HYPNOS ZERO MAX VOLT MANUAL
Similarly, manual selection of representative templates by two distinct experimenters might lead to two distinct interpretation results. While the use of threshold allows a high flexibility, for instance, to adapt the detection if the signal quality changes over time, 16, 19 it also has the disadvantage of introducing subjectivity and bias. 18 Most often these methods require human intervention either to set thresholds for one or more parameters, or to select representative templates of each vigilance state. 15– 17 Because of inter-individual variability, the decision rules frequently include thresholds which need to be adjusted for each animal.
HYPNOS ZERO MAX VOLT SERIES
This process is often based on sequential logic rules with a series of dual choices leading to individual classes (decision tree). 1, 12– 14Īutomated sleep staging methods also include a decision process in charge of assigning a score as close as possible to the one a human expert would assign. 11 The lack of consensus on the characteristic elements of sleep stages to consider and the lack of standard rules to build a hypnogram accounts for interrater variability and leads to difficulties in the quantification and rigorous comparison of sleep studies among laboratories.
While most studies consider three major waking and sleep states, up to seven stages can be identified, 10 not to mention studies distinguishing states and the transitions between them. 1 Since pioneering reports, 6 individual laboratories have often refined their categories and corresponding criteria on their own. Such recognition of sleep and wake states supposes a consensual definition of the vigilance states that need to be identified.Įven with the well-known electrophysiological invariants, sleep scoring in rodents remains subject to a high variability and suffers from poor standardization: While a gold-standard classification method exists for human sleep recordings, 9 there are no standardized guidelines for rodent sleep. However, in contrast to human scorers, automated methods can objectively and infallibly apply the same rigorous criteria over multiple recordings. This pattern recognition process often reproduces what an experimenter would do when visually inspecting recording charts. After extracting one or more characteristic features such as delta (1–4 Hz) or theta (5–9 Hz) power, 6– 8 a logical paradigm is then used to compare incoming epochs to predefined templates of each state to identify the sleep state.
2– 5 They thus share common features in their design, such as the extraction of indices or descriptors from the raw signals, and a decision process to assign a state to an epoch. Whether they use a single EEG channel or combine EEG with electromyography (EMG) and electro-oculograms (EOG), all automated methods presume that information about the behavioral state is contained within the signal(s).
Still being performed manually in many laboratories, sleep scoring is a tedious task that has motivated the development of a variety of automated methods, and the literature continuously describes new ones which can satisfy most experts in the field. The interpretation of an animal's vigilance states from polygraphic recordings takes advantage of a series of invariants contained in the EEG and other signals that are readily observable visually, either on a succession of epochs of equal length or on freely delimited intervals. Attempting to define or characterize brain states is nothing but imposing segmentation to ever-changing electrical brain signals by dividing recordings into categorical homogenous bouts, with the risk of neglecting their graded dynamics.
As assessed by electroencephalogram (EEG) recordings, brain activity is continuously changing. Sleep staging, automatic scoring, unsupervised algorithm, paradoxical sleep deprivation INTRODUCTIONīehavioral states are the expression of the large-scale dynamics of the central nervous system.
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