto the MP1a,b150. shows All signals a block were diagram sampled of at the2 kHz. experimental Figure 1a,b setupshows anda a d block diagram ofd the experimental setup and a time trace of ECG, ICG, and head-to foot BCG. time trace of ECG, dt ICG, and head-to foot BCG. dt

d Figure 1. (a) A block diagram of the experimental setup; (b) a 5 s time trace showing ECG, ICG,d Figure 1. (a) A block diagram of the experimental setup; (b) a 5 s time trace showing ECG,dt dt ICG, head-to-foothead-to-foot force force plate plate BCG. BCG.

2.3. Data2.3. Data Processing Processing The BCG, ICG, and ECG signals were filtered with finite impulse response (FIR) Kaiser window The BCG, ICG, and ECG signals were filtered with finite impulse response (FIR) Kaiser window band-pass filters with cut-off frequencies as follows: 0.9–30 for ICG and ECG, 0.5–20 for head-to-foot band-pass filters with cut-off frequencies as follows: 0.9–30 for ICG and ECG, 0.5–20 for head-to-foot BCG. The ECG R-peaks were detected using an automated peak detection algorithm and verified BCG.manually. The ECG This R-peaks was done were by detectedcomputing using a constant an automated threshold of peak 50% detectionof the maximum algorithm amplitude and verified of manually.the absolute This was value done of bythe computingband-pass filtered a constant ECG threshold signal for ofeach 50% subject; of the local maximum maxima amplitude greater in of the absoluteamplitude value of than the this band-pass threshold filtered were ECG then signal located for automatically each subject; and local annotated maxima as greater R-waves. in amplitude The thanminimum this threshold R-R interval were then was locatedcalculated automatically for every subject and and annotated the detected as R-waves. R-peaks Thewere minimumused as a R-R intervalfiduciary was calculated to segmen fort ICG every and subjectBCG signals and theinto detected individual R-peaks heart beats were each used with as a a fiduciary window length to segment ICG andequal BCG to the signals minimum into individual R-R interval. heart These beats extracted each with heartbeats a window were length then averaged equal to the to obtain minimum R-R interval.ensemble Theseaveraged extracted traces with heartbeats reduced were noise. then averaged to obtain ensemble averaged traces with reduced noise. Sensors 2016, 16, 787 4 of 11 Sensors 2016, 16, 787 4 of 11 2.4. Feature Extraction and Statistical Analysis

2.4. FeatureThe I-wave Extraction in the and weighing Statistical scale Analysis and force plate BCG signals was obtained by detecting the minima before the global maxima in the first 200 ms portion of the corresponding BCG ensemble average.The The I-wave J-peak in the and weighing its amplitude scale and (from force the plate reference BCG threshol signalsd, was i.e. obtained, 0) were by detected detecting as the the globalminima maxima before the of globalthe force maxima plate in and the scale first 200 BCG ms ensemble portion of averages. the corresponding The B-point BCG of ensemble the first derivativeaverage. The of J-peakICG was and detected its amplitude as the (fromglobal the maxima reference of threshold,the secondi.e. derivative, 0) were detectedof the ICG as the[13]. global The Xmaxima-point was of the obtained force plate by detecting and scale the BCG minima ensemble after averages. the global The maxima B-point of of the the first first derivative derivative of of ICG. ICG Thewas detectedamplitude as ofthe the global maxima maxima of of the the first second derivative derivative of ICG of the was ICG also [13 ]. detected. The X-point Figure was 2 obtained shows by detecting the minima afterd the global maxima of the first derivative of ICG. The amplitude of the ensemble averages of ECG, ICG, scale BCG and head-to-foot force plate BCG signals with thde maxima of the first derivativedt of ICG was also detected. Figure2 shows ensemble averages of ECG, dt extractedICG, scale features. BCG and head-to-foot force plate BCG signals with the extracted features.

