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Journal articles on the topic 'Seismocardiography'

Author: Grafiati

Published: 4 June 2021

Last updated: 3 February 2022

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1

Taebi, Amirtahà, Brian Solar, Andrew Bomar, Richard Sandler, and Hansen Mansy. "Recent Advances in Seismocardiography." Vibration 2, no. 1 (January 14, 2019): 64–86. http://dx.doi.org/10.3390/vibration2010005.

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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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2

Wick, Carson A., Omer T. Inan, James H. McClellan, and Srini Tridandapani. "Seismocardiography-Based Detection of Cardiac Quiescence." IEEE Transactions on Biomedical Engineering 62, no. 8 (August 2015): 2025–32. http://dx.doi.org/10.1109/tbme.2015.2411155.

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3

Wahlstrom, Johan, Isaac Skog, Peter Handel, Farzad Khosrow-khavar, Kouhyar Tavakolian, Phyllis K. Stein, and Arye Nehorai. "A Hidden Markov Model for Seismocardiography." IEEE Transactions on Biomedical Engineering 64, no. 10 (October 2017): 2361–72. http://dx.doi.org/10.1109/tbme.2017.2648741.

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4

Zoneraich, Samuel. "Seismocardiography is not a new term." American Journal of Cardiology 69, no. 5 (February 1992): 573. http://dx.doi.org/10.1016/0002-9149(92)91013-t.

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5

Paukkunen, Mikko, Matti Linnavuo, Helena Haukilehto, and Raimo Sepponen. "A System for Detection of Three-Dimensional Precordial Vibrations." International Journal of Measurement Technologies and Instrumentation Engineering 2, no. 1 (January 2012): 52–66. http://dx.doi.org/10.4018/ijmtie.2012010104.

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Accelerometer-based seismocardiography and sternal acceleration ballistocardiography are promising approaches to the noninvasive detection of precordial vibrations. However, in order to be widely accepted as diagnostic or even prognostic tools, clinical validation and standardization of these methods are necessary. In precordial vibration studies, using all three axes instead of one in cardiac vibration analysis is anticipated to enable more accurate cardiac event detection. Simultaneously acquired electrocardiography, photoplethysmography, and respiration information are considered as promising ways to enhance seismocardiogram analysis. In this article, an easy-to-use system that combines three-dimensional seismocardiography, electrocardiography, photoplethysmography, and respiration measurements is described, and its performance is demonstrated. In the test measurements, the system demonstrated its capability to capture accurate cardiovascular data.

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6

Salerno, David M., John M. Zanetti, Liviu C. Poliac, Richard S. Crow, Peter J. Hannan, Kyuhyun Wang, Irvin F. Goldenberg, and Robert A. Van Tassel. "Exercise Seismocardiography for Detection of Coronary Artery Disease." American Journal of Noninvasive Cardiology 6, no. 5 (1992): 321–30. http://dx.doi.org/10.1159/000470383.

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7

Shirkovskiy, P., A. Laurin, N. Jeger-Madiot, D. Chapelle, M. Fink, and R. K. Ing. "Airborne ultrasound surface motion camera: Application to seismocardiography." Applied Physics Letters 112, no. 21 (May 21, 2018): 213702. http://dx.doi.org/10.1063/1.5028348.

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8

D'Mello, Yannick, James Skoric, Shicheng Xu, Megan Akhras, Philip J. R. Roche, Michel A. Lortie, Stephane Gagnon, and David V. Plant. "Autocorrelated Differential Algorithm for Real-Time Seismocardiography Analysis." IEEE Sensors Journal 19, no. 13 (July 1, 2019): 5127–40. http://dx.doi.org/10.1109/jsen.2019.2903449.

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9

Inan, Omer T., Pierre-Francois Migeotte, Kwang-Suk Park, Mozziyar Etemadi, Kouhyar Tavakolian, Ramon Casanella, John Zanetti, et al. "Ballistocardiography and Seismocardiography: A Review of Recent Advances." IEEE Journal of Biomedical and Health Informatics 19, no. 4 (July 2015): 1414–27. http://dx.doi.org/10.1109/jbhi.2014.2361732.

