The Experts below are selected from a list of 510 Experts worldwide ranked by ideXlab platform
Carolina Varon - One of the best experts on this subject based on the ideXlab platform.
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evaluation of a commercial Ballistocardiography sensor for sleep apnea screening and sleep monitoring
Sensors, 2019Co-Authors: Dorien Huysmans, Pascal Borzee, Dries Testelmans, Bertien Buyse, Tim Willemen, Sabine Van Huffel, Carolina VaronAbstract:There exists a technological momentum towards the development of unobtrusive, simple, and reliable systems for long-term sleep monitoring. An off-the-shelf commercial pressure sensor meeting these requirements is the Emfit QS. First, the potential for sleep apnea screening was investigated by revealing clusters of contaminated and clean segments. A relationship between the irregularity of the data and the sleep apnea severity class was observed, which was valuable for screening (sensitivity 0.72, specificity 0.70), although the linear relation was limited ( R 2 of 0.16). Secondly, the study explored the suitability of this commercial sensor to be merged with gold standard polysomnography data for future sleep monitoring. As polysomnography (PSG) and Emfit signals originate from different types of sensor modalities, they cannot be regarded as strictly coupled. Therefore, an automated synchronization procedure based on artefact patterns was developed. Additionally, the optimal position of the Emfit for capturing respiratory and cardiac information similar to the PSG was identified, resulting in a position as close as possible to the thorax. The proposed approach demonstrated the potential for unobtrusive screening of sleep apnea patients at home. Furthermore, the synchronization framework enabled supervised analysis of the commercial Emfit sensor for future sleep monitoring, which can be extended to other multi-modal systems that record movements during sleep.
Migeotte Pierre-françois - One of the best experts on this subject based on the ideXlab platform.
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Kinocardiography Derived from Ballistocardiography and Seismocardiography Shows High Repeatability in Healthy Subjects
'MDPI AG', 2021Co-Authors: Hossein Amin, Rabineau Jérémy, Gorlier Damien, Migeotte Pierre-françois, Van De Borne, Philippe, Del Rio, Jose Ignacio Juarez, Nonclercq AntoineAbstract:Recent years have witnessed an upsurge in the usage of Ballistocardiography (BCG) and seismocardiography (SCG) to record myocardial function both in normal and pathological populations. Kinocardiography (KCG) combines these techniques by measuring 12 degrees-of-freedom of body motion produced by myocardial contraction and blood flow through the cardiac chambers and major vessels. The integral of kinetic energy (iK) obtained from the linear and rotational SCG/BCG signals, and automatically computed over the cardiac cycle, is used as a marker of cardiac mechanical function. The present work systematically evaluated the test–retest (TRT) reliability of KCG iK derived from BCG/SCG signals in the short term (
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Influence of sympathetic activation on myocardial contractility measured with Ballistocardiography and seismocardiography during sustained end-expiratory apnea
'American Physiological Society', 2020Co-Authors: Morra Sofia, Gauthey Anais, Hossein Amin, Rabineau Jérémy, Racapé Judith, Gorlier Damien, Migeotte Pierre-françois, Le Polain De Waroux, Jean Benoît, Van De Borne, PhilippeAbstract:Ballistocardiography (BCG) and seismocardiography (SCG) assess vibrations produced by cardiac contraction and blood flow, respectively, through micro-accelerometers and micro-gyroscopes. BCG and SCG kinetic energies (KE) and their temporal integrals (iK) during a single heartbeat are computed in linear and rotational dimensions. Our aim was to test the hypothesis that iK from BCG and SCG are related to sympathetic activation during maximal voluntary end-expiratory apnea. Multiunit muscle sympathetic nerve traffic [burst frequency (BF), total muscular sympathetic nerve activity (tMSNA)] was measured by microneurography during normal breathing and apnea (n = 28, healthy men). iK of BCG and SCG were simultaneously recorded in the linear and rotational dimension, along with oxygen saturation (SatO2 ) and systolic blood pressure (SBP). The mean duration of apneas was 25.4 9.4 s. SBP, BF, and tMSNA increased during the apnea compared with baseline (P = 0.01, P = 0.002, and P = 0.001, respectively), whereas SatO2 decreased (P = 0.02). At the end of the apnea compared with normal breathing, changes in iK computed from BCG were related to changes of tMSNA and BF only in the linear dimension (r=0.85, P < 0.0001; and r = 0.72, P = 0.002, respectively), whereas changes in linear iK of SCG were related only to changes of tMSNA (r = 0.62, P = 0.01). We conclude that maximal end expiratory apnea increases cardiac kinetic energy computed from BCG and SCG, along with sympathetic activity. The novelty of the present investigation is that linear iK of BCG is directly and more strongly related to the rise in sympathetic activity than the SCG, mainly at the end of a sustained apnea, likely because the BCG is more affected by the sympathetic and hemodynamic effects of breathing cessation. BCG and SCG may prove useful to assess sympathetic nerve changes in patients with sleep disturbances. NEW & NOTEWORTHY Ballistocardiography (BCG) and seismocardiography (SCG) assess vibrations produced by cardiac contraction and blood flow, respectively, through micro-accelerometers and micro-gyroscopes. Kinetic energies (KE) and their temporal integrals (iK) during a single heartbeat are computed from the BCG and SCG waveforms in a linear and a rotational dimension. When compared with normal breathing, during an end-expiratory voluntary apnea, iK increased and was positively related to sympathetic nerve traffic rise assessed by microneurography. Further studies are needed to determine whether BCG and SCG can probe sympathetic nerve changes in patients with sleep disturbances.SCOPUS: ar.jinfo:eu-repo/semantics/publishe
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Ballistocardiography and seismocardiography detection of hemodynamic changes during simulated obstructive apnea.
