Inter-rater reliability of sleep cyclic alternating pattern (CAP) scoring and validation of a new computer-assisted CAP scoring method

Cyclic Alternating Pattern Dynamics in Individuals at Risk for Developing Parkinson's Disease

OBJECTIVE

The objective of this study was to investigate the differences in cyclic alternating patterns (CAP) metrics, a non-rapid eye movement (NREM) sleep physiological rhythm, among recently diagnosed patients with Parkinson's disease (PD), and individuals at high and low risk for developing PD based on genetic and prodromal risk.

METHODS

In this cross-sectional exploratory study, participants underwent clinical, cognitive, and motor evaluation to compute risk based on the Movement Disorder Society (MDS) prodromal criteria and a standard overnight polysomnography. CAP rate, CAP index, A index subtypes, number of CAP sequences, and CAP sequence duration were computed from the electroencephalogram (EEG) signal.

RESULTS

The study included 30 patients with early PD (mean age = 62.80 ± 7.69, disease duration = 1.10 ± 1.09), 26 participants at risk for PD (age = 64.88 ± 10.09), and 36 participants with low risk for PD (age = 56.83 ± 7.41). Despite comparable macrosleep architecture, most CAP measures were significantly lower in patients with PD compared with the low-risk group, whereas the at-risk group showed transitional values between PD and the low-risk group. The A2 index was significantly lower in both the at-risk and PD groups from the low-risk group (at risk = 7.59 ± 4.59; PD = 7.71 ± 5.83; and low risk = 12.85 ± 8.63; p = 0.010). Lower CAP rate and lower CAP index were associated with greater disease severity (r = -0.23 and - 0.24, respectively).

INTERPRETATION

Patients with early clinical PD exhibit alterations in CAP dynamics despite having comparable macrosleep architecture. Alterations of the NREM microsleep structure may occur early in the neurodegenerative process and the A2 index may be an early event in the evolution of the disease with the potential to serve as an early marker for disease progression. ANN NEUROL 2025;98:136-146.

Subject-level Normalization to Improve A-phase Detection of Cyclic Alternating Pattern in Sleep EEG

Automatic detection systems for activation phases (A-phase) of the cyclic alternating pattern (CAP) in electroencephalograms (EEG) are designed to automatically score A-phases in any individual but typically fail to factor in EEG signal variations between individuals, e.g. due to sleep disorders, recording site differences or equipment differences. Here, we investigate the effect of subject-level normalization on the performance of an automatic A-phase detection system consisting of a recurrent neural network. We compared the classification performance of various subject-level normalization methods to the standard training set normalization. Systems were trained and tested on subjects with different sleep disorders using the publicly available CAP Sleep Database on Physionet. Subject-level normalization using Zscore or median and interquartile range (IQR) increases the F1-score for A1-phases by +11-22% (Z-Score: +11-20%, Median/IQR: +16-22%), for A2-phases by +2-9% (Z-Score: +59%, Median/IQR: +2-7%), for A3-phases by -1 - +8% (Z-Score: +3-8%, Median/IQR: -1-+5%) as compared to the standard training data normalization when tested across sleep disorders. Our results show that subject-level normalization drastically improves the precision of A-phase detection in case the training population differs from the testing population.Clinical Relevance- Subject-level normalisation improves the automatic CAP scoring system performances for the general population by minimizing the effect of individual EEG differences.

Association between psychotropic medication and sleep microstructure: evidence from large population studies

STUDY OBJECTIVES

To assess the association between psychotropic medications and sleep microstructure in large community-based cohorts of older people.

METHODS

We analyzed overnight polysomnograms of 381 women from the Study of Osteoporotic Fractures (SOF) and 2,657 men from the Osteoporotic Fractures in Men Sleep Study (MrOS), who either used no psychotropic medication (n = 2,819), only benzodiazepines (n = 112), or only selective serotonin reuptake inhibitors (SSRI) (n = 107). Sleep microstructure (cyclic alternating pattern, CAP) was compared between the no medication group and psychotropic medication groups using the Mann-Whitney U test. Significant differences were investigated using multivariable linear regression adjusted for confounders.

RESULTS

CAP rate, arousal index, apnea-hypopnea index, and the frequency of slow, low-amplitude electroencephalography activation phases were significantly lower in MrOS participants using benzodiazepines than participants not taking psychotropic medication. SSRI users in MrOS experienced no altered sleep microstructure compared to those with no psychotropic use. SOF participants using benzodiazepines did not show similar associations with sleep microstructure. However, SSRI users from SOF had a significantly higher frequency of rapid, high-amplitude electroencephalography activation phases (A2 + 3) and periodic limb-movement index than participants not taking psychotropic medication. Multivariable linear regression adjusted for demographic, lifestyle, mood disorders, and health variables indicated additional significant associations between benzodiazepine usage and CAP rate and A2 + 3 index, respectively, in older men, and between CAP rate and SSRI usage in older women.

CONCLUSIONS

We identified significant associations between sleep microstructure and psychotropic drugs in MrOS and SOF, highlighting the importance of comprehensive sleep analysis, including CAP. Our results may improve understanding of the differences in sleep-wake mechanisms based on psychotropic usage.

