Conference PaperPDF Available

Automated Identification of Persistent Time-Domain Features in Seismocardiogram Signals

Automated Identification of Persistent Time-Domain
Features in Seismocardiogram Signals
Jonathan Zia, Student Member, IEEE, Jacob Kimball, Student Member, IEEE
Md Mobashir Hasan Shandhi, Student Member, IEEE, and Omer T. Inan, Senior Member, IEEE
Abstract—In the field of cardiac monitoring, the seismocar-
diogram (SCG) measures the movement of the chest wall using
accelerometers and gyroscopes. A key limitation of SCG signals
is their sensitivity to transient signal disruptions primarily due
to motion artifacts. This work describes a method for automated
extraction of time-domain features in SCG signals in the presence
of such artifacts, using an iterative method of clustering and
re-sampling features to optimize consistency. The accelerometer
(axl) and gyroscope (gyr) features extracted with this method are
shown to correlate more strongly (median R2= 0.88 (axl), 0.88
(gyr)) with the reference standard for pre-ejection period (PEP),
impedance cardiography (ICG), than both peak-counting (R2=
0.29 (axl), 0.48 (gyr)) and manual labeling (R2= 0.44 (axl), 0.38
(gyr)) in the post-exercise period. This result has implications for
the feasibility of at-home SCG monitoring.
Index Terms—seismocardiography, feature extraction, cardiac
monitoring, automation
The advent of wearable sensing has changed the landscape
of healthcare delivery by enabling the continuous and unob-
trusive monitoring of patients’ health [1]. One modality of
wearable sensing that is being investigated recently is the
seismocardiogram (SCG) measurement—a signal representing
the vibrations of the chest wall in response to the movement
of the heart and blood. Notably, SCG waveform features
have been demonstrated to correlate with cardiac cycle events,
including aortic valve opening (AO) and closing (AC) [2] [3].
Such data can be used to calculate important indicators such as
the pre-ejection period (PEP), defined as the interval between
ventricular depolarization and AO. Continuous monitoring
of such indicators may serve to improve care for patients
suffering from heart disease [4].
A key limitation of the SCG is the presence of transient
disruption, making it difficult to consistently identify sensitive
time-domain features of the waveform [5]. This is especially
true during periods of rapid cardiovascular adaptation—such
as during exercise recovery—or when the patient is moving.
Prior research estimating AO or other points of interest
from SCG waveforms includes abstraction methods such as
using the time-frequency spectrum [6], wavelet decomposition
[7], envelope functions [8], and ensemble-averaged templates
This material is based on work supported by the National Institutes of
Health under Grant 1R01HL130619-A1 and the National Center for Advanc-
ing Translational Sciences of the National Institutes of Health under Award
Number UL1TR002378.
J. Zia, J. Kimball, M.M.H. Shandhi, and O. T. Inan are with the School of
Electrical and Computer Engineering at the Georgia Institute of Technology,
Atlanta, GA 30332 USA (email:
[9] among others. Peak-counting methods have been quite
prevalent, namely selecting numbered peaks from the wave-
form to represent cardiac events [10]. Finally, sensor fusion—
especially across modalities—has emerged as a method of
improving SCG feature extraction [11]. While effective in
predicting cardiac cycle events, such methods may be imprac-
tical in noisy environments. In fact, prior work demonstrating
correlation between SCG and cardiac events has often relied
on manual labeling, which is prone to human error. Conse-
quently, methods that utilize supervised training algorithms are
inherently limited by the efficacy of manual labeling. However,
these results suggest that if SCG features could be extracted
consistently, changes in these features would strongly correlate
with changes in cardiac events or indicators such as PEP
[12]. By extension, this would improve the feasibility of SCG
monitoring in at-home environments.
This work presents a method for identifying time-domain
features of the SCG segments while ignoring transient disrup-
tions in waveform morphology. Rather than identifying fiducial
points, this method extracts features based on their consistency,
and optionally their covariance with PEP. The desired features
in this study include the first and second consistent minima and
maxima of the SCG interval—which are frequently correlated
with the AO point—however, the approach may generalize to
any time-domain features. Figure 1(a) illustrates that when
these features are extracted using a simple peak-counting
method—returning the first two local minima and maxima—
transient disruptions can result in feature corruption.
