Xinnian Chen

Automatic Selection of the Threshold Value

Calculation of approximate entropy (ApEn) requires a priori determination of two unknown parameters, m and r. While the recommended values of r, in the range of 0.1-0.2 times the standard deviation of the signal, have been shown to be applicable for a wide variety of signals, in certain cases, r values within this prescribed range can lead to an incorrect assessment of the complexity of a given signal. To circumvent this limitation, we recently advocated finding the maximum ApEn value by assessing all values of r from 0 to 1, and found that maximum ApEn does not always occur within the prescribed range of r values. Our results indicate that finding the maximum ApEn leads to the correct interpretation of a signals complexity. One major limitation, however, is that the calculation of all choices of r values is often impractical due to the computational burden. Our new method, based on a heuristic stochastic model, overcomes this computational burden, and leads to the automatic selection of the maximum ApEn value for any given signal. Based on Monte Carlo simulations, we derive general equations that can be used to estimate the maximum ApEn with high accuracy for a given value of m. Application to both synthetic and experimental data confirmed the advantages claimed with the proposed approach.

r

A portable, low-cost, and battery-powered wireless monitoring system that is capable of measuring multiple physiologic parameters simultaneously from many subjects was developed. The wireless communication of data is based on a commercially-available mote known as Tmote Sky. The star network topology (SNT), is used to collect data from many patients via multiple motes. Application protocol software was developed to facilitate the communication link between the monitor terminal and multiple motes. Based on the standard specifications of the mote, the SNT strategy, and the application protocol software design, a single mote can support up to 5 electrocardiogram signals with a sampling rate of 200 Hz. This capability facilitates affordable wireless monitoring of multiple physiologic signals from many subjects; its application is especially attractive for monitoring subjects in nursing homes, battlefields, and disaster scenarios

for Approximate Entropy

Numerous experimental preparations from neonatal rodents have been developed to study mechanisms responsible for respiratory rhythm generation. Amongst them, the in vivo anesthetized neonatal rat preparation and the in vitro medullary slice preparation from neonatal rat are commonly used. These two preparations not only contain a different extent of the neuroanatomical axis associated with central respiratory control, but they are also studied under markedly different conditions, all of which may affect the complex dynamics underlying the central inspiratory neural network. Here, we evaluated the approximate entropy (ApEn) underlying inspiratory motor bursts as an index of inspiratory neural network complexity from each preparation to address this possibility. Our findings suggest that the central inspiratory neural network of the in vivo anesthetized neonatal rat exhibits lower complexity (i.e., more order) than that observed in the in vitro transverse medullary slice preparation, both of which are substantially lower than that observed in more intact in vitro (e.g., arterially-perfused rat) and mature in vivo (e.g., anesthetized rat, piglet, cat) preparations. We suggest that additional studies be conducted to identify the precise mechanisms responsible for the differences in central inspiratory neural network complexity between these two neonatal rat preparations.

A portable, low-cost, battery-powered wireless monitoring system for obtaining varying physiologic parameters from multiple subjects.

In this paper, a mathematic model is applied to characterize spectral activity associated with fast oscillatory rhythms inherent in inspiratory discharges. Based on the estimated parameters, features are extracted to allow the model to discriminate between changes in the location, magnitude, and shape of spectral activities under basal conditions and during pharmacological blockade of gap junctions.