Relevant bibliographies by topics / Heart Sounds Mathematical models

Academic literature on the topic 'Heart Sounds Mathematical models'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 February 2022

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Journal articles on the topic "Heart Sounds Mathematical models"

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Narváez, Pedro, and Winston S. Percybrooks. "Synthesis of Normal Heart Sounds Using Generative Adversarial Networks and Empirical Wavelet Transform." Applied Sciences 10, no. 19 (October 8, 2020): 7003. http://dx.doi.org/10.3390/app10197003.

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Currently, there are many works in the literature focused on the analysis of heart sounds, specifically on the development of intelligent systems for the classification of normal and abnormal heart sounds. However, the available heart sound databases are not yet large enough to train generalized machine learning models. Therefore, there is interest in the development of algorithms capable of generating heart sounds that could augment current databases. In this article, we propose a model based on generative adversary networks (GANs) to generate normal synthetic heart sounds. Additionally, a denoising algorithm is implemented using the empirical wavelet transform (EWT), allowing a decrease in the number of epochs and the computational cost that the GAN model requires. A distortion metric (mel–cepstral distortion) was used to objectively assess the quality of synthetic heart sounds. The proposed method was favorably compared with a mathematical model that is based on the morphology of the phonocardiography (PCG) signal published as the state of the art. Additionally, different heart sound classification models proposed as state-of-the-art were also used to test the performance of such models when the GAN-generated synthetic signals were used as test dataset. In this experiment, good accuracy results were obtained with most of the implemented models, suggesting that the GAN-generated sounds correctly capture the characteristics of natural heart sounds.
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Tian, Xin, and Zhong Tan. "Analysis and decision of heart sounds via ARMA models." Measurement 5, no. 3 (July 1987): 102–6. http://dx.doi.org/10.1016/s0263-2241(87)80009-x.

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Xin, T. "Analysis and decision of heart sounds via ARMA models." Measurement 5, no. 3 (September 1987): 102–6. http://dx.doi.org/10.1016/0263-2241(87)90011-x.

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Quarteroni, A., A. Manzoni, and C. Vergara. "The cardiovascular system: Mathematical modelling, numerical algorithms and clinical applications." Acta Numerica 26 (May 1, 2017): 365–590. http://dx.doi.org/10.1017/s0962492917000046.

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Mathematical and numerical modelling of the cardiovascular system is a research topic that has attracted remarkable interest from the mathematical community because of its intrinsic mathematical difficulty and the increasing impact of cardiovascular diseases worldwide. In this review article we will address the two principal components of the cardiovascular system: arterial circulation and heart function. We will systematically describe all aspects of the problem, ranging from data imaging acquisition, stating the basic physical principles, analysing the associated mathematical models that comprise PDE and ODE systems, proposing sound and efficient numerical methods for their approximation, and simulating both benchmark problems and clinically inspired problems. Mathematical modelling itself imposes tremendous challenges, due to the amazing complexity of the cardiocirculatory system, the multiscale nature of the physiological processes involved, and the need to devise computational methods that are stable, reliable and efficient. Critical issues involve filtering the data, identifying the parameters of mathematical models, devising optimal treatments and accounting for uncertainties. For this reason, we will devote the last part of the paper to control and inverse problems, including parameter estimation, uncertainty quantification and the development of reduced-order models that are of paramount importance when solving problems with high complexity, which would otherwise be out of reach.
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Price, G. Richard, Joel T. Kalb, and Georges R. Garinther. "Toward a Measure of Auditory Handicap in the Army." Annals of Otology, Rhinology & Laryngology 98, no. 5_suppl (May 1989): 42–52. http://dx.doi.org/10.1177/00034894890980s508.