풅 Figure 2. Ensemble averaged traces of ECG,d ICG, scale BCG, and head-to-foot force plate BCG Figure 2. Ensemble averaged traces of ECG, dt 풅풕ICG, scale BCG, and head-to-foot force plate BCG with withthe characteristic the characteristic points points and features.and features.

2.4.1.2.4.1. Correlation Correlation at at Rest Rest TheThe 60 60 s baseline recording of of scale BCG, BCG, force plate head head-to-foot-to-foot BCG, BCG, and and ICG ICG were divided into 12 sub sub-ensembles-ensembles (5 (5 s s long) long) and and a a sub sub-ensemble-ensemble average average was was obtained obtained for for each each of of the the 12 12 sub sub-ensembles.-ensembles. ForFor each ofof thosethose sub-ensembles, sub-ensembles, the the RI RI duration duration was was calculated calculated (the (the timing timing at which at which the I-wave the I- occurswave occurssince the since ensemble the ensemble averaging averaging is done with is done respect with to the respect ECG R-peak)to the ECG and PEP R-peak) was calculated and PEP wa (thes calculatedtiming at which (the timing the B-point—opening at which the B- ofpoint the— aorticopening valve—occurs). of the aortic Although valve—occurs). PEP is defined Although as the PEP time is definedelapsed as between the time the elapsed Q-point between in the ECG the Q and-point the in Bpoint the ECG in the and ICG, the detecting Bpoint in the the Q-point ICG, detecting in the ECG the Qis- notpoint always in the asECG robust is not as always detecting as therobust R-point. as detecting Hence, t inhe ourR-point. analysis Hence, we usedin our the analysis R-peak we in used ECG theasa R reference-peak in ECG and estimatedas a reference PEP and as the estimated RB-interval PEP sinceas the we RB are-interval most interestedsince we are in relativemost interested changes inin relative PEP rather changes than absolutein PEP rather measures, than absolute and the QRmeasures, interval and is typically the QR interval consistent is typically from beat-to-beat consistent A fromcorrelation beat-to analysis-beat A correlation was then performed analysis was between then performed RI-intervals between in both RI scale-intervals and force in both plate scale and and the forcecorresponding plate and PEPthe corresponding values (from ICG) PEP amongvalues all(from 17 subjects.ICG) among all 17 subjects.

2.4.2.2.4.2. Correlation Correlation during during Exercise Exercise Recovery TheThe 5 5 min min after after exercise exercise recovery recovery recording recording of head-to-foot of head-to- forcefoot force plate BCG plate and BCG ICG and were ICG divided were dividedinto 15 subinto ensembles15 sub ensembles (20 s long). (20 s long) As compared. As compared to the to rest the data,rest data, a longer a longer time time was was required required for forextracting extracting the sub-ensemblethe sub-ensemble averages averages (20 s (20 compared s compared to 5 s) to due 5 s) to due increased to increased postural postural sway in sway exercise in exerciserecovery recovecomparedry compared to rest. A sub-ensemble to rest. A sub average-ensemble and RI-intervals average and and RI PEP-intervals were calculated and PEP for were each calculatedof the 15 sub-ensembles. for each of the A 15 correlation sub-ensembles. analysis A was correlation then performed analysis wasbetween then RI-intervals performed betweenand PEP RIamong-intervals all 17 and subjects. PEP among all 17 subjects.

2.5. Estimating Relative Changes in Stroke Volume 2.5. Estimating Relative Changes in Stroke Volume SV StrokeStroke volume volume 푆푉11 was calculated for each subject during rest using the reference ICG signal usingusing Sramek’s Sramek’s equation equation [19]: [19]:

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p0.17Hq3 d SV1 “ ¨ z ¨ LVET (1) 4.25Z dt 0 ˆ max ˙ where H is the height of the subject, Z0 is the base impedance of ICG, dz{dtmax is the amplitude of the global maxima of the first derivate of ICG, and LVET is the left ventricular ejection time which is calculated as the timing difference between X-point and B-point in the first derivative of ICG. Then, for each of the 17 subjects, separately, the 5 min after exercise recovery recording of head-to-foot force plate BCG and ICG were divided into 15 20-s sub-ensemble averages (as with the PEP estimation described above). The normalized change in stroke volume ∆SVi for each of the 15 sub-ensemble averages was calculated as follows:

SV ´ SV1 ∆SV “ i (2) i SV1 For each subject, subject specific correlations were performed between the J-peak of the force plate BCG and the X-point of the first derivative of ICG and for a linear regression model was calculated such that: " Xi “ α1 ˆ Jpeaki ` α2 (3) where Xˆ i is the estimated X-point for sub-ensemble average i. Similarly, subject specific correlations were performed between the I-wave of the force plate BGC and the B-point of the first derivative of ICG and for every subject a linear regression model was calculated such that:

" B i “ β1 ˆ Iwavei ` β2 (4) where Bˆi is the estimated B-point for sub-ensemble average i. Estimated LVET for each of the 15 sub-ensemble averages for each subject was then calculated as:

LVETi “ Xˆ i ´ Bˆi (5)

Similarly, another subject-specific correlation{ was performed between the amplitude of the J-peak and dz{dtmax and the linear regression model was used to estimate dz{dtmax for the sub-ensemble averages for that subject. Finally, the estimated ∆SV was calculated for each of the 15 sub-ensemble averages for every subject and the resulting estimates were compared with the reference calculated changes in stroke volume.

2.6. Signal-to-Noise Ratio Calculations For each of the 17 subjects, an ensemble average x was obtained for the 60 s baseline recording of weighing scale BCG and head-to-foot force plate BCG. The 60 s window was then divided into 12 sub-ensembles (5 s long) xi and a sub-ensemble average xi was obtained for each of the 12 sub-ensembles. Five-second ensembles were used rather than individual beats to reduce motion artifacts. Noise was calculated for each of the sub-ensemble averages using:

ni “ xi ´ aix (6) where ai is the normalization coefficient for the amplitude of the ensemble averaged BCG w.r.t the ith sub-ensemble average: x ¨ x a “ i (7) i x ¨ x The noise-to-signal ratio (NSR) was then calculated for each of the sub-ensemble averages:

varpn q NSR “ i (8) i varpxq Sensors 2016, 16, 787 6 of 11

Sensors 2016, 16, 787 6 of 11 Finally,Sensors 201 SNR6, 16, 787 was calculated as follows: 6 of 11 11 SNRSNR“ (9) 1 12121 (9) SNR  1 NSRNSR 121 i“12i11 i i 12 NSR (9) 12 i1 i 3. Results and Discussion ř 3. Results and Discussion 3.1. RI3. andResults PEP and Correlation Discussion for Scale and Force Plate BCG during Rest 3.1. RI and PEP Correlation for Scale and Force Plate BCG during Rest The3.1. RI correlation and PEP Correlation results were:for Scaler 2and“ Force0.85 ,Platem “ BCG0.97 during, b “ Rest34.92 for the force plate and r2 “ 0.81, The correlation results were: 푟2 = 0.85, 푚 = 0.97, 푏 = 34.92 for the force plate and 푟2 = 0.81, m “ 0.92, b “ 24.23 for the scale, with m2 being the slope and b being the y-intercept. These2 results show 푚 = The0.92 ,co 푏rrelation= 24.23 resultsfor the were:scale, with푟 = 0m.85 being, 푚 =the0 .97slope, 푏 =and34 b.92 being for thethe forcey-intercept plate and. These 푟 =results0.81, an improved timing accuracy in force plate compared to scale BCG in estimating PEP, demonstrating show푚 = 0 . an92 , improved푏 = 24.23 for timing the scale, accuracy with inm forcebeing platethe slope compared and b being to scale the BCGy-intercept in estimating. These results PEP, that thedemonstratingshow limited an improved mechanical that the timing limited bandwidth accuracy mechanical in the force bandwidth weighing plate compared in scale the impactsweighing to scale the scaleBCG timing impactsin estimating accuracy. the timing PEP, Figure 3 showsaccuracy.demonstrating a linear Figure regression that 3 shows the fit limited fora linear scale mechanical regression and force bandwidthfit plate for BCGscale inand RI-intervals the force weighing platevs. BCG scalePEP, RI impactsand-intervals Figure the vs4 timing .is PEP a Bland, Altmanandaccuracy. plot Figure [20 Figure ]4 thatis a 3 showsBland shows Altman a a larger linear plot standardregression [20] that deviation fit shows for scale a inlarger and PEP forcestandard estimated plate deviation BCG from RI scale- intervalsin PEP BCG estimated comparedvs. PEP, to PEP estimatedfromand Figure scale BCG from4 is compareda force Bland plate Altman to BCG.PEP plot estimated [20] that from shows force a platelarger BCG. standard deviation in PEP estimated from scale BCG compared to PEP estimated from force plate BCG.

