Accurate and consistent automatic seismocardiogram annotation without concurrent ECG

Laurin, Alexandre; Khosrow, Farzad; Blaber, Andrew P.; Tavakolian, Kouhyar

doi:10.1109/cic.2015.7408577

Cited by 7 publications

(3 citation statements)

References 16 publications

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“…This indicates a strong influence of the presence of valvular heart disease on HRV indices, except AVNN (mean interbeat interval) and VLF, which was in line with [29,30]. The similarities between the results of the HRV analysis in patients with VHD in our study and those reported in [29] prove that the HRV indices obtained from seismocardiograms are valid for patients with aortic stenosis and also for other VHD [14,31].…”

Section: Discussionsupporting

confidence: 86%

Comparison of Heart Rate Variability Indices Based on Seismocardiograms from Healthy Volunteers and Patients with Valvular Heart Diseases

Siecinski¹,

Kostka²,

Tkacz"³

2022

Computing in Cardiology Conference (CinC)

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Heart rate variability (HRV) is a physiological variation of intervals between consecutive heart beats that reflects the activity of the autonomic nervous system. This parameter is traditionally evaluated on the basis of electrocardiograms (ECG signals). Because seismocardiography (SCG) registers cardiac mechanical activity, it may be used in HRV analysis and the evaluation of valvular heart diseases (VHDs) simultaneously. In our study, our objective was to compare HRV indices in the time and frequency domain obtained from seismocardiograms (SCG signals) in healthy volunteers and patients with valvular heart diseases. The results of the HRV analysis indicate that there are significant differences between the HRV indices obtained from the seismocardiograms in healthy volunteers and patients with VHD. This shows the feasibility and validity of HRV analysis based on seismocardiograms in healthy volunteers and patients with VHD.

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Section: Discussionsupporting

confidence: 86%

Comparison of Heart Rate Variability Indices Based on Seismocardiograms from Healthy Volunteers and Patients with Valvular Heart Diseases

Siecinski¹,

Kostka²,

Tkacz"³

2022

Computing in Cardiology Conference (CinC)

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“…Over the years, various algorithms have been employed for HRV detection from ECG, with many based on R-peak detection in QRS complexes [27]. Laurin et al [28] demonstrated the reliability of HRV indices derived from SCG signals, and Tadi et al [29] confirmed a strong association between HRV indices derived from ECG and SCG. Beyond traditional ECG applications, MCG signals have proven effective in classifying cardiovascular anomalies through smartphones [1], [30], estimating respiratory rate (RR) [12], [31], determining tidal volume alongside ECG from SCG [32], estimating cardiac time intervals from SCG [33], real-time cardiovascular disease detection for drivers [34], among other applications.…”

Section: Introductionmentioning

confidence: 99%

M2ECG: Wearable Mechanocardiograms to Electrocardiogram Estimation Using Deep Learning

Tapotee,

Saha,

Mahmud

et al. 2024

IEEE Access

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Chest surface vibrations induced by cardiac activities can provide valuable insights into various heart conditions. Seismocardiogram (SCG) and Gyrocardiogram (GCG) signals, collectively referred to as Mechanocardiograms (MCG) and collected using a chest-mounted accelerometer and gyroscope, respectively, have the potential to serve as an effective alternative to Electrocardiograms (ECG) for continuous cardiac monitoring. In many cases, both modalities (MCG and ECG) can be used in tandem to monitor cardiac functions in both healthy subjects and Intensive Care Unit (ICU) patients. Direct acquisition of ECGs can be challenging in certain scenarios, such as with wearable devices, or due to issues with disconnections arising from loose contact surfaces or gel corrosion during long-term usage. ECG considered the gold standard for heart monitoring, is essential for a comprehensive assessment of cardiac parameters and patient health. MCGs have the potential to reliably estimate ECGs and can replace direct ECG acquisition procedures in such cases. In this study, we introduce M2ECG, a 1D-segmentation-based approach for translating ECG signals from the corresponding MCG signals acquired by an Inertial Measurement Unit (IMU) attached to the chest wall. Using the proposed SA-UNet, we achieved an average Pearson Correlation Coefficient (PCC) of 81.76% on a subject-independent test set. We also compared the estimated heart rates (HR) from the reconstructed ECGs to the ground truth ECGs to validate our model's performance. The overall HR correlation achieved on the subject-independent test set was around 94.167%. The highest correlation of the HR and HRV calculated from the translated and the ground truth ECGs were around 99.073% and 96.289%, respectively for the best test case. The strong correlation observed in cardiac parameters (HR, HRV) underscores the effectiveness of MCG, suggesting its potential use for continuous monitoring of cardiac patients.

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“…With a few exceptions, these methods have been limited to single-point measurements on the chest surface. While automatic annotation algorithms have been developed [3,4], and published studies demonstrate potential uses for clinical applications [5][6][7][8][9], SCG has so far largely remains confined to scientific laboratories. This is in part due to the lack of physiological and physical understanding of the processes that underlie the phenomena, causing lasting uncertainties as to the exact interpretation of the signal's peaks and valleys, as well as some variability in accelerometer placement.…”

Section: Introductionmentioning

confidence: 99%

Airborne ultrasound surface motion camera: Application to seismocardiography

Shirkovskiy

Laurin

Jeger-Madiot

et al. 2018

Applied Physics Letters

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The recent achievements in the accelerometer-based seismocardiography field indicate a strong potential for this technique to address a wide variety of clinical needs. Recordings from different locations on the chest can give a more comprehensive observation and interpretation of wave propagation phenomena than a single-point recording, can validate existing modeling assumptions (such as the representation of the sternum as a single solid body), and provide better identifiability for models using richer recordings. Ultimately, the goal is to advance our physiological understanding of the processes to provide useful data to promote cardiovascular health. Accelerometer-based multichannel system is a contact method and laborious for use in practice, and also even ultralight accelerometers can cause non-negligible loading effects. We propose a contactless ultrasound imaging method to measure thoracic and abdominal surface motions, demonstrating that it is adequate for typical seismocardiogram (SCG) use. The developed method extends non-contact surface-vibrometry to fast 2D mapping by originally combining multi-element airborne ultrasound arrays, a synthetic aperture implementation, and pulsed-waves. Experimental results show the ability of the developed method to obtain 2D seismocardiographic maps of the body surface 30 × 40 cm2 in dimension, with a temporal sampling rate of several hundred Hz, using ultrasound waves with the central frequency of 40 kHz. Our implementation was validated in-vivo on eight healthy human participants. The shape and position of the zone of maximal absolute acceleration and velocity during the cardiac cycle were also observed. This technology could potentially be used to obtain more complete cardio-vascular information than single-source SCG in and out of clinical environments, due to enhanced identifiability provided by the distributed measurements, and observation of propagation phenomena.

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Accurate and consistent automatic seismocardiogram annotation without concurrent ECG

Cited by 7 publications

References 16 publications

Comparison of Heart Rate Variability Indices Based on Seismocardiograms from Healthy Volunteers and Patients with Valvular Heart Diseases

Comparison of Heart Rate Variability Indices Based on Seismocardiograms from Healthy Volunteers and Patients with Valvular Heart Diseases

M2ECG: Wearable Mechanocardiograms to Electrocardiogram Estimation Using Deep Learning

Airborne ultrasound surface motion camera: Application to seismocardiography

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