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The effect of a soldier's ability to hear on the capacity to perform a mission was calculated for a variety of militarily relevant tasks through the use of mathematical models. Changes in hearing can result from organic loss, hearing protectors, the masking effect of noises, etc. The effects were calculated for the detection of sounds of enemy personnel (speech, movement noises) or their equipment (rifle bolt, tank, generator). We also calculated the effects on the ability to control/communicate with troops verbally. The normal ear is highly effective in detecting noises of personnel or their equipment or in understanding speech, even in noise. By contrast, even modest hearing losses and/or the wearing of hearing protectors can have profound effects on military performance, for example, reducing the area that can be monitored acoustically by more than 30-fold or cutting warning times for other sounds by a factor of more than 100. Hearing protectors may have the conflicting effects of protecting hearing while producing unacceptable performance because of their attenuation.
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Zemlyakov, Ivan, Dmitry Zhdanov, Yana Kostelei, Anton Seleznev, and Artem Bureev. "Mathematical model of heart sounds." IOP Conference Series: Materials Science and Engineering 862 (May 28, 2020): 042021. http://dx.doi.org/10.1088/1757-899x/862/4/042021.

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Shahmohammadi, Mehrdad, Hongxing Luo, Philip Westphal, Richard N. Cornelussen, Frits W. Prinzen, and Tammo Delhaas. "Hemodynamics-driven mathematical model of first and second heart sound generation." PLOS Computational Biology 17, no. 9 (September 22, 2021): e1009361. http://dx.doi.org/10.1371/journal.pcbi.1009361.

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We propose a novel, two-degree of freedom mathematical model of mechanical vibrations of the heart that generates heart sounds in CircAdapt, a complete real-time model of the cardiovascular system. Heart sounds during rest, exercise, biventricular (BiVHF), left ventricular (LVHF) and right ventricular heart failure (RVHF) were simulated to examine model functionality in various conditions. Simulated and experimental heart sound components showed both qualitative and quantitative agreements in terms of heart sound morphology, frequency, and timing. Rate of left ventricular pressure (LV dp/dtmax) and first heart sound (S1) amplitude were proportional with exercise level. The relation of the second heart sound (S2) amplitude with exercise level was less significant. BiVHF resulted in amplitude reduction of S1. LVHF resulted in reverse splitting of S2 and an amplitude reduction of only the left-sided heart sound components, whereas RVHF resulted in a prolonged splitting of S2 and only a mild amplitude reduction of the right-sided heart sound components. In conclusion, our hemodynamics-driven mathematical model provides fast and realistic simulations of heart sounds under various conditions and may be helpful to find new indicators for diagnosis and prognosis of cardiac diseases. New & noteworthy To the best of our knowledge, this is the first hemodynamic-based heart sound generation model embedded in a complete real-time computational model of the cardiovascular system. Simulated heart sounds are similar to experimental and clinical measurements, both quantitatively and qualitatively. Our model can be used to investigate the relationships between heart sound acoustic features and hemodynamic factors/anatomical parameters.
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Ryzhov, Oleg S. "Transition length in turbine/compressor blade flows." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 462, no. 2072 (March 7, 2006): 2281–98. http://dx.doi.org/10.1098/rspa.2005.1651.

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High Reynolds number mathematical models for convected vortex impinging against a local hump and sound scattering into Tollmien–Schlichting eigenmodes are introduced to simulate basic mechanisms of the disturbance excitation typical of turbomachinery environments. The streamwise dimension of the transitional flow on the suction side of a blade is evaluated on the assumption that the transition length is of equal order with the extent of viscous/inviscid interaction controlling the boundary-layer response. The triple-deck theory gives a simple power law correlation to express a value of the Reynolds number based on the transition length in terms of the Reynolds number calculated with the blade cord. Precisely the same correlation stems from processing experimental data for both smooth and rough surfaces. The computation shows the explosive development of highly modulated wave packets and their rapid breakdown brought about by erratic short-scaled wiggles riding on the primary long-scaled oscillation cycles. The filling-up of distant parts of the wavenumber spectrum is at the heart of the signal distortion. This process heralds the start of deep transition terminating in fully developed turbulent flow well before reaching the upper stability branch. With the time-harmonic excitation broadly used in experiments, transition requires a much longer distance to complete. An agreement between theoretical predictions based on the assumption of indefinitely large Reynolds numbers and experimental findings from wind-tunnel observations at finite Reynolds numbers is encouraging.
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Rudnitskii, A. G., M. A. Rudnytska, and L. V. Tkachenko. "SINGLE-CHANNEL PROCESSING OF AUSCULTATORY SIGNALS USING METHODS OF MATHEMATICAL MORPHOLOGY." Journal of Numerical and Applied Mathematics, no. 1 (135) (2021): 179–85. http://dx.doi.org/10.17721/2706-9699.2021.1.24.

