Automatic and Robust Delineation of the Fiducial Points of the Seismocardiogram Signal for Noninvasive Estimation of Cardiac Time Intervals

Khosrow-Khavar, Farzad; Tavakolian, Kouhyar; Blaber, Andrew P.; Menon, Carlo

doi:10.1109/tbme.2016.2616382

Cited by 64 publications

(57 citation statements)

References 23 publications

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“…Estimation of PEP and TST using SCG and GCG signals is conventionally performed by annotation of the opening and closure of aortic valves in the recordings. Yet due to the inherent complexity associated with signal morphologies that contain both inter-and intra-subject variability [7], finding fiducial points in SCG and GCG is extremely difficult. Therefore, the solution based on multivariate regression proposed in this paper can be considered as a promisingly powerful tool to estimate approximate cardiac time intervals without inducing implementation complexity.…”

Section: Discussionmentioning

confidence: 99%

Estimation of Cardiac Time Intervals from the Mechanical Activity of the Heart Using Machine Learning

Dehkordi¹,

Tavakolian²,

Zhao³

et al. 2019

2019 Computing in Cardiology Conference (CinC)

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Pre-ejection period (PEP) and total systolic time (TST) are hemodynamic indices which provide vital information about left ventricular performance. In this study, we suggested a method for estimating PEP and TST using the features extracted from the mechanical activity of the heart without finding the fiducial points associated with the opening and closure of heart valves. We recorded seismocardiogram (SCG) and gyrocardiogram (GCG) signals from 50 healthy subjects using a 3-axial micro-electromechanical joint accelerometer-gyroscope sensor. For each axis of the SCG and GCG signals, 18 features were extracted per cardiac cycle. Several regression models were implemented using the features from multiple combinations of SCG and GCG and were validated against the measurements from tissue Doppler imaging (TDI) as the reference. The model that was fitted over the SCG z-axis features provided the most accurate estimates for PEP and TST intervals with the root mean square error of 9.5 and 17.2 millisecond, respectively. The estimation of cardiac time intervals investigated in this study broadens the potential of SCG and GCG in the monitoring of cardiovascular performance. As these technologies yield themselves to wearable applications, they could be used outside of hospital/clinical settings to detect abnormalities and malfunctions of the cardiovascular system.

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Section: Discussionmentioning

confidence: 99%

Estimation of Cardiac Time Intervals from the Mechanical Activity of the Heart Using Machine Learning

Dehkordi¹,

Tavakolian²,

Zhao³

et al. 2019

2019 Computing in Cardiology Conference (CinC)

Self Cite

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“…In a recent study, a delineation algorithm was designed to detect the fiducial points of the SCG signal, including the AO-point, by training an algorithm on 48,318 manually annotated cardiac cycles and resulted in good accuracy when tested on healthy individuals [29]. Additionally, many of the papers in literature detected the AO-point as the second positive peak of the SCG signal [17], However, we have found with our measurements, with standing rather than supine subjects, that this peak is not necessarily reproducible from person to person, and the noise in the measurements can easily corrupt this peak’s detection as compared to the detection of the largest peak (positive or negative).…”

Section: Methodsmentioning

confidence: 99%

Automatic Detection of Seismocardiogram Sensor Misplacement for Robust Pre-Ejection Period Estimation in Unsupervised Settings

Ashouri

Inan

2017

IEEE Sensors J.