'IOP Publishing', 2020Co-Authors: Morra Sofia, Hossein Amin, Rabineau Jérémy, Gorlier Damien, Migeotte Pierre-françois, Chaumont Martin, Van De Borne, PhilippeAbstract:To investigate if modern seismocardiography (SCG) and Ballistocardiography (BCG) are useful in the detection of hemodynamic changes occurring during simulated obstructive apneic events.info:eu-repo/semantics/publishe
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Influence of sympathetic activation on myocardial contractility measured with Ballistocardiography and seismocardiography during sustained end-expiratory apnea.
'American Physiological Society', 2020Co-Authors: Morra Sofia, Gauthey Anais, Hossein Amin, Rabineau Jérémy, Racapé Judith, Gorlier Damien, Migeotte Pierre-françois, Le Polain De Waroux, Jean Benoît, Van De Borne, PhilippeAbstract:Ballistocardiography (BCG) and seismocardiography (SCG) assess vibrations produced by cardiac contraction and blood flow, respectively, through micro-accelerometers and micro-gyroscopes. BCG and SCG kinetic energies (KE) and their temporal integrals (K) during a single heartbeat are computed in linear and rotational dimensions. Our aim was to test the hypothesis that K from BCG and SCG are related to sympathetic activation during maximal voluntary end-expiratory apnea. Multiunit muscle sympathetic nerve traffic [burst frequency (BF), total muscular sympathetic nerve activity (tMSNA)] was measured by microneurography during normal breathing and apnea ( = 28, healthy men). K of BCG and SCG were simultaneously recorded in the linear and rotational dimension, along with oxygen saturation ([Formula: see text]) and systolic blood pressure (SBP). The mean duration of apneas was 25.4 ± 9.4 s. SBP, BF, and tMSNA increased during the apnea compared with baseline ( = 0.01, = 0.002,and = 0.001, respectively), whereas [Formula: see text] decreased ( = 0.02). At the end of the apnea compared with normal breathing, changes in K computed from BCG were related to changes of tMSNA and BF only in the linear dimension ( = 0.85, < 0.0001; and = 0.72, = 0.002, respectively), whereas changes in linear K of SCG were related only to changes of tMSNA ( = 0.62, = 0.01). We conclude that maximal end expiratory apnea increases cardiac kinetic energy computed from BCG and SCG, along with sympathetic activity. The novelty of the present investigation is that linear K of BCG is directly and more strongly related to the rise in sympathetic activity than the SCG, mainly at the end of a sustained apnea, likely because the BCG is more affected by the sympathetic and hemodynamic effects of breathing cessation. BCG and SCG may prove useful to assess sympathetic nerve changes in patients with sleep disturbances. NEW & NOTEWORTHY Ballistocardiography (BCG) and seismocardiography (SCG) assess vibrations produced by cardiac contraction and blood flow, respectively, through micro-accelerometers and micro-gyroscopes. Kinetic energies (KE) and their temporal integrals (K) during a single heartbeat are computed from the BCG and SCG waveforms in a linear and a rotational dimension. When compared with normal breathing, during an end-expiratory voluntary apnea, K increased and was positively related to sympathetic nerve traffic rise assessed by microneurography. Further studies are needed to determine whether BCG and SCG can probe sympathetic nerve changes in patients with sleep disturbances
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Cardiovascular adaptation to simulated microgravity and countermeasure efficacy assessed by Ballistocardiography and seismocardiography
'Springer Science and Business Media LLC', 2020Co-Authors: Rabineau Jérémy, Hossein Amin, Van De Borne, Philippe, Tank Jens, Luchitskaya Elena, Landreani Federica, Haut Benoît, Mulder Edwin, Caiani, Enrico Gianluca, Migeotte Pierre-françoisAbstract:Head-down bed rest (HDBR) reproduces the cardiovascular effects of microgravity. We tested the hypothesis that regular high-intensity physical exercise (JUMP) could prevent this cardiovascular deconditioning, which could be detected using seismocardiography (SCG) and Ballistocardiography (BCG). 