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The paper considers a new method of separating respiratory sounds from heart sounds in a general signal registered on the surface of the human body. The proposed approach is based on a combination of Bayesian noise suppression techniques and methods of mathematical morphology. The proposed method was tested on real auscultatory signals. Evaluation of the efficiency of the algorithm using auditory, visual and numerical analysis shows that the developed approach is a promising alternative to existing techniques for separating auscultatory signals into its natural components.
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Soto-Murillo, Manuel A., Jorge I. Galván-Tejada, Carlos E. Galván-Tejada, Jose M. Celaya-Padilla, Huizilopoztli Luna-García, Rafael Magallanes-Quintanar, Tania A. Gutiérrez-García, and Hamurabi Gamboa-Rosales. "Automatic Evaluation of Heart Condition According to the Sounds Emitted and Implementing Six Classification Methods." Healthcare 9, no. 3 (March 12, 2021): 317. http://dx.doi.org/10.3390/healthcare9030317.

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The main cause of death in Mexico and the world is heart disease, and it will continue to lead the death rate in the next decade according to data from the World Health Organization (WHO) and the National Institute of Statistics and Geography (INEGI). Therefore, the objective of this work is to implement, compare and evaluate machine learning algorithms that are capable of classifying normal and abnormal heart sounds. Three different sounds were analyzed in this study; normal heart sounds, heart murmur sounds and extra systolic sounds, which were labeled as healthy sounds (normal sounds) and unhealthy sounds (murmur and extra systolic sounds). From these sounds, fifty-two features were calculated to create a numerical dataset; thirty-six statistical features, eight Linear Predictive Coding (LPC) coefficients and eight Cepstral Frequency-Mel Coefficients (MFCC). From this dataset two more were created; one normalized and one standardized. These datasets were analyzed with six classifiers: k-Nearest Neighbors, Naive Bayes, Decision Trees, Logistic Regression, Support Vector Machine and Artificial Neural Networks, all of them were evaluated with six metrics: accuracy, specificity, sensitivity, ROC curve, precision and F1-score, respectively. The performances of all the models were statistically significant, but the models that performed best for this problem were logistic regression for the standardized data set, with a specificity of 0.7500 and a ROC curve of 0.8405, logistic regression for the normalized data set, with a specificity of 0.7083 and a ROC curve of 0.8407, and Support Vector Machine with a lineal kernel for the non-normalized data; with a specificity of 0.6842 and a ROC curve of 0.7703. Both of these metrics are of utmost importance in evaluating the performance of computer-assisted diagnostic systems.
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Dissertations / Theses on the topic "Heart Sounds Mathematical models"

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Ewing, Gary John. "A new approacch to the analysis of the third heart sound." Title page, contents and summary only, 1988. http://web4.library.adelaide.edu.au/theses/09SM/09sme95.pdf.

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Hinch, Robert. "Mathematical models of the heart." Thesis, University of Oxford, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.270632.

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Romero-Vivas, Eduardo. "Hidden Markovian models applied to the analysis of heart sounds for diagnostic purposes." Thesis, University of Southampton, 2006. https://eprints.soton.ac.uk/425886/.

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Thalassoudis, Kym. "Numerical studies of flow through prosthetic heart valves /." Title page, contents and summary only, 1987. http://web4.library.adelaide.edu.au/theses/09PH/09pht365.pdf.

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Satterlee, Cody Michael. "The Effects of Spatial and Temporal Properties on a Viscoelastic Model of the Dyssynchronous Canine Heart." Thesis, North Dakota State University, 2011. https://hdl.handle.net/10365/29327.