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10

Sandler, Richard H., Khurshidul Azad, Badrun Rahman, Amirtaha Taebi, Peshala Gamage, Nirav Raval, Robert J. Mentz, and Hansen A. Mansy. "Minimizing Seismocardiography Variability by Accounting for Respiratory Effects." Journal of Cardiac Failure 25, no. 8 (August 2019): S185. http://dx.doi.org/10.1016/j.cardfail.2019.07.521.

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11

Vavrinský, Erik, Daniela Moskal'vá, Martin Darříček, Martin Donoval, František Horínek, Marían Popovič, and Peter Miklovič. "Application of Acceleration Sensors in Physiological Experiments." Journal of Electrical Engineering 65, no. 5 (September 1, 2014): 304–8. http://dx.doi.org/10.2478/jee-2014-0049.

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Abstract This paper illustrates a promising application of an accelerometer sensor in physiological research, we demonstrated use of accelerometers for monitoring the standard proband physical activity (PA) and also in special applications like respiration and mechanical heart activity, the so-called seismocardiography (SCG) monitoring, physiological activation monitoring and mechanomyography (MMG)

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12

Munck, Kim, Kasper Sørensen, Johannes J. Struijk, and Samuel E. Schmidt. "Multichannel seismocardiography: an imaging modality for investigating heart vibrations." Physiological Measurement 41, no. 11 (December 11, 2020): 115001. http://dx.doi.org/10.1088/1361-6579/abc0b7.

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13

Sørensen, Kasper, Mathias Krogh Poulsen, Dan Stieper Karbing, Peter Søgaard, Johannes Jan Struijk, and Samuel Emil Schmidt. "A Clinical Method for Estimation of VO2max Using Seismocardiography." International Journal of Sports Medicine 41, no. 10 (May 26, 2020): 661–68. http://dx.doi.org/10.1055/a-1144-3369.

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AbstractThe purpose of this study was to investigate the correlation between the seismocardiogram and cardiorespiratory fitness. Cardiorespiratory fitness can be estimated as VO2max using non-exercise algorithms, but the results can be inaccurate. Healthy subjects were recruited for this study. Seismocardiogram and electrocardiogram were recorded at rest. VO2max was measured during a maximal effort cycle ergometer test. Amplitudes and timing intervals were extracted from the seismocardiogram and used in combination with demographic data in a non-exercise prediction model for VO2max. 26 subjects were included, 17 females. Mean age: 38.3±9.1 years. The amplitude following the aortic valve closure derived from the seismocardiogram had a significant correlation of 0.80 (p<0.001) to VO2max. This feature combined with age, sex and BMI in the prediction model, yields a correlation to VO2max of 0.90 (p<0.001, 95% CI: 0.83–0.94) and a standard error of the estimate of 3.21 mL·kg−1·min−1 . The seismocardiogram carries information about the cardiorespiratory fitness. When comparing to other non-exercise models the proposed model performs better, even after cross validation. The model is limited when tracking changes in VO2max. The method could be used in the clinic for a more accurate estimation of VO2max compared to current non-exercise methods.

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14

Ачильдиев, В. М., Ю. Н. Евсеева, Н. А. Бедро, В. А. Солдатенков, Ю. К. Грузевич, М. Н. Комарова, А. Д. Левкович, and В. М. Успенский. "ЦИФРОВАЯ ОБРАБОТКА И АНАЛИЗ ЭЛЕКТРОСЕЙСМОКАРДИОСИГНАЛОВ." NANOINDUSTRY Russia 96, no. 3s (June 15, 2020): 374–80. http://dx.doi.org/10.22184/1993-8578.2020.13.3s.374.380.

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В статье описаны параметры и вариабельность кардиосигналов, измеренных с помощью MEMS-гироскопов и акселерометров, особенности обработки и анализа в качестве входных данных для неинвазивной диагностики заболеваний человека. The paper considers an electroseismocardiography system that allows simultaneously registering electrocardiography and seismocardiography signals. The algorithm of the electroseismocardiography system operation has been developed. Experiments and analysis of the obtained results have been carried out.