FigureFigure 3. Linear 3. Linear regression regression fit fit for for both both scale scale andand forceforce plate head head-to-foot-to-foot BCG BCG RI- RI-intervalinterval vs. PEPvs. PEP amongFigure a3ll. Linearsubjects regression. fit for both scale and force plate head-to-foot BCG RI-interval vs. PEP amongamong all subjects. all subjects.

Figure 4. Bland Altman plot for scale and force plate linear prediction models of PEP. The blue line is FiguretheFigure 4. 95%Bland 4 .confidence Bland Altman Altman range plot plot of for forthe scale scalePEP and estimationsand forceforce plate from linear linear the scaleprediction prediction BCG RI models- modelsinterval of PEP.while of PEP. The the Theblue red blueline is line is the 95%the 95% confidence conconfidencefidence range range of ofof the thethe PEP PEPPEP estimations estimationsestimations from from thethe the forcescale scale plateBCG BCG BCGRI RI-interval-interval RI-interval. while while the red the line red is line is the 95% confidence range of the PEP estimations from the force plate BCG RI-interval. the 95% confidence range of the PEP estimations from the force plate BCG RI-interval.

Sensors 2016, 16, 787 7 of 11 Sensors 2016, 16, 787 7 of 11

3.2. RI RI and and PEP PEP Correlation Correlation for Head Head-to-Foot-to-Foot Force Plate BCG during Recovery The results were: were: 푟r2 “= 0.920.92,, m푚“=1.141.14, ,b푏“=14.6414.64.. FigureFigure5 5 shows shows a a linear linear regressionregression fitfit forfor forceforce plate head-to-foothead-to-foot BCGBCG vs.vs PEP duringduring recovery.recovery. The The higher higher correlation correlation of of head-to-foot head-to-foot BCG BCG I-wave I-wave to toPEP PEP during during exercise exercise recovery recovery compared compared to thatto that during during rest rest (r2 “ (푟0.852 =)0 can.85) becan attributed be attributed to the to more the moresignificant significant changes changes in PEP in and PEP RI and intervals RI intervals during during exercise exercise recovery recovery as both valuesas both increase values increase to stabilize to stabilizeagain at theagain baseline at the values.baseline These values. results These show results a better show correlation a better correlation coefficient coefficient (0.92 versus (0.920.86) versus and 0.86)a much and smaller a much y-intercept smaller y- (14.6interceptversus (14.6138) versus than the138) results than the obtained results from obtained correlating from correlating RJ interval RJ to intervalPEP in our to previousPEP in our work previous [16]. One work of the[16]. future One workof the objectives future work in that objectives paper was in that to examine paper was waves to examineother than waves the J-wave other than for better the J- correlationwave for better and smallercorrelation y-intercept and smaller but that y-intercept was not but feasible that was because not feasibleof the difficulty because of the detecting difficulty those of detecting other waves those during other exercisewaves during recovery. exercise However, recovery. with However, the force withplate the BCG, force we platewere ableBCG, to we successfully were able extract to successfully the I-waves extract from the all I subjects-waves from and hence all subjects obtain and the henceimproved obtain results. the improved results.