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In this study, lumped parameter cardiovascular modeling has been used to understand the influence of muscle properties on mechanical dyssynchrony (MD) as well as general muscle dynamics. Incorporating viscous influence into the model allowed for an expanded view when analyzing muscle parameter response to MD. A unique method of ventricle segmentation was introduced that allowed fast analysis of regional and global ventricular properties. This segmentation process produced a ventricle with four identical sections each consisting of separately tunable muscle properties in the form of minimum and maximum elastance, elastance waveform delay, and myocardial viscous friction, yet these regional sections remained globally dependent. Elastance waveform delay proved to be the most influential property on MD as measured by internal flow fraction (IFF), followed by regional elastance magnitude, and finally regional viscosity influence. Due to the unique segmentation of this model, two metrics for IFF were derived: (1) the "true" IFF (IFF-4seg) and (2) the IFF as would be measured by an ideal conductance catheter (IFF-CC). The results of IFF-CC versus IFF-4seg show that conductance catheters are not capable of measuring IFF during a side-to-side volume transfer within the stacked cylinder under measurement. Finally, unique energetic situations were observed with this model that point to likely myocardium remodeling situations.
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Lemmon, Jack David Jr. "Three-dimensional computational modeling of fluid-structure interaction : study of diastolic function in a thin-walled left heart model." Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/15912.

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McCorkle, Tricia Dawn. "Math, music, and membranes: A historical survey of the question "can one hear the shape of a drum"?" CSUSB ScholarWorks, 2005. https://scholarworks.lib.csusb.edu/etd-project/2933.

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In 1966 Mark Kac posed an interesting question regarding vibrating membranes and the sounds they make. His article entitled "Can One Hear the Shape of a Drum?", which appeared in The American Mathematical Monthly, generated much interest and scholarly debate. The evolution of Kac's intriguing question will be the subject of this project.
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Sher, Anna. "Modelling local calcium dynamics and the sodium/calcium exchanger in ventricular myocytes." Thesis, University of Oxford, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.670114.

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Nash, Martyn. "Mechanics and material properties of the heart using an anatomically accurate mathematical model." Thesis, University of Auckland, 1998. http://hdl.handle.net/2292/84.

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Global and regional mechanics of the cardiac ventricles were investigated using an anatomicallyaccurate computational model formulated from concise mathematical descriptions ofthe left and right ventricular wall geometries and the non-homogeneous laminar microstructureof cardiac muscle. The finite element method for finite deformation elasticity was developedfor the analysis and included specialised coordinate systems, interpolation schemesand parallel processing techniques for greater computational efficiency.The ventricular mechanics model incorporated the fully orthotropic pole-zero constitutivelaw, based on the three-dimensional architecture of myocardium, to account for the nonlinearmaterial response of resting cardiac muscle, relative to the three anatomically relevant axes.A fibre distribution model was introduced to reconcile some of the pole-zero constitutiveparameters with direct mechanical properties of the tissue (such as the limiting strainsestimated from detailed physiological observations of the collagen helices that surroundmyofibres), whilst other parameters were estimated from in-vitro biaxial tension tests onthin sections of myocardium. A non-invasive approach to in-vivo myocardial materialparameter estimation was also developed, based on a magnetic resonance imaging techniqueto effectively tag ventricular wall tissue.The spatially non-homogeneous distribution of myocardial residual strain was accounted forin the ventricular mechanics model using a specialised growth tensor. A simple model of fluidshift was formulated to account for the changes in local tissue volume due to movement ofintramyocardial blood. Contractile properties of ventricular myofibres were approximatedusing a quasi-static relationship between the fibre extension ratio, intracellular calciumconcentration and active fibre stress, and the framework has been developed to include amore realistic model of active myocardial mechanics, which could be coupled to a realisticdescription of the time-varying spread of electrical excitation throughout the ventricularwalls. Simple volumetric cavity models were incorporated to investigate the effects of arterialimpedance on systolic wall mechanics.Ventricular mechanics model predictions of the cavity pressure versus volume relationships,longitudinal dimension changes, torsional wall deformations and regional distributions ofmyocardial strain during the diastolic filling, isovolumic contraction and ejection phasesof the cardiac cycle showed good overall agreement with reported observations derivedfrom experimental studies of isolated and in-vivo canine hearts. Predictions of the spatialdistributions of mechanical stress at end-diastole and end-systole are illustrated.
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Wandler, Jeff. "Calculating Cardiovascular Lumped-Parameter Model Values by Injecting Small Volume Perturbations in an Isovolumic Heart." Thesis, North Dakota State University, 2011. https://hdl.handle.net/10365/28884.

