Renyi entropy measures of heart rate Gaussianity

Sample entropy and approximate entropy are measures that have been successfully utilized to study the deterministic dynamics of heart rate (HR). A complementary stochastic point of view and a heuristic argument using the Central Limit Theorem suggests that the Gaussianity of HR is a complementary measure of the physiological complexity of the underlying signal transduction processes. Renyi entropy (or q-entropy) is a widely used measure of Gaussianity in many applications. Particularly important members of this family are differential (or Shannon) entropy (q = 1) and quadratic entropy (q = 2). We introduce the concepts of differential and conditional Renyi entropy rate and, in conjunction with Burg's theorem, develop a measure of the Gaussianity of a linear random process. Robust algorithms for estimating these quantities are presented along with estimates of their standard errors.     

A smart health monitoring chair for nonintrusive measurement of biological signals

We developed nonintrusive methods for simultaneous electrocardiogram, photoplethysmogram, and ballistocardiogram measurements that do not require direct contact between instruments and bare skin. These methods were applied to the design of a diagnostic chair for unconstrained heart rate and blood pressure monitoring purposes. Our methods were operationalized through capacitively coupled electrodes installed in the chair back that include high-input impedance amplifiers, and conductive textiles installed in the seat for capacitive driven-right-leg circuit configuration that is capable of recording electrocardiogram information through clothing. Photoplethysmograms were measured through clothing using seat mounted sensors with specially designed amplifier circuits that vary in light intensity according to clothing type. Ballistocardiograms were recorded using a film type transducer material, polyvinylidenefluoride (PVDF), which was installed beneath the seat cover. By simultaneously measuring signals, beat-to-beat heart rates could be monitored even when electrocardiograms were not recorded due to movement artifacts. Beat-to-beat blood pressure was also monitored using unconstrained measurements of pulse arrival time and other physiological parameters, and our experimental results indicated that the estimated blood pressure tended to coincide with actual blood pressure measurements. This study demonstrates the feasibility of our method and device for biological signal monitoring through clothing for unconstrained long-term daily health monitoring that does not require user awareness and is not limited by physical activity.     

Detection of stress in coniferous forest trees with the VIRAFspectrometer

The Visible Infrared Reflectance Absorbance Fluorescence (VIRAF) spectrometer was applied in outdoor measurements to compare the needles of two spruce trees differently affected by the forest decline in the Northern Black Forest in Germany. Changes of the reflectance signatures due to loss of chlorophyll or other damage effects are interpreted with the help of other techniques currently used for the evaluation of physiological activity. The VIRAF spectrometer was used to examine the spectra of reflectance, absorption, and chlorophyll fluorescence. The measurements were carried out with the same sample without changing its position. By means of the two-wavelength chlorophyll fluorometer and pulse amplitude modulation fluorometer the fluorescence induction kinetics and several fluorescence parameters were determined as an indicator of vitality. In addition, the rate of photosynthetic CO₂ fixation and the stomatal closure were studied using a CO₂ H₂O porometer     

A real-time heart rate analysis for a remote millimeter wave I-Q sensor

This paper analyzes heart rate (HR) information from physiological tracings collected with a remote millimeter wave (mmW) I-Q sensor for biometric monitoring applications. A parameter optimization method based on the nonlinear Levenberg-Marquardt algorithm is used. The mmW sensor works at 94 GHz and can detect the vital signs of a human subject from a few to tens of meters away. The reflected mmW signal is typically affected by respiration, body movement, background noise, and electronic system noise. Processing of the mmW radar signal is, thus, necessary to obtain the true HR. The down-converted received signal in this case consists of both the real part (I-branch) and the imaginary part (Q-branch), which can be considered as the cosine and sine of the received phase of the HR signal. Instead of fitting the converted phase angle signal, the method directly fits the real and imaginary parts of the HR signal, which circumvents the need for phase unwrapping. This is particularly useful when the SNR is low. Also, the method identifies both beat-to-beat HR and individual heartbeat magnitude, which is valuable for some medical diagnosis applications. The mean HR here is compared to that obtained using the discrete Fourier transform.     