A. Experimental Protocol
A total of 17 healthy subjects (10 males, 7 females, Age:
26.1 ±4.1 years, Weight: 66.2 ±13.6 kg, and Height: 168.2 ±
8.9 cm) with no history of heart disease participated in a study
conducted under a protocol approved by the Georgia Institute
of Technology Institutional Review Board [13]. The purpose
of this study was to induce changes in PEP while monitoring
changes in SCG. To this end, a three-axis accelerometer and
gyroscope were placed on the subject’s sternum to collect SCG
waveforms, along with reference electrocardiogram (ECG) and
impedance cardiogram (ICG) sensors. After standing vertically
and motionless for five minutes, the subject performed three
minutes of walking at three miles per hour on a treadmill
followed by 90 seconds of a squatting exercise. Subsequently,
the subject again stood vertically and motionless for a five-
minute recovery period.
Raw Annotated Features using Peak-Counting
Time (ms)
25 50 75 100 125 150
Acceleration (m/s2)
- 20
- 15
- 10
- 5
- 25
Feature Selection using Algorithm 1
Time (ms)
25 50 75 100 125 150
0175 200
Acceleration (m/s2)
- 20
- 15
- 10
- 5
- 25
(a) (b)
Fig. 1. (a) Raw features for a SCG signal during post-exercise period. Features include first and second minima (black, blue), and first and second maxima
(red, green). (b) Updated features after using Algorithm 1 on signals in (a). Notably, finalized features ignore transient peaks in the range 0 - 50 ms.
B. Signal Processing and Feature Extraction
Signal Processing: The data used in this study was limited
to the recovery period due to the subjects’ rapidly-changing
hemodynamic state during this interval. All signals were fil-
tered with a finite impulse response (FIR) band-pass filter with
Kaiser window. Cutoff frequencies were: 1-40 Hz for SCG, 1-
30 Hz for ICG, and 0.5-40 Hz for ECG. These signals were
then segmented into beat-by-beat intervals using the R-peaks
on the reference ECG signal. Segments for each modality were
then smoothed with an exponential moving average filter with
a window of five beats [14].
ICG Feature Extraction: The reference standard for the
aortic opening (AO) point in this study is the B-point of the
ICG waveform, identified as the point of maximal inflection
of the wave in the period preceding the signal peak [15]. Since
ICG is a measure of intra-thoracic bloodflow, the inflection of
the waveform is coupled with ejection of blood through the
aortic valve during systole. Thus, the R-B period serves as a
correlate to the true PEP.
Manual SCG Labeling: Performance of the automated al-
gorithm presented below is compared against manual feature
extraction. For this task, a trained annotator was instructed to
mark the first and second minima and maxima of the SCG
waveform, choosing peaks consistently between intervals and
ignoring transient disturbances [1]. To aid in this, the annotator
was presented with a reference SCG waveform, which was the
ensemble-average of signal segments for each trial.
C. Automated SCG Feature Identification
For both the accelerometer- and gyroscope-based SCG data,
an algorithm was developed for identifying the set of time-
domain features of interest (the first and second minima and
maxima following the ECG R-peak), while ignoring transient
or aberrant peaks that are not present throughout the entirety
of the signal. The feature extraction algorithm is summarized
in Algorithm 1.
Each feature is first extracted using the peak-counting
method described above. In the presence of aberrant peaks,
the raw features may be shifted in time in adjacent seg-
ments, resulting in the apparent clustering of features into
distinct distributions as shown in Figure 2(a). To identify
these distributions, Gaussian mixture modeling (GMM) is
used to determine the probability that each sample belongs
in one of up to Mdistributions, with each sample being
clustered according to the distribution to which it most likely
belongs. Note that, though the data in Figure 2(a) is linearly
separable, this is not always the case — thus, a clustering
algorithm was chosen rather than a linear classifier due to
its superior generalizability. The optimal number of clusters
Algorithm 1 SCG Feature Extraction
1: procedure GET FEATU RE S(M , N, F)
2: for fFdo Loop through features
3: Obtain raw feature vector rf
4: P0Initialize performance
5: while P > 0do
6: for n= 1 Ndo Order of b.f. line
7: Cluster rfinto set Cs.t. |C|=M?M
8: Generate best-fit
bm,n for CmC
9: Resample rfalong
bm,n to obtain sm,n
10: Calculate performance pm,n of sm,n
11: m?, n?argmaxm,n p
12: Ppm?,n?P
13: if P > 0then
14: rfsm?,n?
15: P=pm?,n?