FigureFigure 5. Linear 5. Linear regression regression fit for fithead for- head-to-footto-foot force plate force BCG plate vs BCG. PEPvs. duringPEP duringrecovery recovery among amongall 17 subjects all . 17 subjects. 3.3. Stroke Volume Estimation from Head-to-Foot Force Plate BCG during Recovery 3.3. StrokeThe mean Volume error Estimation among fromall subject Head-to-Foot was 5.3% Force with Plate a BCGstandard during deviation Recovery of 4.2%. We compared our resultsThe mean to other error studies among providing all subject such was detailed 5.3% with error a standard analysis deviationinformation of 4.2%.on non We-invasive compared SV estimation.our results toIn other the ones studies available providing [21–24 such], the detailed following error results analysis were information achieved on and non-invasive considered “acceptable”.SV estimation. In In[21 the], the ones mean available error was [21 –16.5%24], the for followingSV derived results from werefemoral achieved arterial andblood considered pressure “acceptable”.(ABP) and 14.5% In [ 21for], SV the derived mean error from was radial 16.5% ABP. for In SV [22], derived the mean from error femoral was arterial8.9% for blood SV estimated pressure using(ABP) meanand 14.5% arterial for SVpressure derived (MAP) from radialand 14.35% ABP. In for [22 SV], the estimated mean error using was the 8.9% systo forlic SV waveform. estimated Inusing [23], mean the arterial percentage pressure error (MAP) between and 14.35% electric for SV velocimetryestimated using and the transesophageal systolic waveform. Doppler In [23], echocardiographthe percentage error for betweenestimating electric cardiac velocimetry output (CO) and— transesophagealwhich is obtained Doppler from echocardiographmultiplying SV forby heartestimating rate— cardiacwas 29%. output All (CO)—which of these results is obtained were considered from multiplying acceptable. SV by Inheart [24], rate—was the comparison 29%. All betweenof these resultsSV estimation were considered using the acceptable.Vigileo-FloTrac In [24™], system the comparison and esophageal between Doppler SV estimation yielded using an error the rateVigileo-FloTrac of 58% which™ wassystem deemed and esophageal unacceptable, Doppler since according yielded an to error [25], ratelimits of of 58% agreement which was of ±30% deemed are acceptable.unacceptable, Hence, since our according method to allows [25], limits SV changes of agreement estimation of ˘30% for areevery acceptable. subject accurately Hence, our and method can provideallows SV insight changes in the estimation case of forHF everypatients. subject The accurately per subject and results can provide of the absolute insight inand the percentage case of HF meanpatients. and Thestandard per subject deviations results of the of the estimated absolute changes and percentage in stroke volume mean and compared standard to deviationsthe reference of calculationsthe estimated are changes shown in in stroke Table volume 1. Figure compared 6 also toshows the reference the percentage calculations estimated are shown stroke in volume Table1. changesFigure6 alsoin comparison shows the percentage with the calculated estimated stroke percentage volume stroke changes volume in comparison changes from with the ICG. calculated We can observepercentage in Figure stroke volume6 that the changes increase from in SV ICG. positive We can in observe some ca inses Figure while6 that being the negative increase in in other SV positive cases. Thisin some can casesbe attributed while being to the negative fact that in SV other increases cases. Thisduring can exercise be attributed and then to the decreases fact that again SV increases during exerciseduring exercise recovery and and then it sometimes decreases againgoes below during the exercise baseline recovery SV by andthe itend sometimes of the recovery goes below period. the Hence, at the beginning of the recovery period, the change in SV would be positive while at the end of the recovery period the change in SV would be around zero or sometimes negative.

