Cardiovascular Function and Ballistocardiogram: A Relationship Interpreted via Mathematical Modeling

Guidoboni, Giovanna; Sala, Lorenzo; Enayati, Moein; Sacco, Riccardo; Szopos, Marcela; Keller, James M.; Popescu, Mihail; Despins, Laurel; Huxley, Virginia H.; Skubic, Marjorie

doi:10.1109/tbme.2019.2897952

Cited by 44 publications

(65 citation statements)

References 57 publications

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“…The effectiveness of the WFMR may be evaluated in cohorts with different health conditions to further validate the efficacy of the feature to generalize. Lastly, the source of morphology fluctuation, as well as the morphology alteration, may be identified with the help of mathematical models, where the matching the waveforms by tuning parameters may narrow down the source of observations made in this work [35] , [36] .…”

Section: Discussionmentioning

confidence: 99%

Quantification of Resting-State Ballistocardiogram Difference Between Clinical and Non-Clinical Populations for Ambient Monitoring of Heart Failure

Chang

Mak

Armanfard

et al. 2020

IEEE J. Transl. Eng. Health Med.

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A ballistocardiogram (BCG) is a versatile bio-signal that enables ambient remote monitoring of heart failure (HF) patients in a home setting, achieved through embedded sensors in the surrounding environment. Numerous methods of analysis are available for extracting physiological information using the BCG; however, most have been developed based on non-clinical subjects. While the difference between clinical and non-clinical populations are expected, quantification of the difference may serve as a useful tool. In this work, the differences in resting-state BCGs of the two cohorts in a sitting posture were quantified. An instrumented chair was used to collect the BCG from 29 healthy adults and 26 NYHA HF class I and II patients while seated without any stress test for five minutes. Five 20-second epochs per subject were used to calculate the waveform fluctuation metric at rest (WFMR). The WFMR was obtained in two steps. The ensemble average of the segmented BCG heartbeats within an epoch were calculated first. Mean square errors (MSE) between different ensemble average pairs were then retrieved. The MSEs were averaged to produce the WFMR. The comparison showed that the clinical cohort had higher fluctuation than the non-clinical population and had at least 82.2% separation, suggesting that greater errors may result when existing algorithms were used. The WFMR acts as a bridge that may enable important features, including the addition of error margins in parameter estimation and ways to devise a calibration strategy when resting-state BCG is unstable.

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Section: Discussionmentioning

confidence: 99%

Quantification of Resting-State Ballistocardiogram Difference Between Clinical and Non-Clinical Populations for Ambient Monitoring of Heart Failure

Chang

Mak

Armanfard

et al. 2020

IEEE J. Transl. Eng. Health Med.

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“…Substituting the N(s) TF coefficients expressed in equation (23) and equation (24) into equation (34), we have the relation:…”

Section: ) Analysis At Very Low Frequenciesmentioning

confidence: 99%

“…Advanced design with deep learning-based health electronic wearable sensors is introduced in [22]. Despite the progress of these techniques of distortion cancellation, more efficient and simpler methods allowing to avoid the circuit modelling [23], [24] based method can be developed.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction Technique of Distorted Sensor Signals with Low-Pass NGD Function

et al. 2020

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This paper develops a new reconstruction technique of undesirable signal distortion generated by sensor electronic circuits. The introduced reconstruction technique is originally realized with unfamiliar low-pass negative group delay (NGD) function. The feasibility condition of the proposed reconstruction technique in function of the sensor signal spectrum bandwidth under consideration is elaborated. The reconstruction technique principle is theoretically introduced by means of identification of low-pass NGD function parameters and the appropriated circuit topology. The unfamiliar low-pass NGD function analysis and synthesis equations are established. As an example, for the feasibility study, an RC-network based low-pass active cell is considered to implement the low-pass NGD function. A design method of NGD circuit in function of the sensor distortion transfer function is described in different successive steps. The developed NGD reconstruction technique is validated by different proofs of concept. First, transient simulations are carried out with Gaussian and sinc analytical signals. Then, experimental feasibility study is also performed with arbitrary waveform signal. As expected, the NGD reconstruction technique efficiency is confirmed with improvement of distorted signal integrity parameters and cross correlation better than 97%.

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“…The BCG waveform, involving whole body vibrations, is more related to overall cardiovascular function rather than referring to heart mechanical events (such as SCG and GCG). Therefore, some modeling is needed to correlate BCG acceleration patterns to cardiovascular function phases, accounting for a wider picture including, besides the heart, the whole systemic, cerebral and pulmonary circulation apparatuses [29]. Techniques for the acquisition of recoil acceleration patterns include load cells or force plates, piezoelectric sensors, accelerometers.…”

Section: A Related Workmentioning

confidence: 99%

Accurate Heartbeat Detection on Ballistocardiogram Accelerometric Traces

Mora

Cocconcelli

Matrella

et al. 2020

IEEE Trans. Instrum. Meas.

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This paper presents an automated procedure for acquisition and analysis of BallistoCardioGraphy (BCG) traces. A tri-axial accelerometer and a microcontroller unit are used to record heart-induced recoil forces generated from a lying subject. The problem of BCG J-peak annotation is split into two sub-tasks: candidates extraction, based on a detection signal, and actual annotation, guided by subject-specific search windows. Such procedure is derived from an automatic calibration, which is carried out with no need of concurrent ElectroCardioGram (ECG) or user intervention. The algorithm also implements post-annotation checks for refinement of annotation, which effectively reduces the number of missed J-peaks. The impact of each algorithm phase is analyzed, assessing statistical significance of each step; finally, performance is optimized in a data-driven fashion. Results show that the proposed methodology is able to achieve high sensitivity and precision (the median score is 98.9% and 98.1%, respectively) in J-peak detection. The quality of J-peaks timing annotation is further demonstrated by a very low discrepancy between BCG and ECG HR estimates. Over all population, the standard deviation of such error was found to be approximately 6.56 ms, whereas the Mean Absolute Error just 4.7 ms (i.e. ≈ 1.18; Ts, where Ts = 4 ms is the sampling period). Such scores, indeed, improve over recent related literature.

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Cardiovascular Function and Ballistocardiogram: A Relationship Interpreted via Mathematical Modeling

Cited by 44 publications

References 57 publications

Quantification of Resting-State Ballistocardiogram Difference Between Clinical and Non-Clinical Populations for Ambient Monitoring of Heart Failure

Quantification of Resting-State Ballistocardiogram Difference Between Clinical and Non-Clinical Populations for Ambient Monitoring of Heart Failure

Reconstruction Technique of Distorted Sensor Signals with Low-Pass NGD Function

Accurate Heartbeat Detection on Ballistocardiogram Accelerometric Traces

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