低成本非接触生命参数监测系统研究

本文介绍了一种基于过采样技术的低成本非接触式生理信号的高精度采集系统。由于传统采样ADC精度较低,而高精度采样ADC成本较高,本文采用过采样技术实现低成本、高精度数据采集系统的搭建。过采样是用采样速度换取数据精度的技术,因此,对于微弱生命信号的采集,过采样技术具有非常明显的优势。本文主要通过信号的信噪比与动态范围两个参数来系统地量化比较传统采样与过采样技术之间的优劣性。通过使用过采样技术,生理信号的采样量化噪声降低约40dB,提升了系统分辨率和动态范围。同时,本文实现了生理参数的非接触采集,使系统实用性有了较大提高。      

Linear and nonlinear ARMA model parameter estimation using anartificial neural network

Addresses parametric system identification of linear and nonlinear dynamic systems by analysis of the input and output signals. Specifically, the authors investigate the relationship between estimation of the system using a feedforward neural network model and estimation of the system by use of linear and nonlinear autoregressive moving-average (ARMA) models. By utilizing a neural network model incorporating a polynomial activation function, the authors show the equivalence of the artificial neural network to the linear and nonlinear ARMA models. They compare the parameterization of the estimated system using the neural network and ARMA approaches by utilizing data generated by means of computer simulations. Specifically, the authors show that the parameters of a simulated ARMA system can be obtained from the neural network analysis of the simulated data or by conventional least squares ARMA analysis. The feasibility of applying neural networks with polynomial activation functions to the analysis of experimental data is explored by application to measurements of heart rate (HR) and instantaneous lung volume (ILV) fluctuations     

An insulin infusion advisory system based on autotuning nonlinear model-predictive control

This paper aims at the development and evaluation of a personalized insulin infusion advisory system (IIAS), able to provide real-time estimations of the appropriate insulin infusion rate for type 1 diabetes mellitus (T1DM) patients using continuous glucose monitors and insulin pumps. The system is based on a nonlinear model-predictive controller (NMPC) that uses a personalized glucose-insulin metabolism model, consisting of two compartmental models and a recurrent neural network. The model takes as input patient's information regarding meal intake, glucose measurements, and insulin infusion rates, and provides glucose predictions. The predictions are fed to the NMPC, in order for the latter to estimate the optimum insulin infusion rates. An algorithm based on fuzzy logic has been developed for the on-line adaptation of the NMPC control parameters. The IIAS has been in silico evaluated using an appropriate simulation environment (UVa T1DM simulator). The IIAS was able to handle various meal profiles, fasting conditions, interpatient variability, intraday variation in physiological parameters, and errors in meal amount estimations.     

Real-time photoplethysmographic heart rate measurement using deep neural network filters

Photoplethysmography (PPG) is a noninvasive technique that can be used to conveniently measure heart rate (HR) and thus obtain relevant health-related information. However, developing an automated PPG system is difficult, because its waveforms are susceptible to motion artifacts and between-patient variation, making its interpretation difficult. We use deep neural network (DNN) filters to mimic the cognitive ability of a human expert who can distinguish the features of PPG altered by noise from various sources. Systolic (S), onset (O), and first derivative peaks (W) are recognized by three different DNN filters. In addition, the boundaries of uninformative regions caused by artifacts are identified by two different filters. The algorithm reliably derives the HR and presents recognition scores for the S, O, and W peaks and artifacts with only a 0.7-s delay. In the evaluation using data from 11 patients obtained from PhysioNet, the algorithm yields 8643 (86.12%) reliable HR measurements from a total of 10 036 heartbeats, including some with uninformative data resulting from arrhythmias and artifacts.     

