Limitations of Rechtschaffen and Kales

Himanen, Sari-Leena; Hasan, Joel

doi:10.1053/smrv.1999.0086

Cited by 149 publications

(93 citation statements)

References 43 publications

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“…This has been observed previously by many authors (e.g., Anderer et al [40]). This could be due to N1 being a transition phase between wakefulness and different sleep stages as discussed in [41]. In fact, the neurophysiologic signals of N1 and N2 stages present similarities between themselves and a mix of patterns with similarities to awake, N3 and REM stages (Table I); e.g., the N1 epochs can present alpha activity (typical of awake stage) and can present theta activity (typical of N2 sleep stage).…”

Section: Discussionmentioning

confidence: 99%

Automatic sleep staging: A computer assisted approach for optimal combination of features and polysomnographic channels

Khalighi

Sousa

Pires

et al. 2013

Expert Systems with Applications

119

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To improve applicability of automatic sleep staging an efficient subjectindependent method is proposed with application in sleep-wake detection and in multiclass sleep staging (awake, non-rapid eye movement (NREM) sleep and rapid eye movement (REM) sleep). In turn, NREM is further divided into three stages denoted here by N1, N2, and N3. To assess the method, polysomnographic (PSG) records of 40 patients from our ISRUC-Sleep dataset, which was scored by an expert clinician in the central hospital of Coimbra, are used. To find the best combination of PSG signals for automatic sleep staging, six electroencephalographic (EEG), two electrooculographic (EOG), and one electromyographic (EMG) channels are analyzed. An extensive set of feature extraction techniques are applied, covering temporal, frequency and timefrequency domains. The maximum overlap wavelet transform (MODWT), a shift invariant transform, was used to extract the features in time-frequency domain. The extracted feature set is transformed and normalized to reduce the effect of extreme values of features. The most discriminative features are selected through a two-step method composed by a manual selection step based on features' histogram analysis followed by an automatic feature selector. The selected feature set is classified using support vector machines (SVMs). The system achieved the best performance by combining 6 channels (C3, C4, O1, left EOG (LOC), right EOG (ROC) and chin EMG (X1)) for sleep-wake detection, and 9 channels (C3, C4, O1, O2, F3, F4, LOC, ROC, X1) for multiclass sleep staging.

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Section: Discussionmentioning

confidence: 99%

Automatic sleep staging: A computer assisted approach for optimal combination of features and polysomnographic channels

Khalighi

Sousa

Pires

et al. 2013

Expert Systems with Applications

119

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“…2 This standard recommended a single central derivation, despite its apparent shortcomings. 3 In 2007 the American Academy of Sleep Medicine (AASM) published The AASM Manual for the Scoring of Sleep and Associated Events. 4 This was seen as a long-overdue update of the criteria, incorporating the evidence accumulated since the publication of R&K and recognizing the need to improve inter-scorer agreement.…”

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confidence: 99%

The AASM Recommended and Acceptable EEG Montages are Comparable for the Staging of Sleep and Scoring of EEG Arousals

Duce

Rego

Milosavljevic

et al. 2014

Journal of Clinical Sleep Medicine

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Study Objective: To examine the measurement differences in sleep and EEG arousal statistics between the American Academy of Sleep Medicine (AASM) recommended EEG montage (F4-M1, C4-M1, O2-M1) and acceptable EEG montage (Fz-Cz, C4-M1, Oz-Cz). Design: Prospective, blinded, randomized comparison. Setting: Australian clinical sleep laboratory in a tertiary hospital. Patients or Participants: 50 consecutive patients undertaking polysomnography (PSG) for the clinical suspicion of sleep disordered breathing. Interventions: N/A Measurements and Results: Patient EEGs were recorded using both the AASM recommended and acceptable EEG montages during the PSG. Two scorers were used to examine the difference in PSG statistics using the two EEG montages. The scorers analyzed the 50 studies using the two EEG montages. Ten of the studies were scored twice for each montage by each scorer to calculate intra-scorer and interscorer agreement. No statistically signifi cant differences were observed between the PSG statistics of the recommended and acceptable EEG montages. The recommended EEG montage had greater inter-scorer agreement but no difference in intrascorer agreement. Conclusions: This study demonstrates that the two EEG montages endorsed by the AASM Manual produce similar sleep and EEG arousal statistics. Keywords: EEG, recommended montage, acceptable montage, sleep staging, EEG arousal scoring, polysomnography, sleep apnea, inter-scorer agreement, intra-scorer agreement, kappa Citation: Duce B, Rego C, Milosavljevic J, Hukins C. The AASM recommended and acceptable EEG montages are comparable for the staging of sleep and scoring of EEG arousals. J Clin Sleep Med 2014;10(7):803-809.http://dx.doi.org/10.5664/jcsm.3880 S C I E N T I F I C I N V E S T I G A T I O N SA standard of clinical polysomnography (PSG) is the characterization of sleep and sleep fragmentation via the staging of sleep and scoring of EEG arousals.1 For a number of decades the staging of sleep was conducted according to the Rechtschaffen and Kales (R&K) criteria.2 This standard recommended a single central derivation, despite its apparent shortcomings. 3 In 2007 the American Academy of Sleep Medicine (AASM) published The AASM Manual for the Scoring of Sleep and Associated Events. 4 This was seen as a long-overdue update of the criteria, incorporating the evidence accumulated since the publication of R&K and recognizing the need to improve inter-scorer agreement. 5 Among the changes in the AASM manual was the increase to three derivations in the EEG montage as well as the provision of two different EEG montages. The introduction of two extra derivations into the EEG montage was based on evidence that regional differences in EEG morphology exist and may be important in describing sleep. [6][7][8][9] The two EEG montages allowed differ in the type of derivations that make up each montage. The recommended montage (M rec ) consists of all referential derivations, with negative input electrodes over the frontal, central, and occipital regions. The acceptable mo...

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“…Nowadays, R&K scoring proves its usefulness every day, but its limits are more and more admitted [13].…”

Section: Discussionmentioning

confidence: 99%

Comparison Between Five Classifiers for Automatic Scoring of Human Sleep Recordings

Becq¹,

Charbonnier²,

Chapotot³

et al. 2005

Classification and Clustering for Knowledge Discovery

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Abstract. The aim of this work is to compare the performances of 5 classifiers (linear and quadratic classifiers, k nearest neighbors, Parzen kernels and neural network) to score a set of 8 biological features extracted from EEG and EMG, in six classes corresponding to different sleep stages as to automatically elaborate an hypnogram and help the physician diagnosticate sleep disorders. The data base is composed of 17265 epochs of 20 s recorded from 4 patients. Each epoch has been classified by an expert into one of the six sleep stages. In order to evaluate the classifiers, learning and testing sets of fixed size are randomly drawn and are used to train and test the classifiers. After several trials, an estimation of the misclassification percentage and its variability is obtained (optimistically and pessimistically). Data transformations toward normal distribution are explored as an approach to deal with extreme values. It is shown that these transformations improve significantly the results of the classifiers based on data proximity.

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Limitations of Rechtschaffen and Kales

Cited by 149 publications

References 43 publications

Automatic sleep staging: A computer assisted approach for optimal combination of features and polysomnographic channels

Automatic sleep staging: A computer assisted approach for optimal combination of features and polysomnographic channels

The AASM Recommended and Acceptable EEG Montages are Comparable for the Staging of Sleep and Scoring of EEG Arousals

Comparison Between Five Classifiers for Automatic Scoring of Human Sleep Recordings

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