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15

Salerno, David M., and John Zanetti. "Seismocardiography for Monitoring Changes in Left Ventricular Function during Ischemia." Chest 100, no. 4 (October 1991): 991–93. http://dx.doi.org/10.1378/chest.100.4.991.

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16

Morra, Sofia, Amin Hossein, Damien Gorlier, Jérémy Rabineau, Martin Chaumont, Pierre-François Migeotte, and Philippe Van De Borne. "Ballistocardiography and seismocardiography detection of hemodynamic changes during simulated obstructive apnea." Physiological Measurement 41, no. 6 (July 9, 2020): 065007. http://dx.doi.org/10.1088/1361-6579/ab924b.

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17

Choudhary, Tilendra, L. N. Sharma, and M. K. Bhuyan. "Automatic Detection of Aortic Valve Opening Using Seismocardiography in Healthy Individuals." IEEE Journal of Biomedical and Health Informatics 23, no. 3 (May 2019): 1032–40. http://dx.doi.org/10.1109/jbhi.2018.2829608.

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18

Johnson, Ethan M. I., J. Alex Heller, Florencia Garcia Vicente, Roberto Sarnari, Daniel Gordon, Patrick M. McCarthy, Alex J. Barker, Mozziyar Etemadi, and Michael Markl. "Detecting Aortic Valve-Induced Abnormal Flow with Seismocardiography and Cardiac MRI." Annals of Biomedical Engineering 48, no. 6 (March 16, 2020): 1779–92. http://dx.doi.org/10.1007/s10439-020-02491-3.

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19

Jain, Puneet Kumar, Anil Kumar Tiwari, and Vijay S. Chourasia. "Performance analysis of seismocardiography for heart sound signal recording in noisy scenarios." Journal of Medical Engineering & Technology 40, no. 3 (February 9, 2016): 106–18. http://dx.doi.org/10.3109/03091902.2016.1139203.

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20

Shandhi, Md Mobashir Hasan, Joanna Fan, Alex J. Heller, Mozziyar Etemadi, Omer T. Inan, and Liviu Klein. "Seismocardiography Can Assess Cardiopulmonary Exercise Test Parameters in Patients with Heart Failure." Journal of Cardiac Failure 24, no. 8 (August 2018): S124—S125. http://dx.doi.org/10.1016/j.cardfail.2018.07.446.

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21

Hossein, Amin, Jérémy Rabineau, Damien Gorlier, Jose Ignacio Juarez Del Rio, Philippe van de Borne, Pierre-François Migeotte, and Antoine Nonclercq. "Kinocardiography Derived from Ballistocardiography and Seismocardiography Shows High Repeatability in Healthy Subjects." Sensors 21, no. 3 (January 26, 2021): 815. http://dx.doi.org/10.3390/s21030815.

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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 (<15 min) and long term (3–6 h) on 60 healthy volunteers. Additionally, we investigated the difference of repeatability with different body positions. First, we found high short-term TRT reliability for KCG metrics derived from SCG and BCG recordings. Exceptions to this finding were limited to metrics computed in left lateral decubitus position where the TRT reliability was moderate-to-high. Second, we found low-to-moderate long-term TRT reliability for KCG metrics as expected and confirmed by blood pressure measurements. In summary, KCG parameters derived from BCG/SCG signals show high repeatability and should be further investigated to confirm their use for cardiac condition longitudinal monitoring.

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22

Crow, Richard, David M. Salerno, Peter Hunnan, and John M. Zanetti. "Seismocardiography for measurement of left ventricular performance at rest and immediately post exercise." Journal of the American College of Cardiology 17, no. 2 (February 1991): A352. http://dx.doi.org/10.1016/0735-1097(91)92374-u.

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23

Yao, Jingting, Srini Tridandapani, Carson A. Wick, and Pamela T. Bhatti. "Seismocardiography-Based Cardiac Computed Tomography Gating Using Patient-Specific Template Identification and Detection." IEEE Journal of Translational Engineering in Health and Medicine 5 (2017): 1–14. http://dx.doi.org/10.1109/jtehm.2017.2708100.

