sensors
Article
Bed-Based Ballistocardiography: Dataset and Ability to Track
Cardiovascular Parameters
Charles Carlson 1, * , Vanessa-Rose Turpin 2 , Ahmad Suliman 1 , Carl Ade 2 , Steve Warren 1
and David E. Thompson 1
1
2
*
Citation: Carlson, C.; Turpin, V.-R.;
Suliman, A.; Ade, C.; Warren, S.;
Thompson, D.E. Bed-Based
Ballistocardiography: Dataset and
Ability to Track Cardiovascular
Parameters. Sensors 2021, 21, 156.
https://doi.org/10.3390/s21010156
Received: 30 November 2020
Mike Wiegers Department of Electrical and Computer Engineering, Kansas State University,
Manhattan, KS 66506, USA; suliman@ksu.edu (A.S.); swarren@ksu.edu (S.W.); davet@ksu.edu (D.E.T.)
Department of Kinesiology, Kansas State University, Manhattan, KS 66506, USA;
vanessaturpin@ksu.edu (V.-R.T.); cade@ksu.edu (C.A.)
Correspondence: cwcarl@ksu.edu
Abstract: Background: The goal of this work was to create a sharable dataset of heart-driven signals,
including ballistocardiograms (BCGs) and time-aligned electrocardiograms (ECGs), photoplethysmograms (PPGs), and blood pressure waveforms. Methods: A custom, bed-based ballistocardiographic
system is described in detail. Affiliated cardiopulmonary signals are acquired using a GE Datex
CardioCap 5 patient monitor (which collects ECG and PPG data) and a Finapres Medical Systems
Finometer PRO (which provides continuous reconstructed brachial artery pressure waveforms and
derived cardiovascular parameters). Results: Data were collected from 40 participants, 4 of whom
had been or were currently diagnosed with a heart condition at the time they enrolled in the study. An
investigation revealed that features extracted from a BCG could be used to track changes in systolic
blood pressure (Pearson correlation coefficient of 0.54 +/− 0.15), dP/dtmax (Pearson correlation
coefficient of 0.51 +/− 0.18), and stroke volume (Pearson correlation coefficient of 0.54 +/− 0.17).
Conclusion: A collection of synchronized, heart-driven signals, including BCGs, ECGs, PPGs, and
blood pressure waveforms, was acquired and made publicly available. An initial study indicated
that bed-based ballistocardiography can be used to track beat-to-beat changes in systolic blood
pressure and stroke volume. Significance: To the best of the authors’ knowledge, no other database
that includes time-aligned ECG, PPG, BCG, and continuous blood pressure data is available to the
public. This dataset could be used by other researchers for algorithm testing and development in this
fast-growing field of health assessment, without requiring these individuals to invest considerable
time and resources into hardware development and data collection.
Keywords: ballistocardiography; unobtrusive cardiac monitoring; shared biomedical database;
cuff-less blood pressure monitoring; force sensors
Accepted: 22 December 2020
Published: 29 December 2020
Publisher’s Note: MDPI stays neu-
1. Introduction
tral with regard to jurisdictional clai-
Ballistocardiogram (BCG) systems have the potential to track information such as
cardiac beat-to-beat intervals (instantaneous pulse rate) [1], heartrate variability (HRV) features [2], sleep quality [3,4], heart contractility [5], and even blood pressure [6,7]. Given that
a BCG is created by the pumping action of the heart, it contains cardiovascular information
complementary to other cardiac signals, making it sensible to extract additional information when used in combination with other signals (e.g., a BCG plus a photoplethysmogram
(PPG), or a BCG plus an electrocardiogram (ECG)) [8].
A BCG is a representation of the body’s recoil response to the forces created by the
heart as it pumps blood into the vascular system [9]. An acquired BCG is thus a representation of a more general three-dimensional signal that contains vector components
with axial directions named with respect to the body—front-to-back, head-to-foot, and
side-to-side [10]. Different methods to gather BCGs exist, including wearable systems,
ms in published maps and institutional affiliations.
