ABSTRACT
A method for the automatic detection of episodes of wakefulness during sleep is presented. The algorithm is based on the evaluation of the alpha slow-wave index (ASI), a measure that has been developed to detect fluctuations of vigilance in daytime pharmaco-electroencephalogram studies. Its application to sleep data was validated with polysomnographic recordings from 16 elderly insomniacs and 16 young healthy subjects. The rate of agreement between the computerized procedure and the visual scoring of wakefulness was 94.0% for the insomniacs and 96.9% for the healthy subjects. The decision criterion used by the computer allowed the definition of a subject-adapted threshold for the detection of wake episodes. The method opens new perspectives for the automatic analysis of continuous 24-hour sleep-wake recordings.
Subject(s)
Alpha Rhythm , Sleep/physiology , Wakefulness/physiology , Aged , Female , Humans , Male , Middle Aged , Sleep Initiation and Maintenance Disorders/physiopathologyABSTRACT
A computer program for the analysis of a sleep electroencephalogram (EEG) is presented. The method relies on two steps. First, a spectral analysis is performed for signals recorded from one or more electrode locations. Then, two EEG parameters are obtained by storing the spectral activity in a multidimensional space, whose dimension is reduced using principal component analysis (PCA) techniques. The main advantage of these parameters is in describing the process of sleep on a continuous scale as a function of time. Validation of the method was performed with the data collected from 16 subjects (8 young volunteers and 8 elderly insomniacs). Results showed that the parameters correlate highly with the hypnograms established by conventional visual scoring. This signal parametrisation, however, offers more information regarding the time course of sleep, since small variations within individual sleep stages as well as smooth transitions between stages are assessed. Finally, the concurrent use of both parameters provides an original way of considering sleep as a dynamic process evolving cyclically in a single plane.
Subject(s)
Electroencephalography/methods , Signal Processing, Computer-Assisted , Sleep/physiology , Software , Adult , Aged , Humans , Sleep Initiation and Maintenance Disorders/physiopathologyABSTRACT
Sleep onset latency (SOL) is frequently defined as the time between lights-out and the first epoch of sleep stage 2. In practice, SOL can be quantified easily on the basis of visual examination. We have developed a computer algorithm allowing an automatic estimation of this parameter. The agreement between both strategies, visual and computer analysis, was tested using data from a pharmacological sleep study with 16 elderly insomniacs, which was aimed at comparing the effects of lormetazepam and zopiclone on polysomnography. A high correlation was found between the visual and the computer-based determination of SOL. Drug-related differences in SOL could be shown with both approaches.
