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Improved Pre-Ejection Period Estimation From Ballistocardiogram and Electrocardiogram Signals by Fusing Multiple Timing Interval Features A. Ozan Bicen, Member, IEEE, Nil Z. Gurel, Student Member, IEEE, Alexis Dorier, and Omer T. Inan, Senior Member, IEEE
Abstract— Sympathetic nervous system (SNS) activity plays a significant role in cardiovascular control. Preejection period (PEP) is a noninvasive biomarker that reflects SNS activity. In this paper, unobtrusive estimation of PEP of the heart using ballistocardiogram (BCG) and electrocardiogram (ECG) signals is investigated. Although previous work has shown that the time intervals from ECG R-peak to BCG I and J peaks are correlated with PEP, relying on a single BCG beat can be prone to errors. An approach is proposed based on multiple regression and use of initial training data sets with a reference standard, impedance cardiography (ICG). For evaluation, healthy subjects were asked to stand on a force plate to record BCG and ECG signals. Regression coefficients were obtained using leaveone-out cross-validation and the true PEP values were obtained using ECG and ICG. Regression coefficients were averaged over two different recordings from the same subjects. The estimation performance was evaluated based on the data, via leave-one-out cross-validation. Multiple regression is shown to reduce the mean absolute error and the root mean square error, and has a reduced confidence interval compared with the models based on only a single feature. This paper shows that the fusion of multiple timing intervals can be useful for improved PEP estimation. Index Terms— Ballistocardiography, cardiomechanical signals, pre-ejection period, sympathetic nervous system, autonomic balance, wearable sensing systems, regression modeling, crossvalidation.
I. I NTRODUCTION
T
HE autonomic nervous system (ANS) comprises a balance, i.e., autonomic tone, between the “fight or flight” sympathetic nervous system (SNS) and the “rest and digest” parasympathetic nervous system (PNS). SNS overactivity is known to play a significant role in cardiovascular control [1], [2] and in the progression of cardiovascular diseases such as heart failure and hypertension [3], [4]. Quantify-
Manuscript received April 17, 2017; revised May 17, 2017; accepted May 18, 2017. Date of publication May 23, 2017; date of current version June 12, 2017. This work was supported by the National Institutes of Health, National Heart, Lung and Blood Institute under Grant R01HL130619. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The associate editor coordinating the review of this paper and approving it for publication was Prof. Aime Lay-Ekuakille. (Corresponding author: A. Ozan Bicen.) The authors are with the Inan Research Laboratory, School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA (e-mail:
[email protected];
[email protected];
[email protected];
[email protected]). Digital Object Identifier 10.1109/JSEN.2017.2707061
ing SNS activity and responses to various treatments designed to restore normal autonomic balance, such as baroreceptor pacing [5] or vagal nerve stimulation [6], can potentially allow early detection of disease and/or titration of care [7]. Accordingly, there is a need for unobtrusive, reliable and valid method to obtain serial measurements of SNS activity outside of clinical settings. Saliva biomarkers [8], skin conductance [9], pupil diameter [10], electrocardiogram (ECG) based heart rate variability (HRV) measures are non-invasive techniques widely researched to quantify SNS activity. Salivary enzyme α-amylase (sAA) requires a bio-sensing platform for proper handling of assay kits, chemical reactions and result interpretation [11], [12]. Although there are significant efforts to develop point-of-care sAA sensing, the research is currently at an early stage for use at home, without a trained professional [13]. Skin conductance can be misleading, since it can change due to a variety of factors unrelated to SNS activity such as ambient temperature [14], humidity [15] and air pressure [16]. Pupil diameter measurements require expensive clinical devices such as pupillometers or wavefront analyzers [17]. In recent years, infrared video-based image processing algorithms and inexpensive setups have been proposed to compute pupil diameter outside of clinic [18], [19]. However, when evaluated in different disease groups, these approaches have been shown to have insufficient accuracy as a screening tool for ANS activity [20], [21]. The most commonly studied means of assessing SNS activity outside of clinical settings is HRV, quantified from the time- or frequency-domain characteristics of heartbeat intervals. While convenient to measure, HRV analysis has limitations, and there are disagreements in the scientific community regarding its merit in quantifying SNS activity in particular. The low frequency (LF) component of HRV is considered as a marker of SNS activity by some [22], [23], while others consider it to represent both SNS and PNS modulation [24], [25]. The key points against the use of the LF component as an index of SNS activity derive from studies that directly compare invasive measurements of sympathetic activity or brain imaging techniques with LF or LF/HF ratio [7], and find a lack of correlation between the two methods. Moreover, European and North American standards define PNS activity as being a major component of the high-frequency (HF) HRV
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BICEN et al.: IMPROVED PEP ESTIMATION FROM BCG AND ECG SIGNALS
Fig. 1. Illustration of BCG measurement platform used in this work for human subject experiments (a). Human subject is standing on a ballistocardiogram (BCG) sensor, e.g., force plate. Note that ICG electrodes are only required for regression coefficient estimation, not for home monitoring. Wet electrodes for ECG can be replaced with handheld dry electrodes from which R-peaks can be detected in synchronous with BCG signals. Trace of an ensemble averaged BCG heartbeat with annotations of features: I-, J-, and K-peaks (b).