Copyright: © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Sensors 2021, 21, 156. https://doi.org/10.3390/s21010156
https://www.mdpi.com/journal/sensors
Sensors 2021, 21, 156
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weighing scales, chair-based systems, and bed-based systems. Although somewhat intrusive, wearable systems, which provide continuous measurements, have been investigated
for this purpose [11,12]. Weighing scales, which measure the head-to-foot axial component,
are possibly the most researched methodology [6,13–15]. However, these systems do not
have an important property of other BCG systems—the ability to monitor cardiovascular
data unobtrusively without effort from the user. Truly non-unobtrusive systems include
chair-, bed-, video-, or radar-based systems [7,16–23]. Of these, our team has experience
with bed-based systems, and two sample BCGs acquired with a bed system that employs
two different sensing technologies (electromechanical films (EMFis) and load cells) are
illustrated in Figure 1 along with a time-aligned ECG. The main BCG features (I, J, and K)
are annotated for the load cell BCG (LC BCG).
Figure 1. Two sample ballistocardiograms (BCGs) acquired with a bed system using electromechanical film and load cell
sensing technologies along with a time-aligned electrocardiogram. The BCG signals have been scaled/shifted vertically.
ECG: electrocardiogram; LC BCG: load cell BCG; EMFi: electromechanical film.
Often, when new BCG acquisition systems and the related processing algorithms
are created and assessed, ground-truth cardiopulmonary data are collected to affirm hypotheses based upon these BCGs; however, complete datasets are rarely illustrated in the
resulting publications, much less provided to the reader for follow-on analysis. Not having
access to these datasets makes it difficult for other researchers to test new methods or
theories related to the original publication (e.g., when seeking to compare the effectiveness of BCG peak-detection algorithms [1]). When such datasets are fully described in a
publication and made available (along with code when applicable), comparisons become
possible, and significant time can be saved. For example, if a group wants to test a new
BCG heartbeat detection algorithm without having access to a pre-existing dataset, they
must first invest considerable time into hardware design, development, and verification
prior to data collection and algorithm testing.
Our team at Kansas State University has created a bed-based BCG system designed to
monitor and quantify the sleep of children who reside at Heartspring, a residential and
day school facility in Wichita, KS dedicated to helping children with specialized needs
and severe disabilities, including children who are non-verbal and who have disorders
across the autism spectrum [24]. The goal of this paper, as a follow-on to this prior work,
is two-fold: (1) to outline the bed system in detail, and (2) to provide a dataset to the
research community that includes ballistocardiographic signals monitored by the bed
system—signals that are synchronized with more traditional heart-related signals, where
these data originate from adult participants with a variety of ages and health conditions.
By making this dataset publicly available, this team hopes that accelerated progress
can be made in the field of ballistocardiography toward the following:
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1.
2.
3.
4.
Improved heartbeat detection algorithms;
Feasibility assessments related to bed-BCG-based blood pressure tracking;
A better understanding of the influence of sensor location and type on BCG morphology;
Enhanced motion detection/mitigation algorithms.
The idea of continuously monitoring blood pressure using noninvasive means has
become a popular topic in recent years [7,25–28]. Pulse arrival time (PAT), meaning the
time delay between the electrical activation of the heart and the arrival of a commensurate
pulse wave at a distal point, has shown promise for tracking changes in blood pressure
(see [25] or [29] for an overview of the relationship between PAT and blood pressure).
Therefore, in addition to providing the dataset, this paper presents an initial analysis
investigating the ability of the bed system to track beat-to-beat changes in blood pressure
and associated cardiovascular parameters using BCG-related parameters in comparison
with the better-known PAT model.
2. Materials and Methods
2.1. Bed-Based Ballistocardiography
An early description of, and motivation behind, the design of the bed system used
in this study can be found in [24]. The bed system has been used to compare BCG J-peak
detection algorithms [1], and it was used to investigate the relationship between sleep
and daytime behavior and cognitive function in severely disabled children with autism
who reside at Heartspring [30]. Four electromechanical films (EMFit; L series; 300 mm ×
580 mm—“EMFi” sensors) and four load cells (TE Connectivity Measurement Specialties
FX1901-0001-0200-L—“LC” sensors) acquire a participant’s BCG. Most of the children
at Heartspring are in the lower-functioning portion of the autism spectrum, and it was
unclear if a given child would sleep in a standard resting position (e.g., supine or prone).