16: return R:={r1...r|F|}
Gaussian Mixture Model of Raw SCG Features
Normalized Segment
Normalized Amplitude
- 1.5
- 1.0
- 0.5
-1.5 -1.0 -0.5 00.5 1.0
-2 1.5 2.0
Updated Feature Vector after Re-Sampling
100 200 300 400
Scaled Amplitude
Signal Segment
(a) (b)
Fig. 2. (a) Clustering of features corresponding to the first minimum of SCG segments for signals shown in Figure 1(a). Features assigned to clusters 1 and
2 are shown in black and red respectively, along with the contour of the Gaussian model associated with each cluster. (b) Updated feature vector (blue line)
after the first iteration of re-sampling the raw vector (blue area). Best-fit lines corresponding to the two clusters in (a) are overlaid (black, dashed), with the
updated vector corresponding to re-sampling along the uppermost line. The PEP derived from the ICG B-point is provided (green area).
M?is determined by performing GMM iteratively on the raw
feature vector, and selecting the number that yields the highest
average silhouette score, defined as:
max(β(i), α(i)) (1)
for cluster Cin the set of all clusters C. In (1), β(i)is the
average distance between sample iand points in the next-
nearest cluster, and α(i)is the average distance between iand
points in the same cluster. As rfis a time-series, separation
in time does not contribute to distance in (1). An example
of performing GMM on the data in Figure 1(a) is shown in
Figure 2(a). Note that such clustering accounts for the natural
shift in feature values during the recovery period.
Features should be drawn from the distribution that results
in a feature vector with the lowest variability, assuming the
relative beat-by-beat consistency of cardiac parameters. To
determine the proper distribution, Nbest-fit lines of poly-
nomial order 1 to Nare fit to the samples in each cluster.
The original vector rfis then re-sampled such that, of the
candidate features in the signal, the feature closest to the
expected value given by the best-fit
bm,n is chosen. Naturally,
distributions that represent inconsistent features will result in
higher variability. The performance of each re-sampled vector
sm,n (for cluster mof best-fit order n) is thus calculated as:
pm,n =1
where ¯σm,n is the sample-averaged expected standard devi-
ation of
bm,n. The highest-performing vector is then chosen
to update the original feature vector rf, and the process is
iteratively performed until convergence upon the optimal set
of features. Example results are shown in Figures 1(b) and
2(b) when the process is applied to the data in Figure 2(a).
The process described in Algorithm 1 was used to extract
the aforementioned time-domain features from accelerometer-
and gyroscope-based SCG signals for 16 of the 17 subjects
in the study, as the reference ECG for one of the subjects
was not usable. Each set of features was then compared to the
PEP estimated from the corresponding ICG signal. Quality of
the feature was assessed using the coefficient of determination
(R2), which quantifies how much variance in one signal is
explained by the variance in the other. The R2equation used
in this study was adjusted for the number of observations:
R2= 1 n1
npSS E
SS T (3)
where nis the number of observations, pis the number of
polynomial coefficients, SSE is the sum squared error, and
SS T is the sum squared total.
The results of this analysis are shown in Figure 3. The
automated algorithm significantly outperformed both manual
labeling and raw (peak-counting) feature extraction in identi-
fying the desired features. For the accelerometer, median R2
was 0.88 (IQR: 0.77 - 0.92) for automated features, 0.44 (0.30
- 0.63) for manual features, and 0.29 (0.15 - 0.36) for raw
features. For the gyroscope, median R2was 0.88 (0.81 - 0.91)
for automated features, 0.38 (0.22 - 0.56) for manual features,
and 0.48 (0.24 - 0.77) for raw features.
Automated features notably had both a higher mean R2
and lower IQR than the other labeling methods. Furthermore,
gyroscope features had a comparable correlation coefficient
Fig. 3. Performance of Algorithm 1 in feature extraction, reported as average
R2between the time domain features and PEP derived from ICG. Green
boxes represent accelerometer data and blue boxes represent gyroscope data.
with the PEP estimate than accelerometer features. These
results suggest that the automated algorithm was more effec-
tive in identifying consistent features than manual labeling
and raw feature extraction, and supports recent works that
present gyroscopes as a viable alternative to accelerometers
for SCG. Since manual labeling is a very time consuming
task; automating such tasks would greatly benefit researchers
in this field.
As the purpose of this approach is to identify the most
consistent features in the signal set, it is not intended as a
method of AO point or PEP estimation, but rather identifying
consistent features that co-vary with these metrics of interest.
This is most useful in domains such as adverse event pre-
diction, where the patient’s relative deviation from baseline
PEP is more important than the value of PEP itself. Equation
(2) may thus be modified to select for feature vectors that best
achieve this, for example defining performance as R2between
the feature and the reference standard for AO.
