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Biological Signal

The Experts below are selected from a list of 13332 Experts worldwide ranked by ideXlab platform

Wenwen Tung – 1st expert on this subject based on the ideXlab platform

  • entropy measures for Biological Signal analyses
    Nonlinear Dynamics, 2012
    Co-Authors: Jing Hu, Wenwen Tung


    Entropies are among the most popular and promising complexity measures for Biological Signal analyses. Various types of entropy measures exist, including Shannon entropy, Kolmogorov entropy, approximate entropy (ApEn), sample entropy (SampEn), multiscale entropy (MSE), and so on. A fundamental question is which entropy should be chosen for a specific Biological application. To solve this issue, we focus on scaling laws of different entropy measures and introduce an ensemble forecasting framework to find the connections among them. One critical component of the ensemble forecasting framework is the scale-dependent Lyapunov exponent (SDLE), whose scaling behavior is found to be the richest among all the entropy measures. In fact, SDLE contains all the essential information of other entropy measures, and can act as a unifying multiscale complexity measure. Furthermore, SDLE has a unique scale separation property to aptly deal with nonstationarity and characterize high-dimensional and intermittent chaos. Therefore, SDLE can often be the first choice for exploratory studies in biology. The effectiveness of SDLE and the ensemble forecasting framework is illustrated by considering epileptic seizure detection from EEG.

Kwang Suk Park – 2nd expert on this subject based on the ideXlab platform

  • nonintrusive Biological Signal monitoring in a car to evaluate a driver s stress and health state
    Telemedicine Journal and E-health, 2009
    Co-Authors: Hyun Jae Baek, Jong Min Choi, Kwang Suk Park


    Nonintrusive monitoring of a driver’s physiological Signals was introduced and evaluated in a car as a test of extending the concept of ubiquitous healthcare to vehicles. Electrocardiogram, photoplethysmogram, galvanic skin response, and respiration were measured in the ubiquitous healthcare car (U-car) using nonintrusively installed sensors on the steering wheel, driver’s seat, and seat belt. Measured Signals were transmitted to the embedded computer via Bluetooth(R) communication and processed. We collected and analyzed physiological Signals during driving in order to estimate a driver’s stress state while using this system. In order to compare the effect of stress on physical and mental conditions, two categories of stresses were defined. Experimental results show that a driver’s physiological Signals were measured with acceptable quality for analysis without interrupting driving, and they were changed meaningfully due to elicited stress. This nonintrusive monitoring can be used to evaluate a driver’s state of health and stress. Language: en

  • Nonintrusive measurement of Biological Signals for ubiquitous healthcare
    Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, 2009
    Co-Authors: Kwang Suk Park


    In order to monitor Biological Signals during our daily lives for ubiquitous healthcare, nonintrusive Biological Signal monitoring methods have been developed. Without contacting sensors and connecting wires to the subjects, the Biological Signals are monitored using specially designed methods. ECG is measured using capacitive coupling over clothes and PPG is measured nonintrusively during ordinary activities. Blood pressure is also estimated from ECG and PPG by calculating pulse arrival time (PAT). These methods can be applied to evaluate the health levels of subjects without intervening in their ordinary daily activities.

Jim A Rogers – 3rd expert on this subject based on the ideXlab platform

  • emergent decision making in Biological Signal transduction networks
    Proceedings of the National Academy of Sciences of the United States of America, 2008
    Co-Authors: Tomas Helikar, Jim A Rogers, John Konvalina, Jack Heidel


    The complexity of biochemical intracellular Signal transduction networks has led to speculation that the high degree of interconnectivity that exists in these networks transforms them into an information processing network. To test this hypothesis directly, a large scale model was created with the logical mechanism of each node described completely to allow simulation and dynamical analysis. Exposing the network to tens of thousands of random combinations of inputs and analyzing the combined dynamics of multiple outputs revealed a robust system capable of clustering widely varying input combinations into equivalence classes of Biologically relevant cellular responses. This capability was nontrivial in that the network performed sharp, nonfuzzy classifications even in the face of added noise, a hallmark of real-world decision-making.