fluctuations, and both SNS and PNS activity as the components of LF fluctuations [26]. A promising alternative, or complement, to HRV is the pre-ejection period (PEP), a non-invasive measurement which can quantify sympathetic activity. PEP is defined as the time interval from the ECG Q-wave to the opening of the aortic valve, and reflects cardiac contractility, a characteristic primarily controlled by sympathetic (beta-adrenergic) influences [27]–[29]. A shortened PEP for a given heartbeat is associated with an increase in contractility, thus reflecting an increase in sympathetic activity, provided that posture of the subject is held constant [30], [31]. A comparison of PEP and LF/HF HRV showed that PEP outperforms LF/HF for conditions that are known to increase sympathetic activity, such as mental stress [32], [33], exercise [34], [35] and betaadrenergic/cholinergic blockade [30], [36] However, PEP is typically much less convenient than ECG measurements alone, as it requires an impedance cardiogram (ICG) measurement in parallel with ECG, and thus multiple electrodes positioned carefully on the neck and thorax. Thus, it is not typically used for serial assessments of autonomic balance at home. Unobtrusive technologies for estimating PEP accurately outside of clinical settings can improve the state of the art for long-term SNS activity monitoring. To this end, the ballistocardiogram (BCG) and electrocardiogram (ECG) signals from a weighing scale may provide an unobtrusive alternative for PEP estimation that could be used widely [37]. The BCG signal represents the reaction forces of the body in response to cardiac ejection and movement of blood through the vasculature [38]–[42]. For measuring BCG and ECG on a scale, patients would need to stand on the scale and hold the handlebar electrodes, respectively, which could provide an easy-to-use, inexpensive, and potentially widely acceptable method for home use [43]–[45]. Therefore, unobtrusive BCG and ECG measurements can enable assessment of the SNS activity outside the clinical settings. Fig. 1(a) depicts an illustration of a person using the BCG and ECG measurement scale. Overall, simultaneous collection of signals from
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multiple sensing modalities and fusion techniques are considered very interesting in biomedical applications to enable comparison and tracking of physiological parameters [46]. In this paper, we present a novel approach based on multiple regression and repeated measurements to provide robust PEP estimation with lower error than single feature approaches. To this end, the problem of feature set selection for the regression modeling is specifically investigated. Three distinct features of the BCG heartbeat, i.e., R-I, R-J, and RK intervals, are used to create seven different feature sets, which include all possible combinations of the features. Leaveone-out cross-validation is used to estimate the regression coefficients and PEP for each feature set and subject, i.e., the subject whose PEP will be estimated is not included in the regression process. Multiple BCG measurements from the same subjects are considered, i.e., regression coefficients were averaged over two data sets. BCG measurements in a causal setting are targeted, i.e., when the subject is standing still on a force sensor. The following question is sought to be investigated throughout the human subject experiments: what is the effect of the selected feature set on the accuracy of the PEP estimation in a population using regression modeling of BCG heartbeat features? PEP estimation error results are presented in terms of the mean absolute error (MAE) and root mean square error (RMSE), and comparisons are made in the error results across all feature sets. We also studied the confidence intervals for the PEP estimation error with respect to various feature sets. Additionally, we investigated the impact of variability between training sets on the PEP estimation performance, and how the use of multiple features and repeated experiments for regression modeling can help to reduce the estimation error deviations due to the data set specific bias. The remainder of this paper is organized as follows. In Section II, BCG heartbeat and feature extraction are explained. In Section III, we present the regression modeling. We present the experimental performance evaluation for the PEP estimation error in Section IV. The paper is concluded in Section V. II. BCG H EARTBEAT AND F EATURES A typical BCG heartbeat, obtained from a BCG signal windowed into heartbeats using the ECG R-peak, is illustrated in Fig. 1(b), and the timing intervals, i.e., features, related to the PEP, can be extracted from this heartbeat [47]. Prior studies have shown that certain features, i.e., R-I and R-J intervals, of the BCG heartbeats are linearly correlated and homoscedastic with the PEP [48], [49]. However, the accuracy of PEP estimates obtained by joint use of BCG and ECG may not be sufficient and show variability based on many factors, including: 1) subject-specific variations in the shape of BCG heartbeat, 2) motion artifacts and instrumentation errors, and 3) heart disease which affects the overall morphology of the heartbeat such that it is substantially different from healthy subject recordings. In this work, our focus is on addressing the first challenge: variations in the BCG heartbeat compared to the classical
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dividing the BCG time series into equal length windows based on the minimum of the inter-beat R-R interval. B. Sample Mean of BCG Heartbeat
Fig. 2. Illustration of abrupt variations in the BCG heartbeat. Beginning of each BCG heartbeat corresponds to an R peak. The widening and non-symmetric curvature should be noted for I-peak, J-peak, and K-peak in (a), (b), and (c), respectively. Additionally, the transition from I-peak to J-peak has an unusual peak in (d).
