scholarly journals Detection of Impaired Sympathetic Cerebrovascular Control Using Functional Biomarkers Based on Principal Dynamic Mode Analysis

2017 ◽  
Vol 7 ◽  
Author(s):  
Saqib Saleem ◽  
Yu-Chieh Tzeng ◽  
W. Bastiaan Kleijn ◽  
Paul D. Teal
Keyword(s):  
Mode Analysis ◽  
Dynamic Mode ◽  
Functional Biomarkers

Principal dynamic mode analysis of neural mass model for the identification of epileptic states

2016 ◽  
Vol 26 (11) ◽  
pp. 113118 ◽  
Author(s):  
Yuzhen Cao ◽  
Liu Jin ◽  
Fei Su ◽  
Jiang Wang ◽  
Bin Deng
Keyword(s):  
Mode Analysis ◽  
Neural Mass Model ◽  
Dynamic Mode ◽  
Mass Model ◽  
Neural Mass

Principal dynamic mode analysis of action potential firing in a spider mechanoreceptor

2006 ◽  
Vol 96 (1) ◽  
pp. 113-127 ◽  
Author(s):  
Georgios D. Mitsis ◽  
Andrew S. French ◽  
Ulli Höger ◽  
Spiros Courellis ◽  
Vasilis Z. Marmarelis
Keyword(s):  
Action Potential ◽  
Mode Analysis ◽  
Dynamic Mode ◽  
Action Potential Firing ◽  
Potential Firing

scholarly journals Principal Dynamic Mode Analysis of the Hodgkin–Huxley Equations

2015 ◽  
Vol 25 (02) ◽  
pp. 1550001 ◽  
Author(s):  
Steffen E. Eikenberry ◽  
Vasilis Z. Marmarelis
Keyword(s):  
Model Reduction ◽  
System Structure ◽  
Model Complexity ◽  
Mode Analysis ◽  
Neuronal Membrane ◽  
Dynamic Mode ◽  
Model Framework ◽  
Static Nonlinearity ◽  
Out Of Sample ◽  
Out Of Sample Prediction

We develop an autoregressive model framework based on the concept of Principal Dynamic Modes (PDMs) for the process of action potential (AP) generation in the excitable neuronal membrane described by the Hodgkin–Huxley (H–H) equations. The model's exogenous input is injected current, and whenever the membrane potential output exceeds a specified threshold, it is fed back as a second input. The PDMs are estimated from the previously developed Nonlinear Autoregressive Volterra (NARV) model, and represent an efficient functional basis for Volterra kernel expansion. The PDM-based model admits a modular representation, consisting of the forward and feedback PDM bases as linear filterbanks for the exogenous and autoregressive inputs, respectively, whose outputs are then fed to a static nonlinearity composed of polynomials operating on the PDM outputs and cross-terms of pair-products of PDM outputs. A two-step procedure for model reduction is performed: first, influential subsets of the forward and feedback PDM bases are identified and selected as the reduced PDM bases. Second, the terms of the static nonlinearity are pruned. The first step reduces model complexity from a total of 65 coefficients to 27, while the second further reduces the model coefficients to only eight. It is demonstrated that the performance cost of model reduction in terms of out-of-sample prediction accuracy is minimal. Unlike the full model, the eight coefficient pruned model can be easily visualized to reveal the essential system components, and thus the data-derived PDM model can yield insight into the underlying system structure and function.

scholarly journals Principal dynamic mode analysis of hippocampal neuronal networks

2007 ◽  
Vol 8 (S2) ◽  
Author(s):  
Eunji Kang ◽  
Peter L Carlen ◽  
Berj Bardakjian
Keyword(s):  
Neuronal Networks ◽  
Mode Analysis ◽  
Dynamic Mode

scholarly journals Robust Mode Analysis

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1057
Author(s):  
Gemunu H. Gunaratne ◽  
Sukesh Roy
Keyword(s):  
Experimental Data ◽  
Bluff Body ◽  
Numerical Solutions ◽  
Dynamic Mode Decomposition ◽  
Mode Analysis ◽  
Jet Flows ◽  
Dynamic Mode ◽  
Reacting Flow ◽  
Model Free ◽  
Wide Range

