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

The classical posterior alpha rhythm has been decomposed into regular and irregular components using orthogonal transformation. A periodicity generator is considered, which has three characteristic control parameters: the periodicity, the amplitudes or scaling factors and the pattern associated with successive periodic segments. The regular component is shown to be equivalent to an oscillator or periodicity generator whose parameters are dynamically varying and, thus, producing both amplitude- and frequency-modulation. The irregular component is devoid of such modulating behavior. Electroencephalogram signals from normal, maniac and epileptic subjects are studied. Through analytic signal-based analysis, it is shown that for the regular component, healthy brain possesses universal scaling behavior, whereas heterogeneous scaling or absence of universality is observed for the diseased brain.

Decomposition of posterior alpha rhythm

Implementation aspects of the LQ self-tuner proposed in Part I are discussed. The method involves solution of a Riccati equation through U-D factorization, and state estimation through transmittance matrices. Simplified vector formulations of the U-D time update and the transmittance matrix are presented. Simulation results demonstrate the superiority of this algorithm over pole-placement and the generalized minimum-variance controllers. Incorporation of the auxiliary variable ψ(t) instead of y(t) and the incremental control law Δu(t) in the cost function ascribes the right servo properties to this algorithm with offset-free control performance. The algorithmic stability and numerical robustness, without sacrifice of implement-ational simplicity, make the proposed LQ self-tuner attractive.

A generalized LQG approach to self-tuning control Part II. Implementation and simulation

An usual approach for selection of the best subset AR model of known maximal order is to use an appropriate information criterion, like AIC or SIC with an exhaustive selection of regressors and to choose the subset model that produces the optimum (minimum) value of AIC or SIC. This method is computationally intensive. Proposed is a method based on the use of singular value decomposition and QR with column pivoting factorization for extracting a reduced subset from the exhaustive candidate set of regressors and to use AIC or SIC on the reduced subset to obtain the best subset AR model. The result is substantially reduced domain of exhaustive search for the computation of the best subset AR model.

On a method of identification of best subset model from full ar-model

This paper presents a novel approach to the extraction of weaker periodic signals in the presence of noise and a stronger periodic waveform. The signals may have any shape, not necessarily sinusoidal in nature. The approach is based on the Singular Value Decomposition. The extraction procedure consists of two steps, identifying the strongest periodic component and consequent configuring the data matrix followed by extraction of this component. This process is repeated till all the components have been recovered. The approach is both simple and robust and requires no additional reference inputs. The simplicity of the scheme is however, at the expense of certain inherent limitations, which are also investigated. >

Extraction of multiple periodic waveforms from noisy data

A study was performed to analyze the signal obtained from a noninvasive photo-plethysmographic device from four subjects in different clinical conditions. With the help of the theory of nonlinear dynamics, it is verified that the cardiovascular dynamics is dominated by an underlying chaotic attractor. A new robust and computationally efficient method is presented for the detection of the hidden deterministic structure of a time series. It is shown that the degree of chaos as well as the underlying determinism is directly related to the subject's clinical stability.

P.P. Kanjilal

Papers

Decomposition of posterior alpha rhythm

A generalized LQG approach to self-tuning control Part II. Implementation and simulation

On a method of identification of best subset model from full ar-model

Extraction of multiple periodic waveforms from noisy data

Assessing determinism of photo-plethysmographic signal