BCG heartbeats obtained from adjacent heartbeats are grouped together to form an ensemble of beats. The sample mean of the BCG heartbeats, i.e., ensemble average, is calculated for each ensemble for reducing interference due to motion or respiration. The number of ensembles ηm for subject m is given by ηm = Nm /W , where Nm and W are the number of heartbeats in the measurements for subject m and number of heartbeats per ensemble, respectively. Since we used the same recording length during experiments for all subjects, ηm is constant for all subject, hence, we drop subscript m, and denote the number of ensembles as η. The sample mean of the BCG heartbeat is calculated for each ensemble q as (m) (q) ci (t) (1) sm = i∈Wq
Fig. 3.
Steps used for extracting features from the recorded BCG signal.
where m is the subject id, Wq is the set of heartbeats for ensemble q, ci(m) (t) is the BCG heartbeat, which is obtained (q) as explained in Section II-A. sm is used for feature extraction. C. Feature Extraction
BCG heartbeat morphology (see Fig. 1(b)) presented in Starr’s seminal work [47]. In Fig. 2 some of the possible variations in the BCG heartbeat are illustrated based on the data collected in human subject experiments in our study. The I-peak, J-peak, and K-peak exhibit a widened and nonsymmetric curvature in Fig. 2 (a), (b), and (c), respectively. Furthermore, in Fig. 2 (d), the ramp-up from I-peak to J-peak has an unusual recession. Therefore, relying on single features can result in data set specific bias and deviations in the estimated PEP, hence it is crucial to select the feature set that minimizes the estimation error as well as taking repeated measurements for estimation of regression coefficients. In the following, the steps for computation of the BCG heartbeat and the extraction of the features relevant to the PEP are given. A block diagram representation of the feature extraction process is given in Fig. 3. A. Windowing of BCG Time Series to Obtain BCG Heartbeat Filling the heart and ejecting blood in each pumping cycle consists of precisely sequenced electrical and mechanical events (i.e., excitation-contraction coupling). Thus, the timing of BCG features (associated with mechanical events) has been considered in each heartbeat using the R-peak of the ECG (associated with electrical activity) as a fiduciary [48]. To divide the BCG time series into heartbeat windows, the ECG R-peak peaks were detected using a simple automated peak detection algorithm. After manual verification of R-peaks in each of the experiments for each subject, the minimum of the interval between R-peaks of the consecutive heartbeats, i.e., R-R interval, for a subject is set as the window length for that subject. Finally, the BCG heartbeat ci (t) for each heartbeat i is generated via
The relative timing of the BCG heartbeat features with respect to the ECG R-peak has utmost importance for unobtrusive monitoring of cardiac PEP. We specifically utilize relative timings of I-peak, J-peak, and K-peak in the BCG heartbeat with respect to the ECG R-peak to extract features for each subject in the experiments. These features are chosen in particular since the I, J, and K peaks of the BCG signal are typically present in most BCG heartbeats, while other peaks such as the H or L are highly variable. Considered features of the BCG heartbeat are illustrated in Fig. 1 (b). (q) The maximum amplitude value of the BCG heartbeat sm in the first 300msec is located as the J-peak for subject m and ensemble q. The time elapsed between the R-peak of the ECG and J-peak of the BCG gives the R-J interval, which is (m,q) . denoted by x J The I-peak is detected as the minimum before the detected J-peak and after the first 70msec portion of the corresponding (q) sample mean BCG heartbeat sm . This 70msec minimum R-I timing was selected empirically based on extensive BCG recordings from our group and others in the literature. The R-I interval was calculated as the time delay between the R-peak of the ECG and I-peak of the BCG, which is denoted (m,q) for subject m and ensemble q. by x I The K-peak is obtained by detecting the minimum after the J-peak of the sample mean BCG heartbeat in the 200msec interval. The R-K interval was calculated as the time delay between the R-peak of ECG and K-peak of BCG, which is (m,q) denoted by x K . (q) The feature vector xi for each of the R-I, R-J, and R-K intervals is defined as T (q) xi∈{I,J,K} = x i(1,q), x i(2,q) , · · · , x i(M,q) (2)