Therefore, sensors were placed to encompass a large portion of the bed so as to increase the
likelihood of acquiring quality BCGs regardless of sleeping position; the electromechanical
films were placed in a single, linear column underneath the mattress, and four load cells
were positioned under the respective corner bedposts (see Figures 2 and 3).
Figure 2. Approximate EMFi and load cell locations (not drawn to scale) (left) and the actual film
locations (right), where the mattress has been removed and is leaning on the wall next to the bed.
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Figure 3. Image of the bed system from another angle, accompanied by the CardioCap 5 patient
monitor. The analog conditioning and National Instruments data collection hardware can be seen
under the bed.
The analog signals from each film sensor were amplified and bandpass-filtered between 0.3 and 24 Hz, whereas the analog signals from each load cell were amplified and
bandpass filtered between 0.05 and 35 Hz. Two amplification levels were employed for
load cell signals: one level to accommodate large signals due to subject movement for
estimating center of position (COP) and one level to accommodate smaller, more sensitive
BCG signals related to cardiac activity. A computer running LabVIEW version 14.01 managed a National Instruments (NI) 9184 Ethernet chassis housing two NI 9220 analog input
modules, which were configured to synchronously sample each of the 12 EMFi and LC
analog signals at a sampling rate of 1 kHz. No additional software filtering was applied in
the virtual instrument before these data were saved to files.
2.2. ECG, PPG, and Continuous Blood Pressure Waveforms
The aggregate signal collection acquired with this bed-based system and the accompanying external equipment is depicted in Figure 4. A GE Datex Ohmeda CardioCap 5
vital signs monitor was used to gather three-lead ECGs (bandpass filtered between 0.5
and 40 Hz), an estimated heart rate (HR), impedance respiration signals, and finger PPGs.
Analog output signals from the CardioCap unit delayed at most by 15 ms relative to the
bed sensor data were routed to differential input channels of the same NI 9220 analog input
module that collected the BCG data, meaning these signals were time-aligned with, and
sampled at the same 1 kHz rate as, the BCGs [31]. A Finapres Medical Systems Finometer
PRO was used to gather additional cardiovascular information. The Finometer PRO uses
tonometric principles to continuously monitor an individual’s arterial blood pressure via
a small cuff placed around their finger, and then it derives cardiovascular parameters
from that blood pressure signal on a beat-by-beat basis. This system is non-invasive and
requires an initial calibration from a brachial artery blood pressure cuff. Four analog
output channels from the Finometer PRO were interfaced to a second NI 9220 analog input
module connected to the same NI 9184 Ethernet chassis. The analog signals provided by
the Finometer PRO were reconstructed brachial artery pressure (reBAP), stroke volume
(SV), maximum steepness of the current finger pressure waveform (dP/dtmax ), and the
interbeat interval (IBI), with stroke volume calculated using the ModelFlow method after
correcting for age, sex, weight, and height.
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Figure 4. Signal management and acquisition. COP: center of position.
2.3. Data Collection and Shared Database Structure
Data were collected from 40 participants (17 male) under Kansas State University IRB
protocol #9386. Participant demographics are detailed in Table 1. Participant data were
collated in a MATLAB table, Bed_System_Database, to simplify further processing. The
fields in the MATLAB table include Participant ID, Gender, Age, Height_cm, Weight_kg,
RawData, HeartCondition, and Comments. Prior to data collection, each participant was
asked in a survey, “Have you ever been diagnosed with any cardiac anomalies or diseases
such as atrial fibrillation, arrhythmia, or any other heart conditions? If yes, please list here.”
