Human sleep depth was traditionally assessed by scoring electro-encephalographic slow-wave amplitudes at the globally standardized C4-M1 electrode derivation. Since 2007, the American Association of Sleep Medicine (AASM) has accepted three additional derivations for the same purpose. These might well differ in slow wave amplitudes which would bias the scorings.
View Article and Find Full Text PDFThe International Pharmaco-EEG Society (IPEG) presents guidelines summarising the requirements for the recording and computerised evaluation of pharmaco-sleep data in man. Over the past years, technical and data-processing methods have advanced steadily, thus enhancing data quality and expanding the palette of sleep assessment tools that can be used to investigate the activity of drugs on the central nervous system (CNS), determine the time course of effects and pharmacodynamic properties of novel therapeutics, hence enabling the study of the pharmacokinetic/pharmacodynamic relationship, and evaluate the CNS penetration or toxicity of compounds. However, despite the presence of robust guidelines on the scoring of polysomnography -recordings, a review of the literature reveals inconsistent -aspects in the operating procedures from one study to another.
View Article and Find Full Text PDFRapid eye movement (REM) and non-REM sleep processes affect the electrical signals from brain, eyes and muscles. Recording these signals during sleep imposes special demands on electrodes, technicians and equipment. Both human and computerized signal analysis can then be applied to quantify the sleep processes.
View Article and Find Full Text PDFObjective: Traditional electroencephalogram (EEG) recorders reject low frequencies and DC and therefore cannot handle fullband EEG. Dedicated fullband recorders use non-standard file formats, because the standard format (EDF) cannot handle large DC electrode offset voltages. Both facts limit the development and use of fullband EEG.
View Article and Find Full Text PDFTo date, the only standard for the classification of sleep-EEG recordings that has found worldwide acceptance are the rules published in 1968 by Rechtschaffen and Kales. Even though several attempts have been made to automate the classification process, so far no method has been published that has proven its validity in a study including a sufficiently large number of controls and patients of all adult age ranges. The present paper describes the development and optimization of an automatic classification system that is based on one central EEG channel, two EOG channels and one chin EMG channel.
View Article and Find Full Text PDFBackground And Purpose: To specify a simple conversion of neurophysiological signals contained in sleep recordings into standard audio files and to illustrate how our cerebral audio processor can then detect specific signal characteristics.
Methods: A software package (freely accessible from the Internet) has been developed that converts signals from standard EDF (or EDF+) format to standard audio (WAV) format, a process usually called audification. The software has been applied to sleep EEG, EOG and ECG.
Both SignalML and EDF+ offer a solution for the incompatibility between different data storage formats in biomedicine. This article discusses the SignalML approach from an EDF+ point of view.
View Article and Find Full Text PDFThe prevalence of restless legs syndrome (RLS) in various regions in the world has been estimated between 2.5 and 29%. For The Netherlands these figures are not known.
View Article and Find Full Text PDFClin Neurophysiol
September 2003
The European data format (EDF) is a widely accepted standard for exchange of electroencephalogram and polysomnogram data between different equipment and labs. But it hardly accommodates other investigations. EDF+ is a more flexible but still simple format which is compatible to EDF except that an EDF+ file may contain interrupted recordings.
View Article and Find Full Text PDFAlthough various investigators have suggested algorithms for the automatic detection of eye movements during sleep, objective comparisons of the proposed methods have previously been difficult due to different recording arrangements of different investigators. In this study the results of five eye movement detection algorithms applied to the same data were compared to visually scored data. The percentages of true and false detections are given for various threshold levels in rapid and slow eye movement detections.
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