1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

Photoplethysmography (PPG) is a simple and low-cost optical technique that can be used to detect blood volume changes in the microvascular bed of tissue. It is often used non-invasively to make measurements at the skin surface. The PPG waveform comprises a pulsatile ('AC') physiological waveform attributed to cardiac synchronous changes in the blood volume with each heart beat, and is superimposed on a slowly varying ('DC') baseline with various lower frequency components attributed to respiration, sympathetic nervous system activity and thermoregulation. Although the origins of the components of the PPG signal are not fully understood, it is generally accepted that they can provide valuable information about the cardiovascular system. There has been a resurgence of interest in the technique in recent years, driven by the demand for low cost, simple and portable technology for the primary care and community based clinical settings, the wide availability of low cost and small semiconductor components, and the advancement of computer-based pulse wave analysis techniques. The PPG technology has been used in a wide range of commercially available medical devices for measuring oxygen saturation, blood pressure and cardiac output, assessing autonomic function and also detecting peripheral vascular disease. The introductory sections of the topical review describe the basic principle of operation and interaction of light with tissue, early and recent history of PPG, instrumentation, measurement protocol, and pulse wave analysis. The review then focuses on the applications of PPG in clinical physiological measurements, including clinical physiological monitoring, vascular assessment and autonomic function.

https://iopscience.iop.org/article/10.1088/0967-3334/28/3/R01/pdf

Photoplethysmography and its application in clinical physiological measurement.

: This thesis applies neural network feature selection techniques to multivariate time series data to improve prediction of a target time series. Two approaches to feature selection are used. First, a subset enumeration method is used to determine which financial indicators are most useful for aiding in prediction of the S&P 500 futures daily price. The candidate indicators evaluated include RSI, Stochastics and several moving averages. Results indicate that the Stochastics and RSI indicators result in better prediction results than the moving averages. The second approach to feature selection is calculation of individual saliency metrics. A new decision boundary-based individual saliency metric, and a classifier independent saliency metric are developed and tested. Ruck's saliency metric, the decision boundary based saliency metric, and the classifier independent saliency metric are compared for a data set consisting of the RSI and Stochastics indicators as well as delayed closing price values. The decision based metric and the Ruck metric results are similar, but the classifier independent metric agrees with neither of the other metrics. The nine most salient features, determined by the decision boundary based metric, are used to train a neural network and the results are presented and compared to other published results. (AN)

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Nonlinear Time Series Analysis.

Generalized predictive control—Part II. Extensions and interpretations

Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics

A nonlinear scheme was used for the analysis of variability in the heart beat interval [R-R interval (RRI)] data to differentiate heat-intolerant humans from the heat tolerant. All subjects studied had previously suffered exertional heatstroke. Core temperature (Tre ) and electrocardiogram data from 11 heat-tolerant (HT) and 6 heat-intolerant (HIT) males were studied, the grouping being based on the distinguishing rate of rise in Tre versus time up to 39degC during submaximal exercise. The RRI data were subjected to wavelet transformation and the transformed data were utilized to generate weighted singular value (WSV) distribution profiles. The normalized WSV profiles merged together for the HT subjects, but remained widely dispersed for the HIT subjects. From WSV profiles of five HT subjects a standard WSV template (wt) was constructed and with respect to wt the cumulative square error (epsiv) for individual WSV profiles for a cohort of six (additional) HT and six HIT subjects was analyzed. In terms of epsiv, HT and HIT groups could be differentiated with the sensitivity and the specificity exceeding 83%. The strength of the WSV profiles in characterizing processes is also demonstrated using synthetic data

Weighted singular value distribution of RRI series applied to the characterization of heat intolerance in humans

An instrument for noninvasive continuous measurement of blood pressure using a finger cuff

Fetal ECG Extraction from Maternal ECG Using the Singular Value Decomposition

Singular value decomposition (SVD) is used to determine the prime periodicity in a quasiperiodic signal. The detected periodicity may not be sinusoidal. The period length is determined using the SVR spectrum. The prime periodic component can be extracted using SV decomposition.

Analysis of quasiperiodic physiological signals using SVR spectrum

The problem of discrete-time modelling of processes with input and output data is addressed. The objective is to produce the most representative model with adequate numbers of parameters. Conventional methods involve a computationally inefficient method of selection through exhaustive trials of all candidate models. Proposed is a new approach based on singular value decomposition (SVD), where the representative model is determined through rank-loss assessment of appropriately configured data matrix.

P.P. Kanjilal

Papers

Weighted singular value distribution of RRI series applied to the characterization of heat intolerance in humans

An instrument for noninvasive continuous measurement of blood pressure using a finger cuff

Fetal ECG Extraction from Maternal ECG Using the Singular Value Decomposition

Analysis of quasiperiodic physiological signals using SVR spectrum

In search of the optimal model for a process with multiple inputs