Towards robust estimation of systolic time intervals using head-to-foot and dorso-ventral components of sternal acceleration signals

Javaid, Abdul Q.; Fesmire, N. Forrest; Weitnauer, Mary Ann; Inan, Omer T.

doi:10.1109/bsn.2015.7299377

Cited by 10 publications

(2 citation statements)

References 23 publications

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“…The Bayesian approach was used for estimating PEP and, since AV closure corresponds to the second heart sound, its onset was used for estimating LVET. In [88], a novel method was proposed for measuring PEP and LVET based on the combined analysis of ECG, ICG, and SCG in head-to-foot direction, in place of dorsoventral direction. Additionally, the effect of postural variations was examined on the correlation of estimated values of PEP and LVET from SCG and ICG.…”

Section: Extraction Of Cti For Cardiac Health Monitoringmentioning

confidence: 99%

A Comprehensive Review on Seismocardiogram: Current Advancements on Acquisition, Annotation, and Applications

et al. 2021

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In recent years, cardiovascular diseases are on the rise, and they entail enormous health burdens on global economies. Cardiac vibrations yield a wide and rich spectrum of essential information regarding the functioning of the heart, and thus it is necessary to take advantage of this data to better monitor cardiac health by way of prevention in early stages. Specifically, seismocardiography (SCG) is a noninvasive technique that can record cardiac vibrations by using new cutting-edge devices as accelerometers. Therefore, providing new and reliable data regarding advancements in the field of SCG, i.e., new devices and tools, is necessary to outperform the current understanding of the State-of-the-Art (SoTA). This paper reviews the SoTA on SCG and concentrates on three critical aspects of the SCG approach, i.e., on the acquisition, annotation, and its current applications. Moreover, this comprehensive overview also presents a detailed summary of recent advancements in SCG, such as the adoption of new techniques based on the artificial intelligence field, e.g., machine learning, deep learning, artificial neural networks, and fuzzy logic. Finally, a discussion on the open issues and future investigations regarding the topic is included.

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Section: Extraction Of Cti For Cardiac Health Monitoringmentioning

confidence: 99%

A Comprehensive Review on Seismocardiogram: Current Advancements on Acquisition, Annotation, and Applications

et al. 2021

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“…Algorithms that rely on the ECG fiducial points require ECG R-peaks for heartbeat detection and feature extraction in SCG (Pandia et al 2012, Ramos-Castro et al 2012, JafariTadi et al 2015, Javaid et al 2015, Khosrow-Khavar et al 2015, Wiens et al 2015. For example, the recently published paper by Khosrow-Khavar et al (2015) indicated a new method for automatic annotation of high frequency components in the SCG signal by applying several signal processing techniques.…”

Section: Literature Reviewmentioning

confidence: 99%

A real-time approach for heart rate monitoring using a Hilbert transform in seismocardiograms

et al. 2016

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Heart rate monitoring helps in assessing the functionality and condition of the cardiovascular system. We present a new real-time applicable approach for estimating beat-to-beat time intervals and heart rate in seismocardiograms acquired from a tri-axial microelectromechanical accelerometer. Seismocardiography (SCG) is a non-invasive method for heart monitoring which measures the mechanical activity of the heart. Measuring true beat-to-beat time intervals from SCG could be used for monitoring of the heart rhythm, for heart rate variability analysis and for many other clinical applications. In this paper we present the Hilbert adaptive beat identification technique for the detection of heartbeat timings and inter-beat time intervals in SCG from healthy volunteers in three different positions, i.e. supine, left and right recumbent. Our method is electrocardiogram (ECG) independent, as it does not require any ECG fiducial points to estimate the beat-to-beat intervals. The performance of the algorithm was tested against standard ECG measurements. The average true positive rate, positive prediction value and detection error rate for the different positions were, respectively, supine (95.8%, 96.0% and ≃0.6%), left (99.3%, 98.8% and ≃0.001%) and right (99.53%, 99.3% and ≃0.01%). High correlation and agreement was observed between SCG and ECG inter-beat intervals (r > 0.99) for all positions, which highlights the capability of the algorithm for SCG heart monitoring from different positions. Additionally, we demonstrate the applicability of the proposed method in smartphone based SCG. In conclusion, the proposed algorithm can be used for real-time continuous unobtrusive cardiac monitoring, smartphone cardiography, and in wearable devices aimed at health and well-being applications.

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Cardiac Mechanical Signals

Casanella

Khosrow-Khavar

Schmidt

et al. 2019

Series in BioEngineering

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Towards robust estimation of systolic time intervals using head-to-foot and dorso-ventral components of sternal acceleration signals

Cited by 10 publications

References 23 publications

A Comprehensive Review on Seismocardiogram: Current Advancements on Acquisition, Annotation, and Applications

A Comprehensive Review on Seismocardiogram: Current Advancements on Acquisition, Annotation, and Applications

A real-time approach for heart rate monitoring using a Hilbert transform in seismocardiograms

Cardiac Mechanical Signals

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