Participant responses to this survey question are included in the field HeartCondition. A
HeartCondition of ‘N’ means that the participant did not list any heart conditions on the
survey. An image of the MATLAB table, with data visible for ten participants, is illustrated
in Figure 5. Note that, during this process, the raw data were de-identified, and unique
IDs were assigned. For each participant, the data structure of the RawData field was
configured with twenty fields for each acquired signal (PPG, Resp, HR, ECG, Film0, Film1,
Film2, Film3, LC_COP0, LC_BCG0, LC_COP1, LC_BCG1, LC_COP2, LC_BCG2, LC_COP3,
LC_BCG3, reBAP, IBI, SV, and dp_dt). Illustrative raw EMFi and load cell signals collected
from one participant are displayed in Figure 6. Participant ages ranged from 18 to 65 years,
and their body mass indices (BMIs) ranged from 18 to 48 kg/m2 . In total, over 4.5 h of
data were collected. Four of the participants indicated that they had some form of past
or current cardiovascular-related condition: hypertension, supraventricular tachycardia
(addressed by cardiac ablation), atrial fibrillation, and coronary artery disease. These
diagnoses are contained in the MATLAB database table (HeartCondition field). For two of
the participants, load cell 0 did not have good contact with the bed frame and thus did not
collect meaningful BCGs. An additional field, Comments, was added to the database to
include such information.
Table 1. Participant demographics (40 total, 17 male). BMI: body mass index.
Characteristic
Mean +/− Stdev
Age (years)
Weight (kg)
Height (cm)
BMI (kg/m2 )
34 +/− 15
76 +/− 18
171 +/− 11
26 +/− 5.7
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Figure 5. Bed system database excerpt for ten participants.
Figure 6. Representative raw signals collected from participant X1003.
2.4. Signal Preprocessing
In addition to introducing the database, this paper presents the results of an initial
investigation that looked into the connection between BCG and blood pressure related
parameters. Prior to this analysis, the signals were preprocessed using MATLAB version
R2019a. BCGs were bandpass filtered between 1 and 10 Hz to reduce noise and to minimize
the contribution of respiration components. PPGs and reBAPs were lowpass filtered with
a cutoff frequency of 10 Hz. ECGs were bandpass filtered between 1 and 40 Hz. Because
the reBAP signal from the Finometer is a reconstructed signal, it has an overall delay
of 1 s. This delay was removed prior to analysis. Stroke volume (SV) and dP/dtmax
signals from the Finometer PRO also experience a 1-s delay coupled with a 1-beat delay.
In the preprocessed data, SV and dP/dtmax were shifted to compensate for only the 1-s
delay. The processed data used for the analysis are included in a separate MATLAB table,
“Preprocessed_Database.” Note that, in the preprocessed database, the SV and dP/dtmax
metrics still have the 1-beat delay. The analysis presented in this paper compensated for the
1-beat delay during the beat-by-beat feature extraction approach described in the following
section. Both databases can be found on the IEEE DataPort cloud platform [32].
2.5. Initial Analysis—Ballistocardiogram and Blood Pressure Parameter Extraction
Beat-to-beat parameters were extracted from each BCG, PPG, and blood pressure
waveform for approximately 100 cardiac cycles, where ECG R-peaks (located using the PanTompkins method [33]) delineated heart beat cycle boundaries. Noisy or motion corrupted
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data were identified by visual inspection and removed as necessary. Moreover, prior to
feature extraction, BCGs measured from film 0, load cell 0, and load cell 3 were inverted
owing to their relative position to the body compared with BCGs measured from the other
sensors. For each segmented BCG cycle, the most prominent peak that occurred between
100 and 400 ms after the prior ECG R peak was identified as the J peak. The corresponding
I and K features were then determined as the closest local minima prior to and after
the J peak, respectively. The maximum point of the first derivative (estimated using the
MATLAB function, diff()) for the rising edge of a PPG cycle was used to compute pulse
arrival time (PAT) [25]. The maximum and minimum points of a reBAP cycle provided
systolic and diastolic pressures. SV and dP/dtmax metrics were averaged over one cardiac
cycle and then shifted to compensate for the 1-beat delay. Figures 7 and 8 illustrate the
various waveforms and their associated features.
Figure 7. Various cardiopulmonary waveforms and their associated features. The reconstructed
brachial artery pressure (reBAP) waveform acquired by a Finapres Finometer PRO® is scaled at
100 mmHg/volt. The BCG signals were scaled/shifted vertically and preprocessed as described in
Section 2.4. ECG and PPG data are collected with a GE Datex Ohmeda CardioCap 5 patient monitor.
Figure 8. Representative signals collected from the Finapres Finometer PRO® . As in Figure 7, the
reBAP signal is scaled at 100 mmHg/volt. The interbeat interval, stroke volume, and dP/dtmax are
scaled at 1000 ms/volt, 100 mL/volt, and 1 mmHg/s/volt, respectively.