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Diagnosing cardiac patient problems contains many uncertainties, and to fully diagnose the patient's condition usually requires a lengthy drug regimen to see what works and what does not. Compounding this problem is that even after the correct drug regimen has been discovered, the underlying cause for the problem may remain a mystery. Thus, the uncertainty and the length of time required to provide an accurate and adequate solution makes it very difficult to provide quality care to the patient. Templeton and others have shown that lumped cardiac muscle parameters can be extracted from an isolated heart by injecting small volumes at high frequencies relative to the heart rate and measuring the pressure response to this volume change. Using the Hill muscle model of two springs and a dash pot to portray the different elements of the cardiac muscle, the pressure and volume relationship makes it possible to calculate these muscle parameters using frequency response analysis techniques. The hypothesis to be tested is "Is it possible to develop a method to test cardiac muscle for stiffness, resistance, and contractile force from measuring ventricular pressure and injected flow?" To test this hypothesis, an isovolumic heart model is developed and allowed to develop pressure, along with a small volume injected to create a pressure response. Analysis of the pressure and flow waveforms produces a measured value of the cardiac model parameter values to compare to the model values. Results from injecting small volume changes into a mathematical heart model show that it is possible to extract the muscle model parameters of non-linear resistance, inertia of the fluid and muscle, and stiffness of the muscle while filling and contracting. The injected frequency and volume were varied to find usable conditions, both with regard to the calculations and the practical limits. Analyzing the error between the measured and model values for a large number of different combinations of model parameters shows an average error of less than 1%.
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Books on the topic "Heart Sounds Mathematical models"

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Beer, Stafford. The heart of enterprise. Chichester: John Wiley & Sons, 1994.

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Schroter, V. STEAM, Sound from Trains Environmental Analysis Method. [Toronto]: Ontario Ministry of the Environment, 1990.

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Schroter, V. STEAM, sound from trains environmental analysis method: Report. [Ontario]: Queen's Printer for Ontario, 1990.

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K, Cheng Leo, and Buist Martin L, eds. Mathematical modelling the electrical activity of the heart: From cell to body surface and back again. New Jersey: World Scientific, 2005.

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1943-, Othmer H. G., and National Science Foundation (U.S.), eds. Some mathematical questions in biology: The dynamics of excitable media. Providence, R.I: American Mathematical Society, 1989.

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Darowski, Marek. Comprehensive models of cardiovascular and respiratory systems: Their mechanical support and interactions. New York: Nova Science, 2010.

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Kantor, B. I͡A. Nelineĭnai͡a kardiobiomekhanika levogo zheludochka. Kiev: Nauk. dumka, 1991.

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service), SpringerLink (Online, ed. Introduction to Computational Cardiology: Mathematical Modeling and Computer Simulation. Boston, MA: Springer Science+Business Media, LLC, 2010.

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K, Dana Syamal, Roy Prodyot K, and Kurths J. (Jürgen) 1953-, eds. Complex dynamics in physiological systems: From heart to brain. [Dordrecht]: Springer, 2009.

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Titomir, L. I. Bioelectric and biomagnetic fields: Theory and applications in electrocardiology. Boca Raton: CRC Press, 1994.

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Book chapters on the topic "Heart Sounds Mathematical models"

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Patil, Kiran Kumari, B. S. Nagabhushan, and B. P. Vijay Kumar. "Psychoacoustic Models for Heart Sounds." In Communications in Computer and Information Science, 556–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17878-8_56.

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Clark, J. W., J. M. Shumaker, C. R. Murphey, and W. R. Giles. "Mathematical Models of Pacemaker Tissue in the Heart." In Institute for Nonlinear Science, 255–88. New York, NY: Springer New York, 1991. http://dx.doi.org/10.1007/978-1-4612-3118-9_11.