Sensors 2016, 16, 787 8 of 11

Sensorsbaseline 2016 SV, 16, by 787 the end of the recovery period. Hence, at the beginning of the recovery period,8 of the 11 change in SV would be positive while at the end of the recovery period the change in SV would be around zero or sometimes negative.Table 1. Per subject errors in ∆SV estimation. Subject µ Error (mL) σ Error (mL) % µ Error % σ Error Table 1. 1 2.0 Per subject errors1.7 in ∆SV estimation.3.6 3.1 2 1.4 0.9 3.4 2.3 Subject µ Error (mL) σ Error (mL) % µ Error % σ Error 3 3.7 2.6 6.5 4.6 4 14.0 2.04.9 1.7 5.4 3.6 3.16.7 2 1.4 0.9 3.4 2.3 5 32.5 3.71.8 2.6 6.4 6.5 4.64.7 6 42.8 4.02.0 4.9 8.9 5.4 6.76.4 7 53.4 2.53.1 1.8 6.6 6.4 4.76.0 8 62.8 2.82.6 2.0 4.6 8.9 6.44.3 7 3.4 3.1 6.6 6.0 9 5.6 4.9 11.0 9.5 8 2.8 2.6 4.6 4.3 10 93.4 5.62.5 4.9 11.04.5 9.53.4 1110 3.1 3.43.0 2.5 5.3 4.5 3.45.1 1211 3.3 3.11.4 3.0 5.2 5.3 5.12.1 1312 1.6 3.31.3 1.4 3.3 5.2 2.12.8 13 1.6 1.3 3.3 2.8 1414 1.9 1.91.4 1.4 4.6 4.6 3.3 1515 1.0 1.00.8 0.8 2.2 2.2 1.8 1616 3.4 3.41.8 1.8 4.7 4.7 2.5 1717 3.5 3.52.5 2.5 4.6 4.6 3.2 AverageAverage 2.9 2.92.3 2.3 5.3 5.3 4.2

Figure 6.6. Estimated stroke volume percent changes from head head-to-foot-to-foot force plate BCG compared to calculated stroke volume percent changes from reference ICG.ICG.

3.4. Signal Signal-to-Noise-to-Noise Ratio Comparison for Scale BCG and Force Plate Head-to-FootHead-to-Foot BCG SNR results show slightly better SNR values for force plate head head-to-foot-to-foot BCG compared to scale BCG. The results for SNR calculations are shown in Table2 2. . The force of thethe BCGBCG signalsignal isis 22 NppNpp andand the the sensitivity sensitivity of of the the transducer transducer in in the the weighing weighing scale scale is 19.1is 19.1µV/N μV/N [16 [16]] which which would would give give a BCGa BCG voltage voltage of of 38.2 38.2µVpp. μVpp. The The noise noise level level referred referred to to the the input input of ofthe the circuit circuit is 150is 150 nVpp. nVpp. This This would would lead lead to an to SNRan SNR of 255 of in255 the in linearthe linear scale scale or 48 or dB. 48 As dB. for As the for force the forceplate, plate,with the with same the BCG same amplitude BCG amplitude of 2 Npp of for 2 the Npp BCG for and the a BCG sensitivity and a of sensitivity 19 mV/N ofamplified 19 mV/N by amplified250 times inby the 250 z-direction times in the gives z-direction a signal gives of 9.5 a Vpp. signal The of output 9.5 Vpp. noise The level output is 5 noise mVpp level which is 5 leads mVpp to whichan SNR leads of 1900 to an in theSNR linear of 1900 scale in orthe 66 linear dB. Those scale calculationsor 66 dB. Those are clearly calculations far better are fromclearly what far webetter are from what we are obtaining in terms of SNR for both the scale and force plate so the difference cannot be attributed to this. Hence, it is likely that the BCG SNR is limited by motion artifacts, not electronic noise.

Sensors 2016, 16, 787 9 of 11 obtaining in terms of SNR for both the scale and force plate so the difference cannot be attributed to this. Hence, it is likely that the BCG SNR is limited by motion artifacts, not electronic noise.

Table 2. Per subject SNR calculations for scale and force plate BCG.