2.6. Relating ECG, PPG, and BCG Extracted Features to Cardiovascular Parameters
Univariate and multivariate analyses were performed to link ECG, PPG, and BCG
features to cardiovascular parameters. Table 2 lists and describes these beat-to-beat parameters, and Table 3 lists the predictive variables and the associated desired responses for the
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multivariate analyses. The univariate model to estimate systolic pressure (SP) from PAT
during cardiac cycle, i, is expressed in Equation (1), where the scalar coefficients, k0 to kn ,
are determined for each participant. The more general multivariate regression model is
expressed in Equation (2), where Responsei represents a general response parameter (see
Table 3, right column) during the cardiac cycle, i. The mn values are scalar coefficients
(also determined for each participant), and BCGpni represents the nth BCG predictor, p (see
Table 3, left column) during the cardiac cycle, i. The relationship between PAT and systolic
blood pressure is inversely proportional, thus PAT estimates are often either inverted
or log-transformed prior to model fitting [25]. For the analysis presented here, the PAT
estimates were log-transformed (the ln () in Equation (1) is a natural logarithm). Given
that the BCG timing parameters have been linked to blood pressure according to similar
principles [5,6], those timing estimates were also log-transformed prior to model fitting. To
smooth out any jitter, a five-wide moving average filter (employing the current value and
the previous four estimates) was applied to the predictor and response variables prior to
solving for the coefficients.
SPi = k0 + k1 ∗ ln( PATi )
(1)
Responsei = m0 + m1 ∗ BCG p1i + . . . + mn ∗ BCG pni
(2)
Table 2. Beat-to-beat electrocardiogram (ECG), photoplethysmogram (PPG), ballistocardiogram
(BCG), and cardiovascular parameters.
Parameter
Description
PAT
IJ time
JK time
IJ amp
JK amp
SP
DP
PP
SV
dP/dtmax
Time delay between the PPG maximum first derivative and ECG R peak
Time delay between the BCG J and I peaks
Time delay between the BCG K and J peaks
Amplitude difference between the BCG I and J peaks
Amplitude difference between the BCG J and K peaks
Systolic blood pressure
Diastolic blood pressure
Pulse pressure (systolic pressure–diastolic pressure)
Stroke volume
Maximal steepness on the upstroke of the finger pressure waveform
Table 3. Predictor(s)–response pairs. SV: stroke volume; SP: systolic pressure; DP: diastolic pressure;
PP: pulse pressure.
Predictor(s)
Response
Pulse Pressure
PAT *
IJ time *
JK amp
IJ time *, IJ amp, JK time *, JK amp
IJ time *, IJ amp, JK time *, JK amp
IJ time *, IJ amp, JK time *, JK amp
IJ time *, IJ amp, JK time *, JK amp
SV
SP
DP
PP
DP
SP
dP/dtmax
SV
* Estimates were log-transformed prior to model fitting.
For the univariate and multivariate approaches, least squares linear regression approaches were employed to fit each model to the corresponding dataset. The BCG parameter(s) were treated as the predictor variable(s)—see Table 3, left column. Cardiovascular
parameters were treated as response variables—see Table 3, right column. The coefficients
of the linear model were then used to compute estimated cardiovascular values (e.g.,
beat-to-beat systolic blood pressures). The Pearson correlation coefficient was computed between the estimated and true beat-to-beat values for each participant using approximately
100 heartbeat intervals.
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3. Results
The metrics in Table 4 were derived from the preprocessed dataset. Average heart rate
varied considerably between participants, ranging from 42 to 92 beats per minute. The
systolic pressure range (max−min) varied from 11 to 46 mmHg, and the diastolic pressure
range varied from 5 to 27 mmHg.
Table 4. Cardiovascular variation.
Cardiovascular Response
Avg. Range (Max−Min) (Mean +/− Stdev)
Systolic Pressure (mmHg)
Diastolic Pressure (mmHg)
Stroke Volume (ml)
dP/dtmax (mHg/s)
21 +/− 8.6
13 +/− 5.0
23 +/− 9.9
0.31 +/− 0.15
Table 5 presents the correlation coefficients for two predictor–response combinations.