Though the ICG B-point was used as the reference standard,
echocardiogram is the gold-standard for AO point identifica-
tion. Though SCG has been used extensively for this purpose,
a universal translation of SCG features to cardiac cycle events
has yet to be confirmed, and thus event prediction from SCG
often relies on additional mapping of feature vectors to event
timing that may not generalize. For this reason, perhaps the
most judicious use of SCG in cardiac monitoring is to predict
relative rather than absolute event timings.
A possible limitation of this study is that the automated
algorithm was tested against two trained annotators; future
work should test the automated approach against a larger set
of annotators to generalize results.
The method presented in this work has the benefit of
selecting features based on the entire five-minute span of data,
however it could be modified to perform this analysis in real-
time settings. Specifically, the Gaussian mixture model may be
updated dynamically as data becomes available, and feature
selection may incorporate both past and present data, such
as with Kalman filtering. This would be useful as part of
a larger system for continuous monitoring that also includes
signal quality verification and noise reduction components.
Future work should extend this method to real-time systems
for continuous monitoring in at-home environments. This
method should also be evaluated directly against prior methods
of SCG feature extraction.
[1] O. T. Inan, P.-F. Migeotte, K.-S. Park, M. Etemadi, K. Tavakolian, R.
Casanella, J. Zanetti, J. Tank, I. Funtova, G. K. Prisk, and M. D. Rienzo,
“Ballistocardiography and Seismocardiography: A Review of Recent
Advances,IEEE Journal of Biomedical and Health Informatics, vol.
19, no. 4, pp. 1414-1427, 2015.
[2] R. S. Crow, P. Hannan, D. Jacobs, L. Hedquist, and D. M. Salerno, “Re-
lationship between Seismocardiogram and Echocardiogram for Events
in the Cardiac Cycle,” American Journal of Noninvasive Cardiology,
vol. 8, no. 1, pp. 39-46, 1994.
[3] M. J. Tadi, E. Lehtonen, A. Saraste, J. Tuominen, J. Koskinen, M. Ters, J.
Airaksinen, M. Pnkl, and T. Koivisto, “Gyrocardiography: A New Non-
invasive Monitoring Method for the Assessment of Cardiac Mechanics
and the Estimation of Hemodynamic Variables,” Scientific Reports, vol.
7, no. 1, 2017.
[4] J. M. Zanetti and K. Tavakolian, “Seismocardiography: Past, present and
future,” 35th Annual International Conference of the IEEE Engineering
in Medicine and Biology Society (EMBC), 2013.
[5] A. Q. Javaid, H. Ashouri, A. Dorier, M. Etemadi, J. A. Heller, S.
Roy, and O. T. Inan, “Quantifying and Reducing Motion Artifacts in
Wearable Seismocardiogram Measurements During Walking to Assess
Left Ventricular Health,” IEEE Transactions on Biomedical Engineering,
vol. 64, no. 6, pp. 1277-1286, 2017.
[6] A. Taebi and H. Mansy, “Time-Frequency Distribution of Seismocardio-
graphic Signals: A Comparative Study,” Bioengineering, vol. 4, no. 4,
p. 32, 2017.
[7] T. Choudhary, L. N. Sharma, and M. K. Bhuyan, “Automatic Detection
of Aortic Valve Opening using Seismocardiography in Healthy Individ-
uals,” IEEE Journal of Biomedical and Health Informatics, pp. 1–1,
[8] F. Khosrow-Khavar, K. Tavakolian, A. Blaber, and C. Menon, “Auto-
matic and Robust Delineation of the Fiducial Points of the Seismocar-
diogram Signal for Noninvasive Estimation of Cardiac Time Intervals,
IEEE Transactions on Biomedical Engineering, vol. 64, no. 8, pp. 1701-
1710, 2017.
[9] G. Shafiq, S. Tatinati, W. T. Ang, and K. C. Veluvolu, “Automatic
Identification of Systolic Time Intervals in Seismocardiogram,Scientific
Reports, vol. 6, no. 1, 2016.
[10] M. D. Rienzo, E. Vaini, and P. Lombardi, “Use of seismocardiogram
for the beat-to-beat assessment of the Pulse Transit Time: A pilot
study,” 37th Annual International Conference of the IEEE Engineering
in Medicine and Biology Society (EMBC), 2015.
[11] C. Yang, S. Tang, and N. Tavassolian, “Annotation of seismocardiogram
using gyroscopic recordings,” IEEE Biomedical Circuits and Systems
Conference (BioCAS), 2016.
[12] H. Ashouri, S. Hersek, and O. T. Inan, “Universal Pre-Ejection Period
Estimation Using Seismocardiography: Quantifying the Effects of Sen-
sor Placement and Regression Algorithms,” IEEE Sensors Journal, vol.
