Signal Processing Algorithms for Analysis of Categorical and Numerical Time Series: Application to Sleep Study Data
Matthew Robert Kirsch · OhioLink ETD Center (Ohio Library and Information Network) · 2010
In this thesis, novel entropy-based measures are developed to quantify the fragmentation of an individual categorical time series and the coupling strength of two categorical time series.Existing entropy-based measures are also shown to be well suited for the same task.These measures are applied to the analysis of categorical time series derived from sleep study data.Specifically, fragmentation of the hypnogram categorical time series of sleep stages, fragmentation of the breathing categorical time series of oxygen desaturation events, and coupling between the hypnogram and breathing categorical time series is quantified and the relationship between these measures and the diagnostic outcomes of hypertension and obstructive sleep apnea is investigated.Additionally, electroencephalogram (EEG) activity during sleep is explored by analyzing the distribution of power in various frequency bands throughout the night using summary statistics and a histogram entropy measure computed using the maximum-entropy histogram binning procedure developed in this thesis.* indicates p-value > 0.05