Pulse pressure, which is known to be well-correlated to SV [34], did offer a high average correlation coefficient of 0.72 +/− 0.24. The average correlation coefficient for PAT-estimated
systolic blood pressure was 0.48 +/− 0.25. Boxplots illustrating the correlation coefficients
for all 40 participants can be seen in Figure 9.
Table 5. Correlation coefficients for estimated and true cardiovascular parameters averaged across
all participants. PAT: pulse arrival time.
Predictor
Cardiovascular Response
Correlation Coefficient
(Mean +/− Stdev)
PP (Pulse Pressure)
PAT *
Stroke Volume
Systolic Pressure
0.72 +/− 0.24
0.48 +/− 0.25
* Estimates were log-transformed prior to model fitting.
Figure 9. Boxplots of the correlation coefficients for the pulse pressure (PP)–stroke volume (SV) and pulse arrival time
(PAT)–systolic pressure (SP) predictor–response relationships.
Table 6 presents univariate and multivariate model results for each sensor. The
estimated cardiovascular parameters using single BCG predictors (the first two columns
in Table 6) did not correlate well with the paired cardiovascular response parameters; the
average correlation coefficients ranged from 0.17 to 0.29. The bed system did show higher
correlation coefficients when multiple BCG predictors were considered (columns 3 through
6 in Table 6). For Film 0, average correlation coefficients of 0.54 (p < 0.05 for 39 out of 40
participants), 0.51 (p < 0.05 for 40 out of 40 participants), and 0.54 (p < 0.05 for 35 out of 40
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participants) were found when estimating SP, dP/dtmax , and SV, respectively. Figure 10
presents these results in the form of boxplots.
Table 6. Correlation coefficient (mean +/− stdev.) for each response(s)–predictor pair and each sensor. MP: multiple BCG
parameters (IJ time *, JK time *, IJ amp, JK amp). PP: pulse pressure. DP: diastolic pressure. SP: systolic pressure.* Estimates
were log-transformed prior to model fitting.
Sensor
IJ time–DP
JK amp–PP
MP–DP
MP–SP
MP–dP/dtmax
MP–SV
Film 0
Film 1
Film 2
Film 3
Load Cell 0 1
Load Cell 1
Load Cell 2
Load Cell 3
0.25 +/− 0.20
0.25 +/− 0.19
0.22 +/− 0.15
0.29 +/− 0.17
0.20 +/− 0.16
0.20 +/− 0.19
0.24 +/− 0.16
0.23 +/− 0.16
0.26 +/− 0.17
0.22 +/− 0.17
0.23 +/− 0.17
0.21 +/− 0.18
0.17 +/− 0.15
0.19 +/− 0.15
0.22 +/− 0.15
0.23 +/− 0.18
0.44 +/− 0.2
0.43 +/− 0.21
0.44 +/− 0.17
0.44 +/− 0.21
0.44 +/− 0.18
0.41 +/− 0.18
0.45 +/− 0.18
0.40 +/− 0.19
0.54 +/− 0.15
0.52 +/− 0.16
0.49 +/− 0.18
0.52 +/− 0.15
0.51 +/− 0.16
0.51 +/− 0.15
0.53 +/− 0.14
0.50 +/− 0.15
0.51 +/− 0.18
0.50 +/− 0.13
0.48 +/− 0.19
0.49 +/− 0.16
0.50 +/− 0.16
0.48 +/− 0.17
0.49 +/− 0.16
0.49 +/− 0.17
0.53 +/− 0.17
0.54 +/− 0.15
0.52 +/− 0.20
0.48 +/− 0.17
0.52 +/− 0.17
0.54 +/− 0.17
0.53 +/− 0.14
0.54 +/− 0.17
1
Data from participants X1002 and X1003 were excluded from the analysis because of poor signal quality.
Figure 10. Boxplots of the correlation coefficients for the predictor–response pairs described in Table 3. using the BCGs
measured from Film 0. MP: multiple parameters (IJ time, IJ amp, JK time, and JK amp).
