Yoichi Tsuji

Automatic detection of rapid eye movements by discrete wavelet transform

In order to detect rapid eye movements (REM) automatically, the Discrete Wavelet Transform was applied to each 8‐s segment of electrooculogram (EOG) data for 30 min of 8 h of normal sleep. The Haar function was used as an analysing wavelet because this function is similar to the REM waveform. By shifting the phase of the analysing wavelet by π/4 of the function, 96% of REM could be detected. The artifacts caused by body movements could be detected simultaneously by this method. Computing time required for the detection of REM was only 11 s for 30 min EOG data.

Cross‐correlation between 1/f‐like fluctuations in heartbeat and diurnal changes in the sleep‐waking stage

Abstract The cross‐correlation function between diurnal changes in /f‐like fluctuations of the R‐R interval and those of the sleep‐waking stage was calculated based on the data from six normal subjects in order to investigate the interactions among circadian components. Sleep stage was scored according to the standard criteria and waking stage was scored with criteria developed for the present study. Polysomnography was performed for 11 h during the daytime. The equation for 1/f was approximated using the power spectra of the R‐R interval every 10 min. Cross‐correlation analysis between sleep‐waking stage and 1/f fluctuation of the R‐R interval suggested that the neural network for 1/f fluctuation in heartbeat is influenced by the Sleep‐wake cycle with a time lag in the order of tens of minutes.