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)

/pdf/nonlinear-time-series-analysis-1j3a35n4y3.pdf

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

On the singular value decomposition, applied in the analysis and prediction of almost periodic signals

Modelling and prediction of time series using singular value decomposition and neural networks

The problem of modeling complex processes with a large number of inputs is addressed. A new method is proposed for the optimization of the models in minimum C/sub p/ statistic sense using QR with a modified scheme of column pivoting (m-QRcp) factorization. Two different classes of multilayer nonlinear modeling problems are explored: 1) in the first class of models, each layer comprises multiple linearly parameterized submodels or cells; the individual cells are optimally modeled using QR factorization, and m-QRcp factorization ensures optimal selection of variables across the layers. 2) The nonhomogeneous feed-forward neural network is chosen as the second class of models, where the network architecture and structure are optimized in terms of best set of hidden links (and nodes) using m-QPcp factorization. In both the cases, the optimization is shown to be direct and conclusive. The proposed is a generic approach to the optimal modeling of complex multilayered architectures, which leads to computationally fast and numerically robust parsimonious designs, free from collinearity problems. The method is largely free from heuristics and is amenable to automated modeling.

On robust nonlinear modeling of a complex process with large number of inputs using m-QRcp factorization and C/sub p/ statistic

The goal of this study is to explore the chaotic behavior of sea surface temperature (SST) in the Indian Ocean and in the equatorial Pacific Ocean. The SST time series is analyzed for Bay of Bengal, Arabian Sea and South Indian Ocean as well as for two extreme phenomena: El Nino and Indian Ocean Dipole (IOD). The analysis is based on Singular spectrum analysis, and singular value decomposition (SVD). Our analysis reveals that the dynamics of SST is chaotic in varying degrees in all the studied cases, since Lyapunov exponent, an indicator of chaoticity, is positive in each case. To study the degree of predictability of these SST series, we search for embedded periodic component(s) using two different approaches: Orthogonal functions extracted from the Singular spectrum analysis and Periodicity spectrum analysis based on SVD. Both the methods reveal presence of a strong periodic component(s) for the SST signals in the Arabian Sea, Bay of Bengal and South Indian Ocean, whereas no periodicity is found for El Nino and IOD. Therefore, it can be concluded that the dynamics of SST is more complex in the El Nino and IOD region compared to Bay of Bengal, Arabian Sea and South Indian Ocean; hence it is much more difficult to predict El Nino and IOD.

Application of Singular Spectrum Analysis for Investigating Chaos in Sea Surface Temperature

Singular value decomposition (SVD) and analysis based on singular value ratio (SVR) Spectrum have been used for the extraction of fetal ECG (F-ECG) from a single channel composite maternal ECG (CM-ECC) signal. The maternal and the fetal ECG components are identified in terms of the SV-decomposed modes of the the appropriately configured data matrices, and the desired F-ECG is obtained through selective separation of SV-decomposed components.

P.P. Kanjilal

Papers

On the singular value decomposition, applied in the analysis and prediction of almost periodic signals

Modelling and prediction of time series using singular value decomposition and neural networks

On robust nonlinear modeling of a complex process with large number of inputs using m-QRcp factorization and C/sub p/ statistic

Application of Singular Spectrum Analysis for Investigating Chaos in Sea Surface Temperature

Fetal ECG extraction from single channel maternal ECG using SVD and SVR spectrum