18, no. 4, pp. 1665–1674, 2018.
[13] M. Shandhi, B. Semiz, S. Hersek, N. Goller, F. Ayazi, and O. T. Inan,
“Performance Analysis of Gyroscope and Accelerometer Sensors for
Seismocardiography-Based Wearable Pre-Ejection Period Estimation,
IEEE Journal of Biomedical and Health Informatics, 2019, In Press.
[14] M. Etemadi, O. T. Inan, L. Giovangrandi, and G. T. A. Kovacs, “Rapid
Assessment of Cardiac Contractility on a Home Bathroom Scale,” IEEE
Transactions on Information Technology in Biomedicine, vol. 15, no. 6,
pp. 864–869, 2011.
[15] A. Sherwood, M. T. Allen, J. Fahrenberg, R. M. Kelsey, W. R. Lovallo,
and L. J. P. Vandoornen, “Methodological Guidelines for Impedance
Cardiography,Psychophysiology, vol. 27, no. 1, pp. 1–23, 1990.
... Gyrocardiography is defined in [15] as a local pulses signal, obtained by placing a gyroscope in the place of an accelerometer in seismocardiography [6]. The works published in 2019 on gyrocariography describe new topics and already known topics, such as the heart beat detection [14], detection of atrial fibrillation [60,61], hemodynamics analysis [62][63][64], pulse transit time measurement [65] and respiratory and cardiac gating [66]. The review of the state of seismocardiography by A. Taebi et al. in 2019 reveals that the analysis of rotational vibrations may provide complementary information to the SCG signal analysis [67]. ...
Full-text available
Gyrocardiography (GCG) is a non-invasive technique of analyzing cardiac vibrations by a MEMS (micro electromechanical) gyroscope placed on a chest wall. Although its history is short in comparison with seismocardiography (SCG) and electrocardiography (ECG), GCG becomes a technique which may provide additional insight into the mechanical aspects of the cardiac cycle. In this review, we describe the summary of the history, definition, measurements, waveform description and applications of gyrocardiography. The review was conducted on about 55 works analyzed between November 2016 and September 2020. The analyzed works demonstrate the definition of GCG, waveform annotation, several applications of the gyrocardiography, including, heart rate estimation, heart rate variability analysis, hemodynamics analysis, classification of various cardiac diseases.
... Gyrocardiography is defined in [22] as a local pulses signal, obtained by placing a gyroscope in the place of an accelerometer in seismocardiography [8]. The works published in 2019 on gyrocariography describe new topics and already known topics, such as the heart beat detection [9], detection of atrial fibrillation [64,65], hemodynamics analysis [42,66,67], pulse transit time measurement [68] and respiratory and cardiac gating [69]. The review of the state of seismocardiography by A. Taebi et al. in 2019 reveals that the analysis of rotational vibrations may provide complementary information to the SCG signal analysis [70]. ...
Full-text available
Gyrocardiography (GCG) is a non-invasive technique of analyzing cardiac vibrations by a MEMS (microelectromechanical system) gyroscope placed on a chest wall. Although its history is short in comparison with seismocardiography (SCG) and electrocardiography (ECG), GCG becomes a technique which may provide additional insight into the mechanical aspects of the cardiac cycle. In this review, we describe the summary of the history, definition, measurements, waveform description and applications of gyrocardiography. The review was conducted on about 55 works analyzed between November 2016 and September 2020. The aim of this literature review was to summarize the current state of knowledge in gyrocardiography, especially the definition, waveform description, the physiological and physical sources of the signal and its applications. Based on the analyzed works, we present the definition of GCG as a technique for registration and analysis of rotational component of local cardiac vibrations, waveform annotation, several applications of the gyrocardiography, including, heart rate estimation, heart rate variability analysis, hemodynamics analysis, and classification of various cardiac diseases.
... Firstly, the radar signal is more susceptible to interferences from the surroundings and consequently tend to be noiser. Secondly, in previous researches, SCG is generally analyzed along with ECG [9]- [13], whose R-wave serves as the reference signal of cardiac cycles. However, for a thoroughly non-contact system, the assist from ECG signal will no longer be available. ...
