Extensions to a manifold learning framework for time-series analysis on dynamic manifolds in bioelectric signals
| Autor: | Damon E. Hyde, Burak Erem, Jurriaan M. Peters, Dana H. Brooks, Petr Stovicek, Ramon Martinez Orellana, Rob S. MacLeod, Gilead Tadmor, Frank H. Duffy, Simon K. Warfield |
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| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Time Factors
Dynamical systems theory Chaotic Signal-To-Noise Ratio 01 natural sciences Article 010305 fluids & plasmas Machine Learning Electrocardiography 0103 physical sciences Humans 010306 general physics Dynamical system (definition) Mathematics business.industry Data domain Nonlinear dimensionality reduction Brain Pattern recognition Electroencephalography Heart Signal Processing Computer-Assisted Lorenz system Electrophysiological Phenomena Nonlinear Dynamics Embedding Artificial intelligence Focus (optics) business |
| Popis: | This paper addresses the challenge of extracting meaningful information from measured bioelectric signals generated by complex, large scale physiological systems such as the brain or the heart. We focus on a combination of the well-known Laplacian eigenmaps machine learning approach with dynamical systems ideas to analyze emergent dynamic behaviors. The method reconstructs the abstract dynamical system phase-space geometry of the embedded measurements and tracks changes in physiological conditions or activities through changes in that geometry. It is geared to extract information from the joint behavior of time traces obtained from large sensor arrays, such as those used in multiple-electrode ECG and EEG, and explore the geometrical structure of the low dimensional embedding of moving time windows of those joint snapshots. Our main contribution is a method for mapping vectors from the phase space to the data domain. We present cases to evaluate the methods, including a synthetic example using the chaotic Lorenz system, several sets of cardiac measurements from both canine and human hearts, and measurements from a human brain. |
| Databáze: | OpenAIRE |
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