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Majumder, Rupamanjari, Alok Ranjan Nayak, and Rahul Pandit. "An Overview of Spiral- and Scroll-Wave Dynamics in Mathematical Models for Cardiac Tissue." In Heart Rate and Rhythm, 269–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17575-6_14.

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Karadag, Ilyas E., Martin Bishop, Patrick W. Hales, Jürgen E. Schneider, Peter Kohl, David Gavaghan, and Vicente Grau. "Regionally Optimised Mathematical Models of Cardiac Myocyte Orientation in Rat Hearts." In Functional Imaging and Modeling of the Heart, 294–301. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21028-0_36.

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Bell, Michael M., and Elizabeth M. Cherry. "Computational Cardiac Electrophysiology: Implementing Mathematical Models of Cardiomyocytes to Simulate Action Potentials of the Heart." In Methods in Molecular Biology, 65–74. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2572-8_5.

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Adeler, P. T., and J. M. Jacobsen. "3. Blood Flow in the Heart." In Applied Mathematical Models in Human Physiology, 35–71. Society for Industrial and Applied Mathematics, 2004. http://dx.doi.org/10.1137/1.9780898718287.ch3.

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Danielsen, M. "4. The Ejection Effect of the Pumping Heart." In Applied Mathematical Models in Human Physiology, 73–89. Society for Industrial and Applied Mathematics, 2004. http://dx.doi.org/10.1137/1.9780898718287.ch4.

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Bortfeld, Heather, Kathleen Shaw, and Nicole Depowski. "The Miracle Year." In Theoretical and Computational Models of Word Learning, 153–71. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2973-8.ch007.

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In recent years, a functional perspective on infant communication has emerged whereby infants’ production of vocal sounds is understood not only in terms of the acoustic properties of those sounds, but also in terms of the sounds that regulate and are regulated by social interactions with those hearing them. Here, the authors synthesize findings across several disciplines to characterize this holistic view of infant language learning. The goal is to interpret classic and more recent behavioral findings (e.g., on infants’ preferences) in light of data on pre- and postnatal neurophysiological responses to the environment (e.g., fetal heart rate, cortical blood flow). Language learning is a complex process that takes place at multiple levels across multiple systems; this review is an attempt to embrace this complexity and provide an integrated account of how these systems interact to support language learning.
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Wang, Vicky Y., Martyn P. Nash, Ian J. LeGrice, Alistair A. Young, Bruce H. Smaill, and Peter J. Hunter. "Mathematical models of cardiac structure and function: mechanistic insights from models of heart failure." In Cardiac Mechano-Electric Coupling and Arrhythmias, 241–50. Oxford University Press, 2011. http://dx.doi.org/10.1093/med/9780199570164.003.0034.

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Goldberg, David E. "John H. Holland, Facetwise Models, and Economy of Thought." In Perspectives on Adaptation in Natural and Artificial Systems. Oxford University Press, 2005. http://dx.doi.org/10.1093/oso/9780195162929.003.0008.

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Noted complex adaptive system researcher John H. Holland now receives acclaim from many quarters, but it is important to understand that this man and his ideas have been controversial since the beginning of his career. Genetic algorithms (GAs) were ignored or disparaged throughout the 1960s and 1970s, and even now, as these and his other ideas receive worldwide recognition in broad outline, the specifics of his mode of thought and insight are rejected by many who claim to embrace his key insights. This is a mistake. I have known John Holland for 23 years, and I have learned many things from him, but a critical influence has been his style of thought, in particular, his style of modeling. John has an uncanny knack of getting to the heart of a matter through the construction of what I call little models. Sometimes these models are verbal, sometimes they are mathematical, but they almost always shed a great deal of light on some nagging question in the analysis and design of complex systems. In this chapter, I propose to briefly explore John Holland's style of little modeling, and better understand its nature, its essence, and why some of those who embrace the broad outlines of his teaching have been slow to embrace the details of his modeling and the style of his thought. The exploration begins by recalling my own first impressions of John Holland and his style of thought, impressions made 23 years ago in a classroom in Ann Arbor, Michigan. It continues with a case study in Holland-style facetwise model building in constructing a takeover time model. It continues by integrating the takeover time model with a model of innovation on dimensional grounds. Finally, the Hollandian mode of model building is placed on intellectual terra firma with an economic argument, suggesting that the costs of modeling or thought must be weighed in relation to the model's benefits in understanding or designing a complex system.
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Conference papers on the topic "Heart Sounds Mathematical models"

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Alexander, Ben, Gabriel Nallathambi, and Nandakumar Selvaraj. "Screening of Heart Sounds Using Hidden Markov and Gammatone Filterbank Models." In 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2018. http://dx.doi.org/10.1109/icmla.2018.00237.

