Wearable seismocardiography: Towards a beat-by-beat assessment of cardiac mechanics in ambulant subjects

Rienzo, Marco Di; Vaini, Emanuele; Castiglioni, Paolo; Merati, Giampiero; Meriggi, P.; Parati, Gianfranco; Faini, Andrea; Rizzo, Francesco

doi:10.1016/j.autneu.2013.04.005

Cited by 138 publications

(101 citation statements)

References 23 publications

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“…The complex interplay between autonomic control of the heart and cardiac mechanics characterized by the valve intervals, has been previously reported in literature and is consistent with the results of this study [13,14]. According to the literature, PEP is attributed to the sympathetic influences on the heart [13].…”

Section: Discussionsupporting

confidence: 81%

“…These intervals are based on opening and closing timings of the fetal cardiac valves and can be automatically estimated as proposed in our previous papers [11,12]. The valve intervals can be used to assess the Autonomic Nervous System (ANS) function, as an alternative to FHRV, since the cardiac mechanics are known to reflect the autonomic control [13,14]. Significant changes in the valve intervals with advancing GA were also reported in our previous studies [11].…”

Section: Introductionmentioning

confidence: 84%

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Estimating Fetal Gestational Age Using Cardiac Valve Intervals

Marzbanrad

Khandoker

Kimura

et al. 2016

2016 Computing in Cardiology Conference (CinC)

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A new automated method for estimation of the gestational age is presented in this paper based on the intervals between fetal cardiac valve timings and the Q-wave of fetal electrocardiogram (fECG). The intervals were estimated automatically from one-dimensional Doppler Ultrasound and noninvasive fECG. Among the intervals, Isovolumic Contraction Time, Electromechanical Delay Time, Ventricular Filling Time and their interactions were selected in a stepwise regression process. Compared with Crown-Rump Length as gold standard, a mean absolute error of 3.8 weeks was obtained using leave-one-out cross validation. This method also outperformed a fetal heart rate based approach. Since valve intervals reflect the autonomic function, this method provides a novel measure of the development of fetal autonomic nervous system that may be growth curve independent.

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Section: Discussionsupporting

confidence: 81%

Section: Introductionmentioning

confidence: 84%

Estimating Fetal Gestational Age Using Cardiac Valve Intervals

Marzbanrad

Khandoker

Kimura

et al. 2016

2016 Computing in Cardiology Conference (CinC)

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“…They can also be transmitted in real time to an external device through a Bluetooth connection. Further details on the structure of the system can be found in [2,3].…”

Section: Methodsmentioning

confidence: 99%

“…In the present analysis we focussed on two paradigmatic data segments each lasting 30 min. The first segment (segment A, from 0:07 to 0:37 hours) includes data observed during a sleep phase characterized by a certain stability of the RR Interval In each data segment a wavelet-based filtering procedure was applied to the SCG signal to remove a possible slow wandering of the baseline caused by breathing [3]. In short, the original SCG series was decomposed by using the db4 mother wavelet, then the level 6 approximation component (a6) was subtracted from the original signal.…”

Section: Methodsmentioning

confidence: 99%

Wearable seismocardiography for the beat-to-beat assessment of cardiac intervals during sleep

Rienzo

Vaini

Castiglioni

et al. 2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Seismocardiogram (SCG) can be detected during sleep by a textile-based wearable system. This pilot study preliminarily explores the feasibility of a beat-to-beat estimation of cardiac mechanical features (RR interval, RRI, Pre-Ejection Period, PEP, Isovolumic Contraction Time, ICT, Left Ventricular Ejection Time, LVET, Isovolumic Relaxation Time, IRT) from the joint ECG and SCG assessment during sleep. The analysis of two 30-min sleep data segments from one healthy subject, indicated that 1) respiration largely influence the dynamics of most of the parameters; 2) variability of cardiac intervals is only marginally influenced by the RRI variability; 3) appreciable spectral power at frequencies ≤ 0.1 is only observed in the RRI spectrum and not in the spectra of the other indexes; 4) IRT has a broadband variability, that is clearly different from the dynamics of the other indexes. These findings represent the very first description of the beat-to-beat variability of cardiac mechanical indexes. Further investigations on a larger population are in progress to confirm the present results.

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“…The SCG signal is first low-pass filtered with a cutoff of 20 Hz to remove higher frequency content associated with the sounds of the cardiac valves [8]. The resulting signal is then passed through a notch filter centered at 0 Hz with a cutoff of approximately 2 Hz to remove any DC offset and respiratory motion.…”

Section: A Beat-by-beat Detection Of Cardiac Quiescence From Scgmentioning

confidence: 99%

Seismocardiography-based detection of cardiac quiescence for cardiac computed tomography angiography

Wick

McClellan

Inan

et al. 2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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As a measure of chest wall acceleration caused by cardiac motion, the seismocardiogram (SCG) has the potential to supplement the electrocardiogram (ECG) to more accurately trigger cardiac computed tomography angiography (CTA) data acquisition during periods of cardiac quiescence. The SCG was used to identify the systolic and diastolic quiescent periods of the cardiac cycle on a beat-by-beat basis and from composite velocity signals for nine healthy subjects. The cardiac velocity transmitted to the chest wall was calculated using a Kalman filter. The average systolic and diastolic quiescent periods were centered at 30% and 76%, respectively. Inter- and intra-subject variability of the quiescent phases with respect to the ECG was observed, suggesting that the ECG may be a suboptimal modality for predicting cardiac quiescence.

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Wearable seismocardiography: Towards a beat-by-beat assessment of cardiac mechanics in ambulant subjects

Cited by 138 publications

References 23 publications

Estimating Fetal Gestational Age Using Cardiac Valve Intervals

Estimating Fetal Gestational Age Using Cardiac Valve Intervals

Wearable seismocardiography for the beat-to-beat assessment of cardiac intervals during sleep

Seismocardiography-based detection of cardiac quiescence for cardiac computed tomography angiography

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