Analyzing Heart Rate Estimation from Vibrational Cardiography with Different Orientations

Aboulezz, Ezz; Skoric, James; D’Mello, Yannick; Hakim, Siddiqui; Clairmonte, Nathan; Lortie, Michel; Plant, David V.

doi:10.1109/embc44109.2020.9175255

Cited by 3 publications

(8 citation statements)

References 16 publications

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“…D’Mello et al in [ 80 ] identified the heart sounds based on seismocardiography and gyrocardiography with a high correlation coefficients of 0.9887 for HR measured with concurrent ECG measurement. Another study on the heart rate estimation in 2020 was conducted by Ezz Aboulezz et al [ 81 ]. In the same year Ezz Aboulezz et al [ 81 ] developed the beat detector based on Autocorrelated Differential Algorithm (ADA), which uses the autocorrelation of the signal to calculate the HR [ 9 , 82 ].…”

Section: Resultsmentioning

confidence: 99%

“…Another study on the heart rate estimation in 2020 was conducted by Ezz Aboulezz et al [ 81 ]. In the same year Ezz Aboulezz et al [ 81 ] developed the beat detector based on Autocorrelated Differential Algorithm (ADA), which uses the autocorrelation of the signal to calculate the HR [ 9 , 82 ].…”

Section: Resultsmentioning

confidence: 99%

“…Based on the review of available works on the GCG, the overall list of applications is as follows: heart beat detection [ 9 , 20 , 37 , 39 , 48 , 53 , 57 , 58 , 81 , 83 , 84 ], fetal heart rate extraction [ 72 ], the analysis of hemodynamics [ 66 ], including: stroke volume estimation [ 111 ], IVCT, LVET [ 11 ], PEP [ 11 , 112 ], QS2 estimation [ 11 ]. cardiac and respiratory gating in PET/CT [ 33 , 46 , 69 , 113 ], HRV analysis [ 47 , 65 , 77 , 83 , 84 ], annotation of seismocardiograms [ 39 ], annotation of heart sounds [ 80 ], diagnosing of various cardiovascular diseases, including: atrial fibrillation [ 32 , 49 , 60 , 61 , 64 , 65 , 77 , 108 ], myocardial infarction [ 54 , 55 , 60 ...…”

Section: Resultsmentioning

confidence: 99%

“…This approach is an example of repurposing the method proposed for seismocardiograms in [ 86 ] and used the fact that the AO wave is the most prominent peak in GCG [ 61 ]. In the same year Ezz Aboulezz et al [ 81 ] developed the beat detector based on Autocorrelated Differential Algorithm (ADA) based on the autocorrelation of the signal to calculate the HR [ 9 , 82 ]. The correlation of heart rate measured on GCG and reference HR obtained on ECG was very strong (

when supine,

when standing and

across the entire data set) [ 81 ].…”

Section: Resultsmentioning

confidence: 99%

“…• heart beat detection [9,20,37,39,48,53,57,58,81,83,84], • fetal heart rate extraction [72], • the analysis of hemodynamics [66], including:…”

Section: Applicationsmentioning

confidence: 99%

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Gyrocardiography: A Review of the Definition, History, Waveform Description, and Applications

Sieciński

Kostka

Tkacz

2020

Sensors

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

when supine,

when standing and

across the entire data set) [ 81 ].…”

Section: Resultsmentioning

confidence: 99%

“…• heart beat detection [9,20,37,39,48,53,57,58,81,83,84], • fetal heart rate extraction [72], • the analysis of hemodynamics [66], including:…”

Section: Applicationsmentioning

confidence: 99%

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Gyrocardiography: A Review of the Definition, History, Waveform Description, and Applications

Sieciński

Kostka

Tkacz

2020

Sensors

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A Wavelet-Based Approach for Motion Artifact Reduction in Ambulatory Seismocardiography

Skoric,

D’Mello,

Plant

2024

IEEE J. Transl. Eng. Health Med.