In this paper, we introduce a model-free algorithm, robust mode analysis (RMA), to extract primary constituents in a fluid or reacting flow directly from high-frequency, high-resolution experimental data. It is expected to be particularly useful in studying strongly driven flows, where nonlinearities can induce chaotic and irregular dynamics. The lack of precise governing equations and the absence of symmetries or other simplifying constraints in realistic configurations preclude the derivation of analytical solutions for these systems; the presence of flow structures over a wide range of scales handicaps finding their numerical solutions. Thus, the need for direct analysis of experimental data is reinforced. RMA is predicated on the assumption that primary flow constituents are common in multiple, nominally identical realizations of an experiment. Their search relies on the identification of common dynamic modes in the experiments, the commonality established via proximity of the eigenvalues and eigenfunctions. Robust flow constituents are then constructed by combining common dynamic modes that flow at the same rate. We illustrate RMA using reacting flows behind a symmetric bluff body. Two robust constituents, whose signatures resemble symmetric and von Karman vortex shedding, are identified. It is shown how RMA can be implemented via extended dynamic mode decomposition in flow configurations interrogated with a small number of time-series. This approach may prove useful in analyzing changes in flow patterns in engines and propulsion systems equipped with sturdy arrays of pressure transducers or thermocouples. Finally, an analysis of high Reynolds number jet flows suggests that tests of statistical characterizations in turbulent flows may best be done using non-robust components of the flow.

Principal Dynamic Mode Analysis of Nonlinear Transduction in a Spider Mechanoreceptor

10.1114/1.149 ◽  
1999 ◽  
Vol 27 (3) ◽  
pp. 391-402 ◽  
Author(s):  
Vasilis Z. Marmarelis ◽  
Mikko Juusola ◽  
Andrew S. French
Keyword(s):  
Mode Analysis ◽  
Dynamic Mode

scholarly journals Combined Diagnostic Analysis of Dynamic Combustion Characteristics in a Scramjet Engine

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 4029
Author(s):  
Seung-Min Jeong ◽  
Jeong-Yeol Choi
Keyword(s):  
Flame Structure ◽  
Injection Pressure ◽  
Combustion Characteristics ◽  
Dynamic Mode Decomposition ◽  
Mode Analysis ◽  
Dynamic Mode ◽  
Diagnostic Analysis ◽  
Detached Eddy Simulation ◽  
Scramjet Engine ◽  
Dynamic Combustion

In this work, the dynamic combustion characteristics in a scramjet engine were investigated using three diagnostic data analysis methods: DMD (Dynamic Mode Decomposition), STFT (Short-Time Fourier Transform), and CEMA (Chemical Explosive Mode Analysis). The data for the analyses were obtained through a 2D numerical experiment using a DDES (Delayed Detached Eddy Simulation) turbulence model, the UCSD (University of California at San Diego) hydrogen/oxygen chemical reaction mechanism, and high-resolution schemes. The STFT was able to detect that oscillations above 50 kHz identified as dominant in FFT results were not the dominant frequencies in a channel-type combustor. In the analysis using DMD, it was confirmed that the critical point that induced a complete change of mixing characteristics existed between an injection pressure of 0.75 MPa and 1.0 MPa. A combined diagnostic analysis that included a CEMA was performed to investigate the dynamic combustion characteristics. The differences in the reaction steps forming the flame structure under each combustor condition were identified, and, through this, it was confirmed that the pressure distribution upstream of the combustor dominated the dynamic combustion characteristics of this scramjet engine. From these processes, it was confirmed that the combined analysis method used in this paper is an effective approach to diagnose the combustion characteristics of a supersonic combustor.

scholarly journals Deciphering Exhaled Aerosol Fingerprints for Early Diagnosis and Personalized Therapeutics of Obstructive Respiratory Diseases in Small Airways

2021 ◽  
Vol 2 (3) ◽  
pp. 94-117
Author(s):  
Xiuhua April Si ◽  
Jinxiang Xi
Keyword(s):  
Feature Extraction ◽  
Respiratory Diseases ◽  
Airway Remodeling ◽  
Dynamic Mode Decomposition ◽  
Mode Analysis ◽  
Support Vector ◽  
Dynamic Mode ◽  
Small Airways ◽  
Early Stages ◽  
Breath Testing

Respiratory diseases often show no apparent symptoms at their early stages and are usually diagnosed when permanent damages have been made to the lungs. A major site of lung pathogenesis is the small airways, which make it highly challenging to detect using current techniques due to the diseases’ location (inaccessibility to biopsy) and size (below normal CT/MRI resolution). In this review, we present a new method for lung disease detection and treatment in small airways based on exhaled aerosols, whose patterns are uniquely related to the health of the lungs. Proof-of-concept studies are first presented in idealized lung geometries. We subsequently describe the recent developments in feature extraction and classification of the exhaled aerosol images to establish the relationship between the images and the underlying airway remodeling. Different feature extraction algorithms (aerosol density, fractal dimension, principal mode analysis, and dynamic mode decomposition) and machine learning approaches (support vector machine, random forest, and convolutional neural network) are elaborated upon. Finally, future studies and frequent questions related to clinical applications of the proposed aerosol breath testing are discussed from the authors’ perspective. The proposed breath testing has clinical advantages over conventional approaches, such as easy-to-perform, non-invasive, providing real-time feedback, and is promising in detecting symptomless lung diseases at early stages.

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