23 healthy males were exposed to 60-day HDBR: 12 in a physical exercise group (JUMP), the others in a control group (CTRL). SCG and BCG were measured during supine controlled breathing protocols. From the linear and rotational SCG/BCG signals, the integral of kinetic energy (iK) was computed on each dimension over the cardiac cycle. At the end of HDBR, BCG rotational iK and SCG transversal iK decreased similarly for all participants (− 40% and − 44%, respectively, p < 0.05), and so did orthostatic tolerance (− 58%, p < 0.01). Resting heart rate decreased in JUMP (− 10%, p < 0.01), but not in CTRL. BCG linear iK decreased in CTRL (− 50%, p < 0.05), but not in JUMP. The changes in the systolic component of BCG linear iK were correlated to those in stroke volume and VO2 max (R = 0.44 and 0.47, respectively, p < 0.05). JUMP was less affected by cardiovascular deconditioning, which could be detected by BCG in agreement with standard markers of the cardiovascular condition. This shows the potential of BCG to easily monitor cardiac deconditioning.SCOPUS: ar.jinfo:eu-repo/semantics/publishe
Hamido Fujita - One of the best experts on this subject based on the ideXlab platform.
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computer aided detection of breathing disorder from Ballistocardiography signal using convolutional neural network
Information Sciences, 2020Co-Authors: Dalibor Cimr, Filip Studnicka, Hamido Fujita, Hana Tomaskova, Richard Cimler, Jitka Kuhnova, Jan SlegrAbstract:Abstract Sleep-related breathing disorders are diseases related to pharyngeal airway collapse. It can lead to several health problems such as somnolence, poorer daytime cognitive performance, and cardiovascular morbidity and mortality. However, computer-aided diagnostic (CAD) tools play a very important role in the detection of breathing disorders. It is possible to measure breathing activity, but most approaches require some type of device placed on the human body. This paper proposes a novel methodology of an unobtrusive CAD system to the breathing disorder detection. Unobtrusive approach is ensured by Ballistocardiography (BCG) sensors located on the measured bed. The significant pieces of information from the signals are extracted by Cartan curvatures. Thereafter, important features are separated from individual samples as an input to our 9-layer deep convolutional neural network. We achieved an average accuracy of 98.00%, sensitivity of 94.26%, and specificity of 99.22% on 4009 regular and 1307 disordered breathing samples.
Jin-oh Hahn - One of the best experts on this subject based on the ideXlab platform.
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unobtrusive estimation of cardiovascular parameters with limb Ballistocardiography
Sensors, 2019Co-Authors: Yang Yao, Ramakrishna Mukkamala, Sungtae Shin, Azin Mousavi, Changsei Kim, Jin-oh HahnAbstract:This study investigates the potential of the limb ballistocardiogram (BCG) for unobtrusive estimation of cardiovascular (CV) parameters. In conjunction with the reference CV parameters (including diastolic, pulse, and systolic pressures, stroke volume, cardiac output, and total peripheral resistance), an upper-limb BCG based on an accelerometer embedded in a wearable armband and a lower-limb BCG based on a strain gauge embedded in a weighing scale were instrumented simultaneously with a finger photoplethysmogram (PPG). To standardize the analysis, the more convenient yet unconventional armband BCG was transformed into the more conventional weighing scale BCG (called the synthetic weighing scale BCG) using a signal processing procedure. The characteristic features were extracted from these BCG and PPG waveforms in the form of wave-to-wave time intervals, wave amplitudes, and wave-to-wave amplitudes. Then, the relationship between the characteristic features associated with (i) the weighing scale BCG-PPG pair and (ii) the synthetic weighing scale BCG-PPG pair versus the CV parameters, was analyzed using the multivariate linear regression analysis. The results indicated that each of the CV parameters of interest may be accurately estimated by a combination of as few as two characteristic features in the upper-limb or lower-limb BCG, and also that the characteristic features recruited for the CV parameters were to a large extent relevant according to the physiological mechanism underlying the BCG.
