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

Orthogonal transformation, which can lead to compaction of information, has been used in two ways to optimize on the size of feedforward networks: 1) through the selection of optimum set of time-domain inputs, and the optimum set of links and nodes within a neural network (NN); and 2) through the orthogonalization of the data to be used in NN's, in case of processes with periodicity. The proposed methods are efficient and are also extremely robust numerically. The singular value decomposition (SVD) and QR with column pivoting factorization (QRcp) are the transformations used. SVD mainly serves as the null space detector; QRcp coupled with SVD is used for subset selection, which is one of the main operations on which the design of the optimal network is based. SVD has also been used to devise a new approach for the assessment of the convergence of the NN's, which is an alternative to the conventional output error analysis. >

On the application of orthogonal transformation for the design and analysis of feedforward networks

A method for periodicity detection is proposed where unlike available methods a periodic component is characterized in terms of three basic periodicity attributes: the periodicity (or period length), the periodic pattern, and the scaling factors associated with the successive nearly repetitive segments. A scheme is proposed for subsequent successive detection and extraction of such (hidden) periodic or nearly periodic components constituting an irregular cyclical series. To our knowledge, the proposed decomposition is much more powerful in terms of information content and robustness than the presently available tools based on Fourier decomposition. Through the analysis of a variety of natural, experimental, and simulated data series, it is shown that the features of the periodicity attributes of the embedded periodic components can lead to a meaningful characterization of an irregular series in a new perspective.

Robust method for periodicity detection and characterization of irregular cyclical series in terms of embedded periodic components

On the detection of determinism in a time series

The singular value decomposition—applied in the modelling and prediction of quasiperiodic processes

Singular value decomposition (SVD) can lead to compaction of information, and along with QR with column pivoting (QRcp) factorisation can be used for subset selection. The Letter proposes an application of SVD and subset selection for optimising the size of feedforward neural networks; the Mackey Glass (MG) series is used as an example.

P.P. Kanjilal

Papers

On the application of orthogonal transformation for the design and analysis of feedforward networks

Robust method for periodicity detection and characterization of irregular cyclical series in terms of embedded periodic components

On the detection of determinism in a time series

The singular value decomposition—applied in the modelling and prediction of quasiperiodic processes

Reduced-size neural networks through singular value decomposition and subset selection