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24

Salemo, David M., John M. Zanetti, Lisa A. Green, Kyuhyun Wang, Irvin Goldenberg, and Robert A. Van Tassel. "Qualitative exercise seismocardiography for detection of moderate and severe multivessel coronary artery disease." Journal of the American College of Cardiology 15, no. 2 (February 1990): A44. http://dx.doi.org/10.1016/0735-1097(90)91897-4.

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25

Postolache, Octavian, Pedro Girão, and Gabriela Postolache. "Seismocardiogram and Ballistocardiogram Sensing." International Journal of Measurement Technologies and Instrumentation Engineering 1, no. 3 (July 2011): 67–88. http://dx.doi.org/10.4018/ijmtie.2011070106.

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The paper describes the latest development in seismocardiography and ballistocardiography, including sensors with or without mechanical contact with the body, for cardiac functions monitoring in common daily activity. The authors discuss the information related with the seismocardiogramn (SCG) and ballistocardiogram (BCG) and the work on SCG and BCG modeling. The latest advances reported on the devices aiming at BCG and SCG cardiovascular system evaluation are covered, highlighting their key features and novel concepts. The authors also underscore the applications of ElectroMechanical film (EMFi) sensors, MEMS accelerometers and radar sensing technology for vital signs monitoring. Discussion on the current developments and future improvements are included in the paper.

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26

Sidikova, Michaela, Radek Martinek, Aleksandra Kawala-Sterniuk, Martina Ladrova, Rene Jaros, Lukas Danys, and Petr Simonik. "Vital Sign Monitoring in Car Seats Based on Electrocardiography, Ballistocardiography and Seismocardiography: A Review." Sensors 20, no. 19 (October 6, 2020): 5699. http://dx.doi.org/10.3390/s20195699.

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This paper focuses on a thorough summary of vital function measuring methods in vehicles. The focus of this paper is to summarize and compare already existing methods integrated into car seats with the implementation of inter alia capacitive electrocardiogram (cECG), mechanical motion analysis Ballistocardiography (BCG) and Seismocardiography (SCG). In addition, a comprehensive overview of other methods of vital sign monitoring, such as camera-based systems or steering wheel sensors, is also presented in this article. Furthermore, this work contains a very thorough background study on advanced signal processing methods and their potential application for the purpose of vital sign monitoring in cars, which is prone to various disturbances and artifacts occurrence that have to be eliminated.

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27

D’Mello, Skoric, Xu, Roche, Lortie, Gagnon, and Plant. "Real-Time Cardiac Beat Detection and Heart Rate Monitoring from Combined Seismocardiography and Gyrocardiography." Sensors 19, no. 16 (August 8, 2019): 3472. http://dx.doi.org/10.3390/s19163472.

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Cardiography is an indispensable element of health care. However, the accessibility of at-home cardiac monitoring is limited by device complexity, accuracy, and cost. We have developed a real-time algorithm for heart rate monitoring and beat detection implemented in a custom-built, affordable system. These measurements were processed from seismocardiography (SCG) and gyrocardiography (GCG) signals recorded at the sternum, with concurrent electrocardiography (ECG) used as a reference. Our system demonstrated the feasibility of non-invasive electro-mechanical cardiac monitoring on supine, stationary subjects at a cost of $100, and with the SCG–GCG and ECG algorithms decoupled as standalone measurements. Testing was performed on 25 subjects in the supine position when relaxed, and when recovering from physical exercise, to record 23,984 cardiac cycles at heart rates in the range of 36–140 bpm. The correlation between the two measurements had r2 coefficients of 0.9783 and 0.9982 for normal (averaged) and instantaneous (beat identification) heart rates, respectively. At a sampling frequency of 250 Hz, the average computational time required was 0.088 s per measurement cycle, indicating the maximum refresh rate. A combined SCG and GCG measurement was found to improve accuracy due to fundamentally different noise rejection criteria in the mutually orthogonal signals. The speed, accuracy, and simplicity of our system validated its potential as a real-time, non-invasive, and affordable solution for outpatient cardiac monitoring in situations with negligible motion artifact.