4. Discussion
The average PAT correlation coefficient (0.48 +/− 0.25) is slightly lower compared to
other coefficients reported in the literature (0.66 +/− 0.15 [35] (fifteen young and healthy
volunteers); 0.59 [29]). Three participant datasets had a poor-quality or weak-amplitude
PPG (X1019, X1037, and X1046). Removing these three datasets from the analysis improves
the mean PAT correlation coefficient to 0.52 +/− 0.22. Given that the bed system records
BCGs that represent a superposition of the dorsoventral and head-to-foot axes, it seems
reasonable to not expect the same results as seen in other BCG-based blood pressure tracking publications that predominately measure the head-to-foot axial BCG component (i.e.,
standing–BCG measurement systems). In one publication where bed-based BCG features
demonstrated a high correlation with blood pressure, the authors did not consider continuous or beat-to-beat pressures, but instead investigated blood pressure values measured
from a cuff-based system [7]. The dataset presented here makes it possible to perform a
multitude of studies investigating various aspects of bed-based ballistocardiography. This
initial analysis addressed cardiovascular metrics for 40 participants of varying ages and
body types.
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Limitations
Each participant laid in the same position (supine) with their head close to Film
0. However, the exact position of their heart relative to each sensor varied from one
participant to the next, owing in part to variations in participant height. While this lack of
exact positioning may be a form of noise in the data, the dataset does represent the results
of ecologically valid measurements. The same mattress type was used for all participants.
Future studies that contribute signals to this database will consider mattresses of different
types as well as different resting positions.
In other studies that attempted to track blood pressure using features extracted from
BCGs, protocols to modulate blood pressure were often conducted (e.g., mental arithmetic,
breath holding, and exercise; a list of commonly used intervention techniques is given
in [25]). However, our shared dataset includes only endogenous changes in blood pressure
observed as the participants laid on the bed for this study. Still, these changes were at
times considerable—one participant had a systolic pressure range of 46 mmHg for the
window of data analyzed. The average cardiovascular parameter ranges can be seen in
Table 4. Finally, most of the participants had no history of cardiovascular disease. As the
database is expanded, more participants with prior or current cardiovascular conditions
will be recruited.
5. Conclusions
BCG acquisition systems have regained popularity in recent years thanks to their
ability to acquire cardiopulmonary information without requiring user intervention. However, such datasets as acquired and employed by researchers to present new algorithms
or test new theories are not typically made available to the research community. This
paper described a bed-based system to acquire several heart-driven signals, with a goal to
provide a diverse set of heart-related biomedical signals, including ballistocardiograms, to
research teams that wish to develop and test novel algorithms without the need to invest
time and resources toward physical bed hardware and data acquisition equipment. Further,
the results of an initial investigation that assessed the ability of a bed-based BCG to monitor
changes in cardiovascular function were also presented.
Author Contributions: Conceptualization, C.C.; data curation, C.C. and V.-R.T.; formal analysis, C.C.;
investigation, C.C., C.A., S.W. and D.E.T.; methodology, C.C., C.A. and D.E.T.; project administration,
C.C., V.-R.T. and D.E.T.; resources, C.C., V.-R.T., A.S., C.A., S.W. and D.E.T.; writing—original draft,
C.C.; writing—review & editing, C.C., V.-R.T., A.S., C.A., S.W. and D.E.T. All authors have read and
agreed to the published version of the manuscript.
Funding: This research was supported by the National Science Foundation General and Age-Related
Disabilities Engineering (GARDE) Program under grants CBET-1067740 and UNS-1512564, and by
the National Science Foundation Human-Centered Computing (HCC) and Integrative Strategies
for understanding Neural and Cognitive Systems Programs under grant UNS-1512564. Opinions,
findings, conclusions, or recommendations expressed in this material are those of the authors and do
not necessarily reflect the views of the National Science Foundation.
Institutional Review Board Statement: This study was conducted according to the guidelines of the
Declaration of Helsinki, and it was approved by the Kansas State University Institutional Review
Board (protocol number 9386, approved July 5th 2019).
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: The data presented in this study are openly available in IEEE DataPort
at doi:10.21227/77hc-py84, reference number [32].
Acknowledgments: The authors would like to thank Sam Fruth, who helped with data collection.
Conflicts of Interest: The authors declare no conflict of interest.
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