Mathematical modeling and spectrum analysis of the physiologicalpatello-femoral pulse train produced by slow knee movement

Analysis of vibration signals emitted by the knee joint has the potential for the development of a noninvasive procedure for the diagnosis and monitoring of knee pathology. In order to obtain as much information as possible from the power density spectrum of the knee vibration signal, it is necessary to identify the physiological factors (or physiologically relevant parameters) that shape the spectrum. This paper presents a mathematical model for knee vibration signals, in particular the physiological patello-femoral pulse (PFP) train produced by slow knee movement. It demonstrates through the mathematical model that the repetition rate of the physiological PFP train introduces repeated peaks in the power spectrum, and that it affects the spectrum mainly at low frequencies. The theoretical results also show that the spectral peaks at multiples of the PFP repetition rate become more evident when the variance of the interpulse interval (IPI) is small, and that these spectral peaks shift toward higher frequencies with increasing PFP repetition rates. To evaluate the mathematical model, a simulation algorithm was developed, which generates PFP signals with adjustable repetition rate and IPI variance. Signals generated by simulation were seen to possess representative spectral characteristics typically observed in physiological PFP signals. This simulation procedure allows an interactive examination of several factors which affect the PFP train spectrum. Finally, in vivo measurements of physiological PFP signals of normal volunteers are presented. Results of simulations and analysis of signals recorded from human subjects support the mathematical model's prediction that the IPI statistics play a very significant role in determining the low-end power spectrum of the physiological PFP signal.(ABSTRACT TRUNCATED AT 250 WORDS)     

对10GHz DFB激光器参数的数值模拟及实验

对激光器速率方程所涉及的激光器的内部参数进行变换，得到一组新参数。它们可由实验中测得的激光器的数据通过曲线拟合的方法得到。利用拟合得到的参数对10GHz的分布反馈(DFB)半导体激光器(LD)进行数值求解，所得的结果和实验测量的结果相吻合。     

A Beat-to-Beat Heart Rate Meter

A new technique for computing beat-to-beat heart rate using a programmable counter is described. The circuit used is much less complex and consequently more economical than the circuits described elsewhere in the literature. Yet, it achieves acceptable accuracy in the rate measurements. The circuit can be conveniently modified to measure very low-frequency physiological phenomena such as respiration rate.     

2.5 GHz分布反馈激光器内部参数的实验测量

用实验方法对激光器速率方程所涉及的激光器内部参数进行了测定。然后利用该参数 ,通过半导体激光器的大信号速率方程 ,得到它的光输出脉冲波形和啁啾的数值解 ,对模拟结果与测量结果进行比较 ,二者非常吻合     

Design of a recognition system to predict movement duringanesthesia

The need for a reliable method of predicting movement during anesthesia has existed since the introduction of anesthesia. This paper proposes a recognition system, based on the autoregressive (AR) modeling and neural network analysis of the electroencephalograph (EEG) signals, to predict movement following surgical stimulation. The input to the neural network will be the AR parameters, the hemodynamic parameters blood pressure (BP) and heart rate (HR), and the anesthetic concentration in terms of the minimum alveolar concentration (MAC). The output will be the prediction of movement. Design of the system and results from the preliminary tests on dogs are presented here. The experiments were carried out on 13 dogs at different levels of halothane. Movement prediction was tested by monitoring the response to tail clamping, which is considered to be a supramaximal stimulus in dogs. The EEG data obtained prior to tail clamping was processed using a tenth-order AR model and the parameters obtained were used as input to a three-layer perceptron feedforward neural network. Using only AR parameters the network was able to correctly classify subsequent movement in 85% of the cases as compared to 65% when only hemodynamic parameters were used as the input to the network. When both the measures were combined, the recognition rate rose to greater than 92%. When the anesthetic concentration was added as an input the network could be considerably simplified without sacrificing classification accuracy. This recognition system shows the feasibility of using the EEG signals for movement during anesthesia     

Model-based estimation with boundary side information or boundary regularization [cardiac emission CT