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D. True Cardiac PEP Value for Regression Modeling and Validation To estimate the exact cardiac PEP, the regression coefficients needs to be estimated using the ”true” cardiac PEP measurements. For example, these true PEP measurements can be taken in a clinical environment before the subject is discharged for follow-on monitoring at home and personalized treatment control. BCG is an easy-to-use tool that subjects can use themselves without a trained medical professional present. In this work, reference standard PEP measurements from ICG are used to represent the ”true” value of PEP together with a simultaneous ECG measurement [50]. The ICG time series are windowed and sample mean ICG heartbeats are obtained as in the BCG case explained earlier in Section II-A and II-B. The interval between ICG B-peak, i.e., the incisura point at the foot of the the ascending leg of ICG heartbeat, and the ECG R-peak is taken as the cardiac PEP, which is denoted by y(m,q) for subject m and ensemble q. Feature vector y(q) for R-B interval is as T (3) yq = y1,q , y2,q · · · , y M,q Regression coefficients are estimated for each feature set using the true cardiac PEP via leave one-out cross-validation method. Details of the regression modeling are given in the next section. III. R EGRESSION M ODELING Linear regression models of cardiac PEP are developed based on the various subsets of BCG heartbeat features. Signal modeling for PEP which incorporates all considered features of the BCG heartbeat in this work is given by (m,q)
ym,q = βI x I
(m,q)
+ βJ x J
(m,q)
+ βK x K
+ β0 +
(4)
where βI , βJ , and βK are the regression coefficients for I, J, and K features, respectively; β0 is the constant term, i.e., y-intercept; and is the normally distributed additive noise variable. Details of regression coefficient estimation, PEP estimation, and error calculation is given in the following subsections. A. Regression Coefficient Estimation Regression coefficients are estimated for each ensemble using the least-squares method, which is given by βˆ q = (XqT Xq )−1 XqT yq
(5)
where βˆ q is the estimated regression coefficients for the ensemble q T (6) βˆ q = βˆI(q), βˆJ(q) , βˆK(q) , βˆ0(q) , and Xq is the feature matrix for ensemble q composed of feature vectors as (q) (q) (q) (7) Xq = x I x J x K 1 It should be noted that a column vector of 1s is appended to the feature vectors for the estimation of the constant
Fig. 4. Steps used for PEP estimation and error calculation. Repeated for each ensemble in each data set. In each data set, regression coefficients were averaged over ensembles. (q)
term β0 . The regression coefficients obtained per ensemble are averaged across the ensembles as η 1ˆ βˆ = βq η
(8)
q=1
For cross-validation purposes, the leave-one-out method is used during regression, i.e., the subject whose PEP will be estimated is excluded from the regression coefficient estimation. Therefore, regression coefficients are estimated for each subject repeatedly for each feature set. B. PEP Estimation and Error Calculation For any feature set, the PEP estimate can be calculated using the obtained regression coefficients via the leave-oneout method (see Fig. 4) as T yˆ q = βˆ Xq
(9)
For performance evaluation, MAE, RMSE, and confidence interval metrics are examined. The MAE is calculated as MAE =
M η 1 ym − yˆm,q ηM
(10)
m=1 q=1
The RMSE is calculated as η M 1 RMSE =
(ym,q − yˆm,q )2 ηM
(11)
m=1 q=1
Confidence interval is studied for PEP estimation error for a confidence level of 1 − α. Sample mean error ξ is defined as (12) ξ = E{ym − yˆm,q } We assume the error ξ is Gaussian distributed. Standardized distribution ζ of the ξ is given by ζ ∼
ξ −μ √ σ/ M
(13)
where μ and σ are the sample mean and the sample standard deviation of the error. μ can be computed by modifying (10) not to include absolute value operation. σ is equal to the RMSE in (11). For confidence level 1 − α, the corresponding critical value is found as α (14) γ = Q −1 ( ) 2 where Q denotes to the Q-function. Accordingly, probability value of the confidence level 1 − α is given by 1 − α = P(−γ ≤ ζ ≤ γ )
(15)
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Using (13) and (14) in (15), we obtain the below equality ξ −μ −1 α −1 α 1−α = P Q ≤ √ ≤Q 2 2 σ/ M α σ σ −1 −1 α √ ≤μ≤ξ+Q √ = P ξ−Q 2 2 M M (16) √ √ where ξ + Q −1 α2 σ/ M and ξ − Q −1 α2 σ/ M give the upper and lower end points, respectively, for the confidence level 1 − α. In the next section, human subject experiments are presented.