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Ushenin, K. S., A. Dokuchaev, S. M. Magomedova, O. V. Sopov, V. V. Kalinin, and O. Solovyova. "Models of human heart and torso electrophysiology verified against clinical data." In Mathematical Biology and Bioinformatics. Pushchino: IMPB RAS - Branch of KIAM RAS, 2018. http://dx.doi.org/10.17537/icmbb18.41.

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Abubakar, Mohammed Mansur, and Taner Tuncer. "Heart Sounds Classification Using Hybrid CNN Architecture." In International Students Science Congress. Izmir International Guest Student Association, 2021. http://dx.doi.org/10.52460/issc.2021.023.

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In this paper, we propose a hybrid model for diagnosing heart conditions by analyzing heart sounds and signals. The Hybrid CNN (Convolutional Neural Network) model is trained to classify distinguishable pathological heart sounds into three classes; normal, murmur, and extrasystole. Scalogram images of heart sounds were obtained by applying wavelet transform to heart sound signals. Images are inputs for Resnet50 and Resnet101 CNN models. The feature vectors of these architectures in the fc1000 layer are combined. Relief feature selection algorithm was applied to the obtained feature vector, and then the classification was performed with the support vector machine algorithm. Training the proposed model resulted in accuracy of 92.75%, thus, making it the best performing model in comparison to other models in this paper.
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Zhao, Zhidong, and Qinqin Shen. "A human identification system based on Heart sounds and Gaussian Mixture Models." In 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI). IEEE, 2011. http://dx.doi.org/10.1109/bmei.2011.6098471.

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Cheffer de Melo, Augusto Luiz, and Marcelo Savi. "RANDOMNESS EFFECT ON HEART DYNAMICS ANALYSIS USING MATHEMATICAL MODELS." In 25th International Congress of Mechanical Engineering. ABCM, 2019. http://dx.doi.org/10.26678/abcm.cobem2019.cob2019-0721.

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Shi, Wenyu, and Meng-Sang Chew. "Mathematical and physical models of a total artificial heart." In 2009 IEEE International Conference on Control and Automation (ICCA). IEEE, 2009. http://dx.doi.org/10.1109/icca.2009.5410410.

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Beritelli, Francesco, and Andrea Spadaccini. "An improved biometric identification system based on heart sounds and Gaussian Mixture Models." In 2010 IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications (BIOMS). IEEE, 2010. http://dx.doi.org/10.1109/bioms.2010.5610442.

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Yamashita, Masaru, Masataka Himeshima, and Shoichi Matsunaga. "Robust classification between normal and abnormal lung sounds using adventitious-sound and heart-sound models." In ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014. http://dx.doi.org/10.1109/icassp.2014.6854437.

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Mayorga, Pedro, Daniela Ibarra, Vesna Zeljkovic, and Christopher Druzgalski. "Quartiles and Mel Frequency Cepstral Coefficients vectors in Hidden Markov-Gaussian Mixture Models classification of merged heart sounds and lung sounds signals." In 2015 International Conference on High Performance Computing & Simulation (HPCS). IEEE, 2015. http://dx.doi.org/10.1109/hpcsim.2015.7237053.

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Nielsen, B. F., M. Lysaker, C. Tarrou, M. C. MacLachlan, A. Abildgaard, and A. Tveito. "On the use of st-segment shifts and mathematical models for identifying ischemic heart disease." In Computers in Cardiology, 2005. IEEE, 2005. http://dx.doi.org/10.1109/cic.2005.1588280.

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