This research aims to propose a standalone systolic profile (SP) detection method that could locate systolic profiles without referring to concurrent R-peaks of electrocardiogram (ECG) signal. This technique is specifically used to facilitate the study of seismocardiogram (SCG) obtained by noncontact sensing methods, such as radar acceleration waveform (RAW). In our research, a single-layer bi-directional long short-term memory (LSTM) network with 300 hidden units is used to distinguish SP, which is the part of SCG that depicts the phase of systole, including fiducial points such as isovolumic contraction (IM) and aortic valve opening (AO) points. The network is trained on a semi-automatically constructed dataset that consists of 2115 positive segments (PSs) and 2115 negative segments (NSs) with the length of 256 milliseconds. The trained network is applied to RAW signal in the form of a sliding window, judging whether each segment is SP. The performance of the proposed approach is evaluated using the prediction accuracy of SP and average inter-beat intervals (IBIs) on 6 subjects. The results show that this approach is promising in replacing the ECG R-peaks as the reference signal for clean or partially noisy RAWs. In addition, the proposed approach demonstrates better performance than the existing heart rate envelope-based methods.
Full-text available
Seismocardiogram (SCG) is a low-cost monitoring method to collect precordial vibrations of sternum due to heartbeats and evaluate cardiac activity. It is mostly used as an auxiliary measurement to the other monitoring methods; however, it carries significant patterns reflecting current cardiovascular health status of subjects. If it is properly collected within a non-clinical environment, it might be able to present preliminary data to physicians before clinic. SCG signals are morphologically noisy. These signals store excessive amount of data. Extracting significant information corresponding to heartbeat complexes is so important. Previously, the method called compressed sensing (CS) had been applied to weed up the redundant information by taking the advantage of sparsity feature in a study. This compressed sensing is based on storing significant signals below the Nyquist rate which suffice for medical diagnosis. It has been feasible to compress SCG signals with 3:1 compression rate at least while maintaining accurate signal reconstruction. Nevertheless, higher compression rates lead to the formation of artifacts on reconstructed signals. This limits a more aggressive compression to reduce the amount of data. The requirement of a different approach which will allow higher compression rates and lower loss of information arises. The purpose of this study is to obtain more competent results by using a method called predefined signature and envelope vector sets (PSEVS) which has been satisfyingly applied to electrocardiogram (ECG) and speech signals. In the study, simultaneously recorded ECG and SCG signals were modeled with the method called PSEVS. The reconstructed signals were compared to the original signals so as to investigate the efficacy of signature-based modeling methods in constructing medically remarkable biosignals for clinical use. After examining the components of reconstructed signals called frame-scaling coefficient, signature and envelope vectors, it has been seen that the error function values of envelope vectors differ from expected values. We concluded that reconstructed SCG signals were not adequate for medical diagnosis.
Full-text available
As the leading cause of trauma-related mortality, blood loss due to hemorrhage is notoriously difficult to triage and manage. To enable timely and appropriate care for patients with trauma, this work elucidates the externally measurable physiological features of exsanguination, which were used to develop a globalized model for assessing blood volume status (BVS) or the relative severity of blood loss. These features were captured via both a multimodal wearable system and a catheter-based reference and used to accurately infer BVS in a porcine model of hemorrhage ( n = 6). Ultimately, high-level features of cardiomechanical function were shown to strongly predict progression toward cardiovascular collapse and used to estimate BVS with a median error of 15.17 and 18.17% for the catheter-based and wearable systems, respectively. Exploring the nexus of biomedical theory and practice, these findings lay the groundwork for digital biomarkers of hemorrhage severity and warrant further study in human subjects.
Full-text available
Accurate detection of fiducial points in a seismocardiogram (SCG) is a challenging research problem for its clinical application. In this paper, an automated method for detecting aortic valve opening (AO) instants using the dorsoventral component of SCG signal is proposed. This method does not require electrocardiogram (ECG) as a reference signal. After pre-processing the SCG, multiscale wavelet decomposition is carried out to get signal components in different wavelet subbands. The subbands having possible AO peaks are selected by a newly proposed dominant multiscale kurtosis (DMK) and dominant multiscale central frequency (DMCF) based criteria. The signal is reconstructed using selected subbands, and it is emphasized using the weights derived from proposed relative squared dominant multiscale kurtosis (RSDMK). The Shannon energy (SE) followed by autocorrelation coefficients are computed for systole envelope construction. Finally, AO peaks are detected by a Gaussian derivative filtering based scheme. The robustness of the proposed method is tested using clean and noisy SCG signals from CEBS database. Evaluation results show that the method can achieve an average sensitivity (Se) of 94%, prediction rate (+P) of 90% and detection accuracy (ACC) of 86% approximately over 4585 analyzed beats.