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ballistocardiogram mechanism and potential for unobtrusive cardiovascular health monitoring
Scientific Reports, 2016Co-Authors: Changsei Kim, Omer T. Inan, Ramakrishna Mukkamala, Stephanie Ober, Sean M Mcmurtry, Barry A Finegan, Jin-oh HahnAbstract:For more than a century, it has been known that the body recoils each time the heart ejects blood into the arteries. These subtle cardiogenic body movements have been measured with increasingly convenient Ballistocardiography (BCG) instruments over the years. A typical BCG measurement shows several waves, most notably the "I", "J", and "K" waves. However, the mechanism for the genesis of these waves has remained elusive. We formulated a simple mathematical model of the BCG waveform. We showed that the model could predict the BCG waves as well as physiologic timings and amplitudes of the major waves. The validated model reveals that the principal mechanism for the genesis of the BCG waves is blood pressure gradients in the ascending and descending aorta. This new mechanistic insight may be exploited to allow BCG to realize its potential for unobtrusive monitoring and diagnosis of cardiovascular health and disease.
Filip Studnicka - One of the best experts on this subject based on the ideXlab platform.
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computer aided detection of breathing disorder from Ballistocardiography signal using convolutional neural network
Information Sciences, 2020Co-Authors: Dalibor Cimr, Filip Studnicka, Hamido Fujita, Hana Tomaskova, Richard Cimler, Jitka Kuhnova, Jan SlegrAbstract:Abstract Sleep-related breathing disorders are diseases related to pharyngeal airway collapse. It can lead to several health problems such as somnolence, poorer daytime cognitive performance, and cardiovascular morbidity and mortality. However, computer-aided diagnostic (CAD) tools play a very important role in the detection of breathing disorders. It is possible to measure breathing activity, but most approaches require some type of device placed on the human body. This paper proposes a novel methodology of an unobtrusive CAD system to the breathing disorder detection. Unobtrusive approach is ensured by Ballistocardiography (BCG) sensors located on the measured bed. The significant pieces of information from the signals are extracted by Cartan curvatures. Thereafter, important features are separated from individual samples as an input to our 9-layer deep convolutional neural network. We achieved an average accuracy of 98.00%, sensitivity of 94.26%, and specificity of 99.22% on 4009 regular and 1307 disordered breathing samples.
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automatic detection of breathing disorder from Ballistocardiography signals
Knowledge Based Systems, 2020Co-Authors: Dalibor Cimr, Filip StudnickaAbstract:Abstract Ballistocardiography (BCG) is a common method, wherein sensory information is used to identify blood-flow cardiac activity by measuring the mechanical micromovements of the human body generated by heart movements and blood eviction to the large arteries. BCG signals can be used to detect non-standard vital functions or predict likely health problems. However, the analysis of BCG signal is challenging because it contains various mechanical noises made by human body movements. This study is aimed at extracting information regarding the pulse arrival time from BCG signals and then establishing a connection with changes in breathing disorders, such as simulated apnoea, using convolutional neural networks. We present a novel approach toward recognizing the form of breathing which is independent of the body position while data are being collected from tensometers measuring the mechanical micromovements (motion) of the individual. The mechanical motions are caused by cardiac activity with multivariate time series output, which is processed to obtain the source data for breath detection. The signals are first processed by Cartan curvature. This is a differential geometric invariant, which enables the detection of marginal variations in the signals. Conditional dependency and short-term fluctuations are eliminated in longer measuring-periods. By these means, the breathing anomalies of individuals are subsequently detected between heartbeats using the time delay between the R-wave from the electrocardiogram (ECG) and the pulse arrival times. Moreover, ECG signals are included in the system for data sampling. In addition, the values of the time delay are used as the inputs to train a convolutional neural network classifier with two outputs (regular and disordered breathing) to validate the experiment. We achieved an average accuracy of 89.35%, sensitivity of 86.35%, and specificity of 91.22% on 828 regular and 1332 disordered breathing states from eight human subjects. The conclusion is that our novel method can detect disordered breathing from processed BCG signal, i.e. from the pulse arrival time, in a manner not previously used elsewhere.