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Wearable sensing has become a vital approach to cardiac health monitoring, and seismocardiography (SCG) is emerging as a promising technology in this field. However, the applicability of SCG is hindered by motion artifacts, including those encountered in practice of which the strongest source is walking. This holds back the translation of SCG to clinical settings. We therefore investigated techniques to enhance the quality of SCG signals in the presence of motion artifacts. To simulate ambulant recordings, we corrupted a clean SCG dataset with real-walking-vibrational noise. We decomposed the signal using several empirical-mode-decomposition methods and the maximum overlap discrete wavelet transform (MODWT). By combining MODWT, time-frequency masking, and nonnegative matrix factorization, we developed a novel algorithm which leveraged the vertical axis accelerometer to reduce walking vibrations in dorsoventral SCG. The accuracy and applicability of our method was verified using heart rate estimation. We used an interactive selection approach to improve estimation accuracy. The best decomposition method for reduction of motion artifact noise was the MODWT. Our algorithm improved heart rate estimation from 0.1 to 0.8 r-squared at −15 dB signal-to-noise ratio (SNR). Our method reduces motion artifacts in SCG signals up to a SNR of −19 dB without requiring any external assistance from electrocardiography (ECG). Such a standalone solution is directly applicable to the usage of SCG in daily life, as a content-rich replacement for other wearables in clinical settings, and other continuous monitoring scenarios. In applications with higher noise levels, ECG may be incorporated to further enhance SCG and extend its usable range. This work addresses the challenges posed by motion artifacts, enabling SCG to offer reliable cardiovascular insights in more difficult scenarios, and thereby facilitating wearable monitoring in daily life and the clinic.

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Heartbeat Detection in Gyrocardiography Signals without Concurrent ECG Tracings

Parlato

Centracchio

Esposito

et al. 2023

Sensors

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A heartbeat generates tiny mechanical vibrations, mainly due to the opening and closing of heart valves. These vibrations can be recorded by accelerometers and gyroscopes applied on a subject’s chest. In particular, the local 3D linear accelerations and 3D angular velocities of the chest wall are referred to as seismocardiograms (SCG) and gyrocardiograms (GCG), respectively. These signals usually exhibit a low signal-to-noise ratio, as well as non-negligible amplitude and morphological changes due to changes in posture and the sensors’ location, respiratory activity, as well as other sources of intra-subject and inter-subject variability. These factors make heartbeat detection a complex task; therefore, a reference electrocardiogram (ECG) lead is usually acquired in SCG and GCG studies to ensure correct localization of heartbeats. Recently, a template matching technique based on cross correlation has proven to be particularly effective in recognizing individual heartbeats in SCG signals. This study aims to verify the performance of this technique when applied on GCG signals. Tests were conducted on a public database consisting of SCG, GCG, and ECG signals recorded synchronously on 100 patients with valvular heart diseases. The results show that the template matching technique identified heartbeats in GCG signals with a sensitivity and positive predictive value (PPV) of 87% and 92%, respectively. Regression, correlation, and Bland–Altman analyses carried out on inter-beat intervals obtained from GCG and ECG (assumed as reference) reported a slope of 0.995, an intercept of 4.06 ms (R2 > 0.99), a Pearson’s correlation coefficient of 0.9993, and limits of agreement of about ±13 ms with a negligible bias. A comparison with the results of a previous study obtained on SCG signals from the same database revealed that GCG enabled effective cardiac monitoring in significantly more patients than SCG (95 vs. 77). This result suggests that GCG could ensure more robust and reliable cardiac monitoring in patients with heart diseases with respect to SCG.

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Analyzing Heart Rate Estimation from Vibrational Cardiography with Different Orientations

Cited by 3 publications

References 16 publications

Gyrocardiography: A Review of the Definition, History, Waveform Description, and Applications

Gyrocardiography: A Review of the Definition, History, Waveform Description, and Applications

A Wavelet-Based Approach for Motion Artifact Reduction in Ambulatory Seismocardiography

Heartbeat Detection in Gyrocardiography Signals without Concurrent ECG Tracings

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