Full-text available
Gyrocardiography (GCG) is a new non-invasive technique for assessing heart motions by using a sensor of angular motion – gyroscope – attached to the skin of the chest. In this study, we conducted simultaneous recordings of electrocardiography (ECG), GCG, and echocardiography in a group of subjects consisting of nine healthy volunteer men. Annotation of underlying fiducial points in GCG is presented and compared to opening and closing points of heart valves measured by a pulse wave Doppler. Comparison between GCG and synchronized tissue Doppler imaging (TDI) data shows that the GCG signal is also capable of providing temporal information on the systolic and early diastolic peak velocities of the myocardium. Furthermore, time intervals from the ECG Q-wave to the maximum of the integrated GCG (angular displacement) signal and maximal myocardial strain curves obtained by 3D speckle tracking are correlated. We see GCG as a promising mechanical cardiac monitoring tool that enables quantification of beat-by-beat dynamics of systolic time intervals (STI) related to hemodynamic variables and myocardial contractility.
Full-text available
Accurate estimation of seismocardiographic (SCG) signal features can help successful signal characterization and classification in health and disease. This may lead to new methods for diagnosing and monitoring heart function. Time-frequency distributions (TFD) were often used to estimate the spectrotemporal signal features. In this study, the performance of different TFDs (e.g., short-time Fourier transform (STFT), polynomial chirplet transform (PCT), and continuous wavelet transform (CWT) with different mother functions) was assessed using simulated signals, and then utilized to analyze actual SCGs. The instantaneous frequency (IF) was determined from TFD and the error in estimating IF was calculated for simulated signals. Results suggested that the lowest IF error depended on the TFD and the test signal. STFT had lower error than CWT methods for most test signals. For a simulated SCG, Morlet CWT more accurately estimated IF than other CWTs, but Morlet did not provide noticeable advantages over STFT or PCT. PCT had the most consistently accurate IF estimations and appeared more suited for estimating IF of actual SCG signals. PCT analysis showed that actual SCGs from eight healthy subjects had multiple spectral peaks at 9.20 ± 0.48, 25.84 ± 0.77, 50.71 ± 1.83 Hz (mean ± SEM). These may prove useful features for SCG characterization and classification.
Full-text available
Continuous and non-invasive monitoring of hemodynamic parameters through unobtrusive wearable sensors can potentially aid in early detection of cardiac abnormalities, and provides a viable solution for long-term follow-up of patients with chronic cardiovascular diseases without disrupting the daily life activities. Electrocardiogram (ECG) and siesmocardiogram (SCG) signals can be readily acquired from light-weight electrodes and accelerometers respectively, which can be employed to derive systolic time intervals (STI). For this purpose, automated and accurate annotation of the relevant peaks in these signals is required, which is challenging due to the inter-subject morphological variability and noise prone nature of SCG signal. In this paper, an approach is proposed to automatically annotate the desired peaks in SCG signal that are related to STI by utilizing the information of peak detected in the sliding template to narrow-down the search for the desired peak in actual SCG signal. Experimental validation of this approach performed in conventional/controlled supine and realistic/challenging seated conditions, containing over 5600 heart beat cycles shows good performance and robustness of the proposed approach in noisy conditions. Automated measurement of STI in wearable configuration can provide a quantified cardiac health index for long-term monitoring of patients, elderly people at risk and health-enthusiasts.
Objective: Systolic time intervals, such as the pre-ejection period (PEP), are important parameters for assessing cardiac contractility that can be measured non-invasively using seismocardiography (SCG). Recent studies have shown that specific points on accelerometer- and gyroscope-based SCG signals can be used for PEP estimation. However, the complex morphology and inter-subject variation of the SCG signal can make this assumption very challenging and increase the root mean squared error (RMSE) when these techniques are used to develop a global model. Methods: In this study, we compared gyroscope- and accelerometer-based SCG signals, individually and in combination, for estimating PEP to show the efficacy of these sensors in capturing valuable information regarding cardiovascular health. We extracted general time-domain features from all the axes of these sensors and developed global models using various regression techniques. Results: In single-axis comparison of gyroscope and accelerometer, angular velocity signal around head to foot axis from the gyroscope provided the lowest RMSE of 12.63 ± 0.49 ms across all subjects. The best estimate of PEP, with a RMSE of 11.46 ± 0.32 ms across all subjects, was achieved by combining features from the gyroscope and accelerometer. Our global model showed 30% lower RMSE when compared to algorithms used in recent literature. Conclusion: Gyroscopes can provide better PEP estimation compared to accelerometers located on the mid-sternum. Global PEP estimation models can be improved by combining general time domain features from both sensors. Significance: This work can be used to develop a low-cost wearable heart-monitoring device and to generate a universal estimation model for systolic time intervals using a single- or multiple-sensor fusion.