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28

Yu, Shuai, and Sheng Liu. "A Novel Adaptive Recursive Least Squares Filter to Remove the Motion Artifact in Seismocardiography." Sensors 20, no. 6 (March 13, 2020): 1596. http://dx.doi.org/10.3390/s20061596.

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This paper presents a novel adaptive recursive least squares filter (ARLSF) for motion artifact removal in the field of seismocardiography (SCG). This algorithm was tested with a consumer-grade accelerometer. This accelerometer was placed on the chest wall of 16 subjects whose ages ranged from 24 to 35 years. We recorded the SCG signal and the standard electrocardiogram (ECG) lead I signal by placing one electrode on the right arm (RA) and another on the left arm (LA) of the subjects. These subjects were asked to perform standing and walking movements on a treadmill. ARLSF was developed in MATLAB to process the collected SCG and ECG signals simultaneously. The SCG peaks and heart rate signals were extracted from the output of ARLSF. The results indicate a heartbeat detection accuracy of up to 98%. The heart rates estimated from SCG and ECG are similar under both standing and walking conditions. This observation shows that the proposed ARLSF could be an effective method to remove motion artifact from recorded SCG signals.

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29

Yao, Jingting, S. Tridandapani, W. F. Auffermann, C. A. Wick, and P. T. Bhatti. "An Adaptive Seismocardiography (SCG)-ECG Multimodal Framework for Cardiac Gating Using Artificial Neural Networks." IEEE Journal of Translational Engineering in Health and Medicine 6 (2018): 1–11. http://dx.doi.org/10.1109/jtehm.2018.2869141.

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30

Yao, J., S. Tridandapani, and P. T. Bhatti. "Near Real-Time Implementation of An Adaptive Seismocardiography – ECG Multimodal Framework for Cardiac Gating." IEEE Journal of Translational Engineering in Health and Medicine 7 (2019): 1–4. http://dx.doi.org/10.1109/jtehm.2019.2923353.

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31

Korzeniowska-Kubacka, Iwona, Maria Bilinska, and Ryszard Piotrowicz. "Usefulness of Seismocardiography for the Diagnosis of Ischemia in Patients with Coronary Artery Disease." Annals of Noninvasive Electrocardiology 10, no. 3 (July 2005): 281–87. http://dx.doi.org/10.1111/j.1542-474x.2005.00547.x.

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32

Wick, C. A., Jin-Jyh Su, J. H. McClellan, O. Brand, P. T. Bhatti, A. L. Buice, A. E. Stillman, Xiangyang Tang, and Srini Tridandapani. "A System for Seismocardiography-Based Identification of Quiescent Heart Phases: Implications for Cardiac Imaging." IEEE Transactions on Information Technology in Biomedicine 16, no. 5 (September 2012): 869–77. http://dx.doi.org/10.1109/titb.2012.2198071.

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33

Di Rienzo, M., E. Vaini, P. Castiglioni, G. Merati, P. Meriggi, G. Parati, A. Faini, and F. Rizzo. "Wearable seismocardiography: Towards a beat-by-beat assessment of cardiac mechanics in ambulant subjects." Autonomic Neuroscience 178, no. 1-2 (November 2013): 50–59. http://dx.doi.org/10.1016/j.autneu.2013.04.005.

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34

BUCHINGER, W., G. BINTER, B. SCHUBERT, B. EBER, W. KLEIN, D. BRANDT, and O. EBER. "The value of seismocardiography and Thallium-201-scintigraphy in the detection of coronary artery disease." Journal of Nuclear Cardiology 2, no. 2 (March 1995): S75. http://dx.doi.org/10.1016/s1071-3581(05)80385-x.

