Current practice of clinical electroencephalography

Background Sleep is important to brain organization, but few strategies to promote sleep among premature infants have been tested. Behaviorally based measures of sleep have shown increased quiet sleep (QS) and decreased active sleep (AS) during skin-to-skin contact (SSC) with the mother, but these results have not been confirmed with objective electroencephalographic/polysomnographic measures of sleep organization. Important differences exist between behavioral and electroencephalographic/polysomnographic definitions of sleep state. Methods Data for the first 28 relatively healthy, preterm subjects of an ongoing randomized trial of one 2- to 3-hour session of SSC or incubator care between feedings are reported here. Infants were positioned prone, inclined, and nested in an incubator during the 2- to 3-hour pretest period, were fed, and then went into the test period of SSC or incubator care. Infants were left largely undisturbed throughout testing. A mixed-model regression analysis compared the test-pretest differences in outcome measures within and between groups. Results Results showed that arousals were significantly lower in the SSC group, compared with the control group, for the entire study period and for test-pretest matched segments of QS and AS. Rapid eye movement was significantly lower for the SSC group for the study period and AS segments. Indeterminate sleep was significantly lower for the SSC group when confounding environmental variables were included in the regression analysis. When 4 subjects who experienced excessive ambient light levels during SSC were removed from analysis, QS increased during SSC. Conclusions The patterns demonstrated by the SSC group are analogous to more-mature sleep organization. SSC may be used as an intervention to improve sleep organization in this population of preterm infants.

/pdf/neurophysiologic-assessment-of-neonatal-sleep-organization-1fmgymz3bk.pdf

Neurophysiologic Assessment of Neonatal Sleep Organization: Preliminary Results of a Randomized, Controlled Trial of Skin Contact With Preterm Infants

In March 2014, the American Academy of Sleep Medicine (AASM) Board of Directors requested the Scoring Manual Editorial Board develop rules, terminology, and technical specifications for scoring sle...

The Visual Scoring of Sleep in Infants 0 to 2 Months of Age.

OBJECTIVE This study classifies sleep stages from a single lead electrocardiogram (ECG) using beat detection, cardiorespiratory coupling in the time-frequency domain and a deep convolutional neural network (CNN). APPROACH An ECG-derived respiration (EDR) signal and synchronous beat-to-beat heart rate variability (HRV) time series were derived from the ECG using previously described robust algorithms. A measure of cardiorespiratory coupling (CRC) was extracted by calculating the coherence and cross-spectrogram of the EDR and HRV signal in 5 min windows. A CNN was then trained to classify the sleep stages (wake, rapid-eye-movement (REM) sleep, non-REM (NREM) light sleep and NREM deep sleep) from the corresponding CRC spectrograms. A support vector machine was then used to combine the output of CNN with the other features derived from the ECG, including phase-rectified signal averaging (PRSA), sample entropy, as well as standard spectral and temporal HRV measures. The MIT-BIH Polysomnographic Database (SLPDB), the PhysioNet/Computing in Cardiology Challenge 2018 database (CinC2018) and the Sleep Heart Health Study (SHHS) database, all expert-annotated for sleep stages, were used to train and validate the algorithm. MAIN RESULTS Ten-fold cross validation results showed that the proposed algorithm achieved an accuracy (Acc) of 75.4% and a Cohen's kappa coefficient of [Formula: see text]  =  0.54 on the out of sample validation data in the classification of Wake, REM, NREM light and deep sleep in SLPDB. This rose to Acc  =  81.6% and [Formula: see text]  =  0.63 for the classification of Wake, REM sleep and NREM sleep and Acc  =  85.1% and [Formula: see text]  =  0.68 for the classification of NREM sleep versus REM/wakefulness in SLPDB. SIGNIFICANCE The proposed ECG-based sleep stage classification approach that represents the highest reported results on non-electroencephalographic data and uses datasets over ten times larger than those in previous studies. By using a state-of-the-art QRS detector and deep learning model, the system does not require human annotation and can therefore be scaled for mass analysis.

https://iopscience.iop.org/article/10.1088/1361-6579/aaf339/pdf

Deep learning in the cross-time frequency domain for sleep staging from a single-lead electrocardiogram

Actigraphy offers one of the best-known alternatives to polysomnography for sleep-wake identification. The advantages of actigraphy include high accuracy, simplicity of use and low intrusiveness. These features allow the use of actigraphy for determining sleep-wake states in such highly sensitive groups as infants. This study utilizes a motion sensor (accelerometer) for a dual purpose: to determine an infant's position in the crib and to identify sleep-wake states. The accelerometer was positioned over the sacral region on the infant's diaper, unlike commonly used attachment to an ankle. Opposed to broadly used discriminant analysis, this study utilized logistic regression and neural networks as predictors. The accuracy of predicted sleep-wake states was established in comparison to the sleep-wake states recorded by technicians in a polysomnograph study. Both statistical and neural predictors of this study provide an accuracy of approximately 77-92% which is comparable to similar studies achieving prediction rates of 85-95%, thus validating the suggested methodology. The results support the use of body motion as a simple and reliable method for determining sleep-wake states in infants. Nonlinear mapping capabilities of the neural network benefit the accuracy of sleep-wake state identification. Utilization of the accelerometer for the dual purpose allows us to minimize intrusiveness of home infant monitors.