Subject Gender Height (cm) Weight (kg) FP SNR (dB) Scale SNR (dB) 1 Female 160 59 6.0 5.6 2 Male 175 75 8.6 7.6 3 Female 168 68 8.1 5.3 4 Female 160 52 1.7 1.3 5 Male 183 86 8.3 4.3 6 Male 175 74 1.5 -1.8 7 Female 152 49 5.5 4.6 8 Male 178 65 3.4 1.7 9 Male 178 88 1.6 1.5 10 Male 178 68 2.6 1.2 11 Male 190 88 7.1 5.1 12 Female 175 68 9.0 8.1 13 Male 175 70 3.8 3.8 14 Male 185 76 8.3 8.3 15 Male 175 79 3.1 2.7 16 Female 163 75 6.7 6.6 17 Female 168 61 8.0 7.8

We attribute the improvement in SNR in the force plate BCG over scale BCG to the fact that a subject can stand more comfortably on the force plate with their feet wider apart than with the scale. To further investigate this theory, we had one subject stand on the force plate with feet 12.7 (same distance as for the scale), 20.3, 25.4 and 35.6 cm apart. The SNR results were as follows: 2.2, 2.9, 4.4, and 4.3 dB, respectively, suggesting that there is a slight improvement in SNR associated with allowing the feet to be wider apart as compared to the scale. This goes in accordance with Reference [26] which states that stance width influences the magnitude of body sway and observed that for subjects standing with their feet 5, 17, and 30 cm apart and exposed to visual motion, the widest distance (30 cm) was associated with reduced motion sickness. Moreover, we observed the order in which subjects stood on the scale and force plate (since half the subjects stood on scale first while the other half stood on the force plate first to account for any bias due to the order in which the subject stood on the force plate versus the weighing scale), and found that the difference in SNRs between force plate and weighing scale BCG was bigger for all the subjects that stood on the force plate first (subjects: 2, 3, 5, 6, 8, 10, 11, and 12 in Table2) compared to those who stood on the weighing scale first (subjects: 1, 4, 7, 9, 13, 14, 15, 16, and 17 in Table2). For both groups, the results are statistically significant (p < 0.05).

3.5. Limitations One limitation of the work is that all participants are healthy, young subjects. Future work will focus on acquiring and analyzing data from a more diverse population and from HF patients in particular. The methods described in this paper set the technological framework for studies on HF patients. The main challenges in such patients are irregularities in the ECG signal whose R-peaks we use as reference to ensemble average the rest of the signals, as well as reduced signal quality in the BCG signals themselves.

4. Conclusions We have shown in this paper that force plate BCG allows for more reliable PEP estimations than scale BCG due to more accurate timing information that can be attributed to the wider bandwidth of the force plate. Nevertheless, the cost of the force plate is significantly higher than the weighing scale, Sensors 2016, 16, 787 10 of 11 and thus the force plate—at this current cost—would not be feasible for at-home use. If the force plate was positioned in a central location accessible to multiple HF patients such as in a grocery store, the cost of the force plate could be amortized over the large number of patients that could access the device, thus allowing it to be a feasible option for monitoring patients outside the clinic/hospital setting. We have also shown that force plate head-to-foot BCG can be used to estimate SV changes accurately. However, in order to determine subject-specific coefficients for how the BCG parameters relate to the ICG parameters, simultaneous ICG measurements are needed at first for calibration purposes. Hence, the estimation of SV changes hinges on having the initial reference measurement. Nevertheless, for the HF patients monitoring application, such initial measurements of SV can be obtained at the index visit, and then the BCG-equipped force plate can be used to measure changes from that initial value over time. Changes in SV could then be used to titrate care, thus potentially leading to improved outcomes and reduced costs.

Acknowledgments: Research reported in this publication was supported in part by the National Institute on Aging of the National Institutes of Health under Award Number R56AG048458. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Author Contributions: All authors conceived and designed the experiments; H.A. and L.O. performed the experiments and analyzed the data; all authors were involved in writing and editing the paper. Conflicts of Interest: The authors declare no conflict of interest.

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