The authors have previously developed a model-based strategy for joint estimation of myocardial perfusion and boundaries using ECT (emission computed tomography). They have also reported difficulties with boundary estimation in low contrast and low count rate situations. Here they propose using boundary side information (obtainable from high resolution MRI and CT images) or boundary regularization to improve both perfusion and boundary estimation in these situations. To fuse boundary side information into the emission measurements, the authors formulate a joint log-likelihood function to include auxiliary boundary measurements as well as ECT projection measurements. In addition, they introduce registration parameters to align auxiliary boundary measurements with ECT measurements and jointly estimate these parameters with other parameters of interest from the composite measurements. In simulated PET O-15 water myocardial perfusion studies using a simplified model, the authors show that the joint estimation improves perfusion estimation performance and gives boundary alignment accuracy of <0.5 mm even at 0.2 million counts. They implement boundary regularization through formulating a penalized log-likelihood function. They also demonstrate in simulations that simultaneous regularization of the epicardial boundary and myocardial thickness gives comparable perfusion estimation accuracy with the use of boundary side information.     

Nonlinear modeling and control of human heart rate response during exercise with various work load intensities

The first objective of this paper is to introduce a nonlinear system to model the heart rate (HR) response during and after treadmill walking exercise. The model is a feedback interconnected system that has components to describe the central and peripheral local responses to exercise and their interactions. The parameters of the model were experimentally identified from subjects walking on a treadmill at different speeds. The stability of the obtained nonlinear model was mathematically proven. The modeling results demonstrate that the proposed model can be useful in examining the cardiovascular response to exercise. Based on the nonlinear model, the second objective is to present a computer-controlled treadmill system for the regulation of HR during treadmill exercise. The proposed nonlinear controller consists of feedforward and feedback components. The designed control system was experimentally verified and the results demonstrated that the proposed computer-controlled treadmill system regulated the HR of the experimental subjects according to two different exercising HR profiles, indicating that it can play an important role in the design of exercise protocols for individuals.      

Determining land-surface parameters from the ERS wind scatterometer

The ERS-1 wind scatterometer (WSC) has a resolution cell of about 50 km but provides a high repetition rate (less than four days) and makes measurements at multiple incidence angles. In order to retrieve quantitative geophysical parameters over land surfaces using this instrument, a method is presented that applies a mixed-target modeling approach to estimate subpixel fractional vegetation cover at a regional scale. The model represents the footprint area as a combination of part dense, homogeneous vegetation and part bare soil (with homogeneous roughness and dielectric properties). Inversion of this model is then carried out using a retrieval procedure that incorporates a priori information in a quantitative manner The method is applied to the estimation of fractional cover over an area in Africa using WSC data from 1992 to 1995. Retrieved parameters are also compared to ground measurements made in the area during the 1992 HAPEX-Sahel campaign. The procedure illustrates the applicability of WSC data for measuring geophysical parameters over land and offers the potential of deriving a physically-based alternative to empirical indices for estimating regionally-variable parameters     

Engineering notion of mean-residual-life and hazard-rate for finitepopulations with known distributions

This article overviews some foundation issues in reliability and argues that generally it does not make sense to use classical notions of the hazard rate (HR) and mean residual life (MRL) to describe the HR and MRL of a device (the HR and failure rate are interchangeable terms). It provides a new methodology, using engineering notions of HR and MRL, to describe the HR and MRL of a device. and discusses the relevance of these methods in comparison to existing methods. In practice, the engineering notions of HR and MRL can be more complicated and more difficult to compute than the classical notions of these functions. Nevertheless, one should adhere to the procedures in this paper insofar as feasible because, as the examples indicate, the engineering notions of these functions dramatically differ from the classical notions. The finite populations have specified joint distributions     

Artificial Neural Network Expert System for Integrated Heart Rate Variability

This paper describes the combination of an expert system for bio-information with smart devices using a wireless sensor network. A wireless bio-sensor module acquires physiological signals, including electrocardiogram, heart rate, heart rate variability (HRV) and autonomic nervous system activity. The smart device transmits the bio-information over a wireless network to a real-time expert consultation function for analysis, storage and decision making. An artificial neural network algorithm detects the HRV parameters and examines them for features of diabetes. A centralized internet information service platform can interrogate the remote client at any time for its bio-information. In addition, the system platform can compare data files. Bio-information and diabetes information can trigger timely alert messages. The system described in this paper could be the basis for a ubiquitous mobile physiological monitor.