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35

Shandhi, Md Mobashir Hasan, Beren Semiz, Sinan Hersek, Nazli Goller, Farrokh Ayazi, and Omer T. Inan. "Performance Analysis of Gyroscope and Accelerometer Sensors for Seismocardiography-Based Wearable Pre-Ejection Period Estimation." IEEE Journal of Biomedical and Health Informatics 23, no. 6 (November 2019): 2365–74. http://dx.doi.org/10.1109/jbhi.2019.2895775.

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36

Pouyan, Maziyar Bran, Sean Dowling, Teresa De Marco, Liviu Klein, and Omer T. Inan. "Quantifying the Accuracy of Heart Failure Decompensation Classification Using Wearable Seismocardiography and Graph Mining Algorithms." Journal of Cardiac Failure 23, no. 8 (August 2017): S125—S126. http://dx.doi.org/10.1016/j.cardfail.2017.07.369.

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37

Lee, Hyoung Youn, Yong Hun Jung, Kyung Woon Jeung, Dong Hun Lee, Byung Kook Lee, Geuk Young Jang, Tong In Oh, Najmiddin Mamadjonov, and Tag Heo. "Discrimination between the presence and absence of spontaneous circulation using smartphone seismocardiography: A preliminary investigation." Resuscitation 166 (September 2021): 66–73. http://dx.doi.org/10.1016/j.resuscitation.2021.07.009.

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38

Ashouri, Hazar, Sinan Hersek, and Omer T. Inan. "Universal Pre-Ejection Period Estimation Using Seismocardiography: Quantifying the Effects of Sensor Placement and Regression Algorithms." IEEE Sensors Journal 18, no. 4 (February 15, 2018): 1665–74. http://dx.doi.org/10.1109/jsen.2017.2787628.

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39

Morra, Sofia, Amin Hossein, Damien Gorlier, Jérémy Rabineau, Martin Chaumont, Pierre-François Migeotte, and Philippe van de Borne. "Modification of the mechanical cardiac performance during end-expiratory voluntary apnea recorded with ballistocardiography and seismocardiography." Physiological Measurement 40, no. 10 (November 4, 2019): 105005. http://dx.doi.org/10.1088/1361-6579/ab4a6a.

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40

Morra, Sofia, Anais Gauthey, Amin Hossein, Jérémy Rabineau, Judith Racape, Damien Gorlier, Pierre-François Migeotte, Jean Benoit le Polain de Waroux, and Philippe van de Borne. "Influence of sympathetic activation on myocardial contractility measured with ballistocardiography and seismocardiography during sustained end-expiratory apnea." American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 319, no. 4 (October 1, 2020): R497—R506. http://dx.doi.org/10.1152/ajpregu.00142.2020.

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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 ([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 ( P = 0.01, P = 0.002, and P = 0.001, respectively), whereas [Formula: see text] 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.

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41

Johnson, Ethan M. I., Mozziyar Etemadi, S. Chris Malaisrie, Patrick M. McCarthy, Michael Markl, and Alex J. Barker. "Seismocardiography and 4D flow MRI reveal impact of aortic valve replacement on chest acceleration and aortic hemodynamics." Journal of Cardiac Surgery 35, no. 1 (October 15, 2019): 232–35. http://dx.doi.org/10.1111/jocs.14289.

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42

Morra, Sofia, Lorenzo Pitisci, Fuhong Su, Amin Hossein, Jérémy Rabineau, Judith Racape, Damien Gorlier, Pierre François Migeotte, Jacques Creteur, and Philippe Van De Borne. "QUANTIFICATION OF CARDIAC KINETIC ENERGY AND ITS CHANGES DURING TRANSMURAL MYOCARDIAL INFARCTION ASSESSED BY MULTI-DIMENSIONAL SEISMOCARDIOGRAPHY." Journal of Hypertension 39, Supplement 1 (April 2021): e104. http://dx.doi.org/10.1097/01.hjh.0000745452.31495.bd.

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43

Sieciński, Szymon, Paweł S. Kostka, and Ewaryst J. Tkacz. "Gyrocardiography: A Review of the Definition, History, Waveform Description, and Applications." Sensors 20, no. 22 (November 22, 2020): 6675. http://dx.doi.org/10.3390/s20226675.