TABLE I S UBJECT D EMOGRAPHICS
IV. H UMAN S UBJECT E XPERIMENTS A. Experimental Setup 1) Instrumentation and Measurement System: Oscillations of the body weight associated with each heartbeat (i.e., the BCG signal) were sensed by a force plate. We utilized the same instrumentation as in [49]. Briefly, the force plate (Type 9260AA6, Kistler Instrument Corp, NY, USA) had a natural frequency of approximately 200Hz in the head-to-foot direction. The measured BCG signal was fed from the force plate to the MP150 data acquisition system (BIOPAC Systems Inc., Goleta, CA, USA). The ECG and ICG signals were measured using BN-RSPEC and BN-NICO wireless modules (BIOPAC Systems, Inc., Goleta, CA, USA), respectively, again connected to the MP150 module. Three electrodes were placed for ECG recordings, and ECG leads were connected in the Lead II configuration. Eight electrodes were connected for ICG recordings. Locations of the ECG and ICG electrodes are illustrated in Fig. 1 (a). It should be noted that although we used wet electrodes for ECG measurements, handheld dry electrodes can be utilized for at home deployment, which can be recorded synchronously with BCG signals from a bathroom scale, as shown in [43]. The sampling rate was set to 2kHz for all measurements. ECG, BCG, and ICG signals were bandpass filtered with a passband of 10-30Hz, 0.8-20Hz, and 0.8-30Hz, respectively. For all bandpass filters, 60dB suppression was used for stopbands. 2) Experimental Protocol: Thirteen human subjects (three female and ten male) participated in the experiments. After the wet electrodes were attached, subjects were asked to stand still with the same posture on the force plate. Data collection is illustrated in Fig. 1 (a). Data were collected with approval from the Institutional Review Board (protocol H13512) at the Georgia Institute of Technology, and with written informed consent obtained. Subject demographics are given in Table I. B. PEP Estimation Error Analysis The regression scheme described in Section III-A was applied to two resting data sets, and then the obtained regression coefficients were averaged. 1) Absolute Error: In Table II, results for the absolute error are presented. Regression coefficients were obtained using the steps given in Fig. 4 and averaged over two data sets from the same subjects. The absolute error was computed for each subject using both data recordings and then
TABLE II A BSOLUTE E RROR FOR L EAVE -O NE -O UT C ROSS VALIDATION
averaged. Then, MAE was computed via averaging over all subjects as formulated in (10). It is observed that the MAE and the standard deviation of the absolute error for feature sets {I, J} and {I, J, K} are lower than the other feature sets. The feature set {I, J, K} shows the minimum MAE performance in our experiments. Specifically, multiple regression using all three features, i.e., R-I, R-J, and R-K intervals, are observed to reduce the MAE about half compared to the models based on only a single feature. 2) RMSE: The RMSE results are given in Fig. 5. Bars represent the RMSE for each feature set. Grey colored bars are RMSE results when regression coefficients calculated using only data set 1. Yellow colored bars are RMSE results when regression coefficients calculated using only data set 2. Red colored bars are RMSE result when regression coefficients were averaged over the two resting data sets. We observed a variation of the estimation results based on single feature modeling. For example, when regression coefficients were estimated using only data set 1, modeling based on I-peak gave the best result. Modeling based on J-peak gave the best result, when only data set 2 was used for estimation of regression coefficients. This is attributed to the to subject-specific morphological changes in the BCG heartbeat. The RMSE for regression modeling based on single features
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the RMSE was reduced by approximately 3dB (p