http://iopscience.iop.org/article/10.1088/0967-3334/25/5/018/pdf

Activity-based sleep–wake identification in infants

Infant arousal scoring based on the Atlas Task Force definition of transient EEG arousal was evaluated to determine (1). whether transient arousals can be identified and assessed reliably in infants and (2). whether arousal and no-arousal epochs scored previously by trained raters can be validated reliably by independent sleep experts. Phase I for inter- and intrarater reliability scoring was based on two datasets of sleep epochs selected randomly from nocturnal polysomnograms of healthy full-term, preterm, idiopathic apparent life-threatening event cases, and siblings of Sudden Infant Death Syndrome infants of 35 to 64 weeks postconceptional age. After training, test set 1 reliability was assessed and discrepancies identified. After retraining, test set 2 was scored by the same raters to determine interrater reliability. Later, three raters from the trained group rescored test set 2 to assess inter- and intrarater reliabilities. Interrater and intrarater reliability kappa's, with 95% confidence intervals, ranged from substantial to almost perfect levels of agreement. Interrater reliabilities for spontaneous arousals were initially moderate and then substantial. During the validation phase, 315 previously scored epochs were presented to four sleep experts to rate as containing arousal or no-arousal events. Interrater expert agreements were diverse and considered as noninterpretable. Concordance in sleep experts' agreements, based on identification of the previously sampled arousal and no-arousal epochs, was used as a secondary evaluative technique. Results showed agreement by two or more experts on 86% of the Collaborative Home Infant Monitoring Evaluation Study arousal scored events. Conversely, only 1% of the Collaborative Home Infant Monitoring Evaluation Study-scored no-arousal epochs were rated as an arousal. In summary, this study presents an empirically tested model with procedures and criteria for attaining improved reliability in transient EEG arousal assessments in infants using the modified Atlas Task Force standards. With training based on specific criteria, substantial inter- and intrarater agreement in identifying infant arousals was demonstrated. Corroborative validation results were too disparate for meaningful interpretation. Alternate evaluation based on concordance agreements supports reliance on infant EEG criteria for assessment. Results mandate additional confirmatory validation studies with specific training on infant EEG arousal assessment criteria.

Infant polysomnography: reliability and validity of infant arousal assessment.

Study Objectives: Epoch lengths from 20 seconds to 1 minute, and smoothing strategies from zero to three minutes are encountered in the infant sleep and waking literature. The present study systematically examined the impact of various epoch lengths and smoothing strategies on infant sleep state architecture. Design: Overnight polysomnographic recordings were visually assessed by epoch as wake or as each of four sleep state parameters: electroencephalographic patterns, respiration, body movement, and eye movement. From these findings, sleep and waking states were assigned for each of six combinations of epoch length (30-second or 1-minute) and smoothing window length (none, 3-epoch, or 5-epoch). Setting: N/A Participants: Subjects were 91 term infants, 42-46 weeks postconceptional age, from the Collaborative Home Infant Monitoring Evaluation (CHIME) study. Interventions: N/A Measurements and Results: A greater epoch length resulted in more active and less quiet sleep as a percentage of total study; however, the size of the smoothing window did not affect the percentage of sleep/waking states. In general, the greater the epoch length and the greater the smoothing window length, the fewer the number of, the greater the mean duration of, and the greater the longest continuous episode of sleep/waking states. Analysis of significant interactions indicated that a 1-minute epoch length relative to a 30-second epoch length resulted in increasingly longer episodes of quiet and especially active sleep with a greater smoothing window length. Conclusions: Smoothing strategy significantly altered sleep state architecture in infants and may explain part of the variability in infant sleep state findings between laboratories.

/pdf/the-effect-of-epoch-length-and-smoothing-on-infant-sleep-and-1sc6mm3wy8.pdf

The effect of epoch length and smoothing on infant sleep and waking state architecture for term infants at 42 to 46 weeks postconceptional age.

/pdf/infant-polysomnography-arousal-identification-and-scoring-1lmlw96p4c.pdf

Infant Polysomnography: Arousal Identification and Scoring Reliability

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Infant polysomnography: reliability and validity of infant arousal assessment.

The effect of epoch length and smoothing on infant sleep and waking state architecture for term infants at 42 to 46 weeks postconceptional age.

Infant Polysomnography: Arousal Identification and Scoring Reliability