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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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44

Shandhi, Mobashir Md Hasan, Joanna Fan, J. Alex Heller, Mozziyar Etemadi, Omer T. Inan, and Liviu Klein. "Seismocardiography and Machine Learning Algorithms to Assess Clinical Status of Patients with Heart Failure in Cardiopulmonary Exercise Testing." Journal of Cardiac Failure 25, no. 8 (August 2019): S64—S65. http://dx.doi.org/10.1016/j.cardfail.2019.07.184.

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Uspensky, V. M., V. M. Achildiev, N. A. Bedro, V. A. Soldatenkov, Y. K. Gruzevich, Y. N. Evseeva, M. N. Komarova, and A. D. Levkovich. "A seismocardiography system and a possibility of its use for diagnosis of internal organs diseases using seismocardiogram information analysis." Journal of Physics: Conference Series 1379 (November 2019): 012066. http://dx.doi.org/10.1088/1742-6596/1379/1/012066.

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Liu, Yuhao, James J. S. Norton, Raza Qazi, Zhanan Zou, Kaitlyn R. Ammann, Hank Liu, Lingqing Yan, et al. "Epidermal mechano-acoustic sensing electronics for cardiovascular diagnostics and human-machine interfaces." Science Advances 2, no. 11 (November 2016): e1601185. http://dx.doi.org/10.1126/sciadv.1601185.

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Physiological mechano-acoustic signals, often with frequencies and intensities that are beyond those associated with the audible range, provide information of great clinical utility. Stethoscopes and digital accelerometers in conventional packages can capture some relevant data, but neither is suitable for use in a continuous, wearable mode, and both have shortcomings associated with mechanical transduction of signals through the skin. We report a soft, conformal class of device configured specifically for mechano-acoustic recording from the skin, capable of being used on nearly any part of the body, in forms that maximize detectable signals and allow for multimodal operation, such as electrophysiological recording. Experimental and computational studies highlight the key roles of low effective modulus and low areal mass density for effective operation in this type of measurement mode on the skin. Demonstrations involving seismocardiography and heart murmur detection in a series of cardiac patients illustrate utility in advanced clinical diagnostics. Monitoring of pump thrombosis in ventricular assist devices provides an example in characterization of mechanical implants. Speech recognition and human-machine interfaces represent additional demonstrated applications. These and other possibilities suggest broad-ranging uses for soft, skin-integrated digital technologies that can capture human body acoustics.

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Rai, Deepak, Hiren Kumar Thakkar, Shyam Singh Rajput, Jose Santamaria, Chintan Bhatt, and Francisco Roca. "A Comprehensive Review on Seismocardiogram: Current Advancements on Acquisition, Annotation, and Applications." Mathematics 9, no. 18 (September 12, 2021): 2243. http://dx.doi.org/10.3390/math9182243.

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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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Lee, Hyunwoo, Hana Lee, and Mincheol Whang. "An Enhanced Method to Estimate Heart Rate from Seismocardiography via Ensemble Averaging of Body Movements at Six Degrees of Freedom." Sensors 18, no. 1 (January 15, 2018): 238. http://dx.doi.org/10.3390/s18010238.

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Martinek, Radek, Jindrich Brablik, Jakub Kolarik, Martina Ladrova, Jan Nedoma, Rene Jaros, Lukas Soustek, et al. "A Low-Cost System for Seismocardiography-Based Cardiac Triggering: A Practical Solution for Cardiovascular Magnetic Resonance Imaging at 3 Tesla." IEEE Access 7 (2019): 118608–29. http://dx.doi.org/10.1109/access.2019.2936184.

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Wilson, Richard A., Virinderjit S. Bamrah, Joseph Lindsay, Markus Schwaiger, and Joel Morganroth. "Diagnostic accuracy of seismocardiography compared with electrocardiography for the anatomic and physiologic diagnosis of coronary artery disease during exercise testing." American Journal of Cardiology 71, no. 7 (March 1993): 536–45. http://dx.doi.org/10.1016/0002-9149(93)90508-a.

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