Seismocardiography (SCG), the measurement of local chest vibrations due to the heart and blood movement, is a non-invasive technique to assess cardiac contractility via systolic time intervals such as the pre-ejection period (PEP). Recent studies show that SCG signals measured before and after exercise can effectively classify compensated and decompensated heart failure (HF) patients through PEP estimation. However, the morphology of the SCG signal varies from person to person and sensor placement making it difficult to automatically estimate PEP from SCG and electrocardiogram signals using a global model. In this proof-of-concept study, we address this problem by extracting a set of timing features from SCG signals measured from multiple positions on the upper body. We then test global regression models that combine all the detected features to identify the most accurate model for PEP estimation obtained from the best performing regressor and the best sensor location or combination of locations. Our results show that ensemble regression using XGBoost with a combination of sensors placed on the sternum and below the left clavicle provide the best RMSE= 11.6 ±0.4 ms across all subjects. We also show that placing the sensor below the left or right clavicle rather than the conventional placement on the sternum results in more accurate PEP estimates. IEEE
Objective: The purpose of this research was to design a delineation algorithm that could detect specific fiducial points of the seismocardiogram (SCG) signal with or without using the electrocardiogram (ECG) R-wave as the reference point. The detected fiducial points were used to estimate cardiac time intervals. Due to complexity and sensitivity of the SCG signal, the algorithm was designed to robustly discard the low-quality cardiac cycles, which are the ones that contain unrecognizable fiducial points. Method: The algorithm was trained on a dataset containing 48,318 manually annotated cardiac cycles. It was then applied to three test datasets: 65 young healthy individuals (dataset 1), 15 individuals above 44 years old (dataset 2), and 25 patients with previous heart conditions (dataset 3). Results: The algorithm accomplished high prediction accuracy with the rootmean- square-error of less than 5 ms for all the test datasets. The algorithm overall mean detection rate per individual recordings (DRI) were 74, 68, and 42 percent for the three test datasets when concurrent ECG and SCG were used. For the standalone SCG case, the mean DRI was 32, 14 and 21 percent. Conclusion: When the proposed algorithm applied to concurrent ECG and SCG signals, the desired fiducial points of the SCG signal were successfully estimated with a high detection rate. For the standalone case, however, the algorithm achieved high prediction accuracy and detection rate for only the young individual dataset. Significance: The presented algorithm could be used for accurate and non-invasive estimation of cardiac time intervals.
Conference Paper
We propose a new methodology for the estimation of Pulse Transit Time, PTT, based on the use of the seismocardiogram for the identification of the aortic valve opening, AO. This method has been implemented to obtain a first description of the AO-derived PTT beat-to-beat variability at rest and during the recovery after a cycloergometer exercise at 25W and 100W, its relation with systolic blood pressure, S(BP), and its difference with respect to variability of the Pulse Arrival Time, PAT (i.e. the BP transit time estimated by considering the ECG R peak instead of AO as proximal site). Our preliminary data indicate that 1) the fast components of the PTT variability are only marginally influenced by respiration; 2) only the slower components of the PTT variability are correlated with systolic BP; 3) major differences exist in the dynamics of PTT and PAT, being PAT variability significantly larger and importantly influenced by the beat-to-beat changes occurring in the Pre Ejection Period.
Goal: Our objective is to provide a framework for extracting signals of interest from the wearable seismocardiogram (SCG) measured during walking at normal (subject's preferred pace) and moderately-fast (1.34 - 1.45 m/s) speeds. Methods: We demonstrate, using empirical mode decomposition (EMD) and feature tracking algorithms, that the pre-ejection period (PEP) can be accurately estimated from a wearable patch that simultaneously measures electrocardiogram (ECG) and sternal acceleration signals. We also provide a method to determine the minimum number of heartbeats required for an accurate estimate to be obtained for the PEP from the accelerometer signals during walking. Results: The EMD-based de-noising approach provides a statistically significant increase in the signal-to-noise ratio (SNR) of wearable SCG signals and also improves estimation of PEP during walking. Conclusion: The algorithms described in this paper can be used to provide hemodynamic assessment from wearable SCG during walking. Significance: A major limitation in the use of the SCG, a measure of local chest vibrations caused by cardiac ejection of blood in the vasculature, is that a user must remain completely still for high quality measurements. The motion can create artifacts and practically render the signal unreadable. Addressing this limitation could allow, for the first time, SCG measurements to be obtained reliably during movement-aside from increasing the coverage throughout the day of cardiovascular monitoring, analyzing SCG signals during movement would quantify the cardiovascular system's response to stress (exercise), and thus provide a more holistic assessment of overall health.