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Seismocardiography (SCG), the measurement of the local chest vibrations due to the movements of blood and the heart, is a non-invasive technique for assessing myocardial contractility via the pre-ejection period (PEP). Recently, SCG-based extraction of PEP has been shown to be an effective means of classifying decompensated from compensated heart failure patients, and thus can be potentially used for monitoring such patients at home. Accurate extraction of PEP from SCG signals hinges on lab-based population data (i.e., regression curves) linking particular time-domain features of the SCG signal to corresponding features from reference standard bulky instruments such as impedance cardiography (ICG). Such regression curves, in the case of SCG, have always been estimated based on the “ideal” positioning of the SCG sensor on the chest. However, in settings such as the home where users may position the SCG measurement hardware on the chest without supervision, it is likely that the sensor will not always be placed exactly on this “ideal” location on the sternum, but rather on other positions on the chest as well. In this study, we show for the first time that the regression curve for estimating PEP from SCG signals differs significantly as the position of the sensor changes. We further devise a method to automatically detect when the sensor is placed in any position other than the desired one in order to avoid inaccurate systolic time interval estimation. Our classification algorithm for this purpose resulted in 0.83 precision and 0.82 recall when classifying whether the sensor is placed in the desired position or not. The classifier was tested with heartbeats taken both at rest, and also during exercise recovery to ensure that waveform changes due to positioning could be accurately discriminated from those due to physiological effects.

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“…Most research groups applied conventional band-pass filters to remove baseline wandering, body movements, and breathing artefacts from SCG signals [26,36,38,41,45,46,55,[58][59][60][61][62][63]67,71,75,76,[78][79][80]82,93]. A few studies utilized or proposed more advanced noise removal techniques [64,76,88,[94][95][96].…”

Section: Noise Reductionmentioning

confidence: 99%

“…More studies are needed that compare different filtering methods in clinical and ambulatory settings. [26,36,38,41,45,46,55,[58][59][60][61][62][63]67,71,75,76,[78][79][80]82,93] Adaptive filtering Motion artefact removal [88,95] Averaging theory Motion artefact removal [101] Comb filtering Removing respiration noise from radar signal [50] Empirical mode decomposition Baseline wandering, breathing and body movement artefact removal [76,94,95] Independent component analysis Motion artefact removal [102] Median filtering [96] Morphological filtering [95] Polynomial smoothing Motion artefact removal [103] Savitzky-Golay filtering Motion artefact removal [83,103] Wavelet denoising Segmentation of HSs and SCG [64,95,96] Wiener filtering [94] 2.…”

Section: Noise Reductionmentioning

confidence: 99%

“…These include CTIs (e.g., PEP, isovolumic contraction time (IVCT), LVET, and isovolumic relaxation time (IVRT)) and other metrics such as PEP/LVET ratio, (IVCT+IVRT)/LVET (also called myocardial performance index), and the LVET/R-R-interval ratio [81,107]. Amplitudes and slopes associated with the fiducial points, such as MC to AO slope, were used in some studies [61,107], as well as features of the SCG signal that do not depend on specific fiducial points, such as maxima, minima, and their associated widths of specific segments of the SCG signal [82].…”

Section: Time-domain Featuresmentioning

confidence: 99%

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Recent Advances in Seismocardiography

et al. 2019

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Cardiovascular disease is a major cause of death worldwide. New diagnostic tools are needed to provide early detection and intervention to reduce mortality and increase both the duration and quality of life for patients with heart disease. Seismocardiography (SCG) is a technique for noninvasive evaluation of cardiac activity. However, the complexity of SCG signals introduced challenges in SCG studies. Renewed interest in investigating the utility of SCG accelerated in recent years and benefited from new advances in low-cost lightweight sensors, and signal processing and machine learning methods. Recent studies demonstrated the potential clinical utility of SCG signals for the detection and monitoring of certain cardiovascular conditions. While some studies focused on investigating the genesis of SCG signals and their clinical applications, others focused on developing proper signal processing algorithms for noise reduction, and SCG signal feature extraction and classification. This paper reviews the recent advances in the field of SCG.

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Automatic and Robust Delineation of the Fiducial Points of the Seismocardiogram Signal for Noninvasive Estimation of Cardiac Time Intervals

Abstract: The presented algorithm could be used for accurate and non-invasive estimation of cardiac time intervals.

Cited by 64 publications

References 23 publications

Estimation of Cardiac Time Intervals from the Mechanical Activity of the Heart Using Machine Learning

Estimation of Cardiac Time Intervals from the Mechanical Activity of the Heart Using Machine Learning

Automatic Detection of Seismocardiogram Sensor Misplacement for Robust Pre-Ejection Period Estimation in Unsupervised Settings

Recent Advances in Seismocardiography

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