Document Type

Conference Proceeding

Publication Date


Publication Title

The 21st IASTED International Conference on Modelling and Simulation (MS 2010)


Medical embedded systems are capable of recording vast data sets for physiological and medical research. Linear modeling techniques are proposed as a means to explore relationships between two or more medical or physiological signal measurements where a causal relationship is believed to be present. Multiple regression is explored for use in medical monitoring, telehealth, and clinical applications.

Spectral regression methods for high-bandwidth medical and physiological signals are demonstrated. The twostage method consists of performing an FFT over a timelagged window of the predictor signal, and constructing a model based on the FFT coefficients. The output of the regression is used in a clustering to explore structure in the array of spectral predictors. It has been applied to medical and physiological time series data, specifically the link between respiration and blood oxygen saturation percentage in sleep apnea patients.

Spectral predictors achieved a dramatically better goodness of fit than time-lagged predictors according to standard analysis of variance measures. In the dataset examined, the spectral model achieved a multiple R2 of 0.90, indicating that 90% of the variation in the dependent signal was captured by the model, while an ordinary distributed lag model had a R2 of only 0.016.


Biomedical Modelling, Cardiovascular Modelling, Time Series Analysis, Respiratory Mechanics


Archived as published.

Presented at The 21st IASTED International Conference on Modelling and Simulation (MS 2010), Banff, Alberta, Canada, July, 2010



To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.