Time series analysis and sleep research

Abstract

The characterization of biological data during sleepwaking state changes requires the utilization of time series and point process techniques of analysis. The time series procedures that have been found useful in describing biological activity during sleep include frequency domain techniques, such as the autospectrum, digital filtering, complex demodulation, coherence, and cross-spectrum calculations, as well as time domain procedures, such as autocorrelation and matched filtering. The point process techniques include interval distributions, intensity functions, serial correlations, and cross-intensity functions. In addition, applications of frequency domain techniques such as the interval spectrum and spectrum of counts have been implemented for point process data.