CLINICAL TRIAL REGISTRATION

Registry: ClinicalTrials.gov; Title: Outcomes of Sleep Disorders in Older Men; Identifier: NCT00070681; URL: https://clinicaltrials.gov/ct2/show/record/NCT00070681.

CITATION

Hartmann S, Parrino L, Ensrud K, et al. Association between psychotropic medication and sleep microstructure: evidence from large population studies. J Clin Sleep Med. 2023;19(3):581-589.

Sleep macrostructure and cyclic alternating pattern in patients who underwent surgery for hippocampal sclerosis: A prospective controlled polysomnographic study

INTRODUCTION

Temporal lobe epilepsy due to hippocampal sclerosis (TLE-HS) is one of the most common drug-resistant epilepsy. Surgery is currently accepted as an effective and safe therapeutic approach compared to antiseizure medications (ASMs). The study aims to evaluate the effect of surgical treatment of TLE-HS on sleep profile and architecture by subjective and objective evaluation of sleep in basal condition after one month and one year.

METHODS

Thirteen patients with TLE-HS were recruited to undergo overnight polysomnography and a subjective evaluation of nocturnal sleep utilizing the Pittsburgh Sleep Quality Index (PSQI) and daytime somnolence through the Epworth Sleepiness Scale (ESS) in basal condition (T0), one month (T1) and one year after surgery (T2), respectively. Thirteen healthy controls (HC) matched for age, sex and BMI were recruited. Scoring and analysis of sleep macrostructure and cyclic alternating pattern (CAP) parameters were performed.

RESULTS

The comparison between patients in basal condition (T0) and HC showed a significant lower sleep efficiency (p = 0.003) and REM percentage (p < 0.001). Regarding CAP, patients at T0 showed higher total CAP rate (p < 0.001), CAP rate in N2 (p < 0.001), higher A3 (%) (p = 0.001), higher mean duration of A1 (p = 0.002), A3 index (p < 0.001), cycle in sequences (p < 0.001), lower B duration (p < 0.001), cycle mean duration (p < 0.001) than HC. Surgery did not induce significant changes in nocturnal macrostructural polysomnographic variables in T1 and T2. Lower CAP rate (T1 vs T0 and T2 vs T0 p < 0.001), CAP rate in N3 (T1 vs T0 and T2 vs T0 p < 0.001), A3 (%) (T1 vs T0 and T2 vs T0 p < 0.001); lower phase A2 index (T1 vs T0 p < 0.001) and A3 index (T1 vs T0 p < 0.001), lower phase A1 index (T2 vs T0 p < 0.001) and cycle in sequences (T2 vs T0 p = 0.002) higher B mean duration (T2 vs T0 p = 0.002). No significant differences were found between T1 and T2 in CAP parameters.

CONCLUSION

We found a significant NREM sleep instability in patients with TLE-HS compared with HC. In addition, anterior temporal lobectomy (ATL) induced a significant improvement in sleep continuity as evaluated by cyclic alternating pattern already one month later and this effect persisted after one year. ALT seems to restore a more resilient sleeping brain.

Automatic Cyclic Alternating Pattern (CAP) analysis: Local and multi-trace approaches

The Cyclic Alternating Pattern (CAP) is composed of cycles of two different electroencephalographic features: an activation A-phase followed by a B-phase representing the background activity. CAP is considered a physiological marker of sleep instability. Despite its informative nature, the clinical applications remain limited as CAP analysis is a time-consuming activity. In order to overcome this limit, several automatic detection methods were recently developed. In this paper, two new dimensions were investigated in the attempt to optimize novel, efficient and automatic detection algorithms: 1) many electroencephalographic leads were compared to identify the best local performance, and 2) the global contribution of the concurrent detection across several derivations to CAP identification. The developed algorithms were tested on 41 polysomnographic recordings from normal (n = 8) and pathological (n = 33) subjects. In comparison with the visual CAP analysis as the gold standard, the performance of each algorithm was evaluated. Locally, the detection on the F4-C4 derivation showed the best performance in comparison with all other leads, providing practical suggestions of electrode montage when a lean and minimally invasive approach is preferable. A further improvement in the detection was achieved by a multi-trace method, the Global Analysis-Common Events, to be applied when several recording derivations are available. Moreover, CAP time and CAP rate obtained with these algorithms positively correlated with the ones identified by the scorer. These preliminary findings support efficient automated ways for the evaluation of the sleep instability, generalizable to both normal and pathological subjects affected by different sleep disorders.

Cyclic alternating pattern in children with obstructive sleep apnea and its relationship with adenotonsillectomy, behavior, cognition, and quality of life

STUDY OBJECTIVES

To determine in children with obstructive sleep apnea (OSA) the effect of adenotonsillectomy (AT) on the cyclic alternating pattern (CAP) and the relationship between CAP and behavioral, cognitive, and quality-of-life measures.

METHODS

CAP parameters were analyzed in 365 overnight polysomnographic recordings of children with mild-to-moderate OSA enrolled in the Childhood Adenotonsillectomy Trial (CHAT), randomized to either early AT (eAT) or watchful waiting with supportive care (WWSC). We also analyzed CAP in a subgroup of 72 children with moderate OSA (apnea-hypopnea index > 10) that were part of the CHAT sample. Causal mediation analysis was performed to determine the independent effect of changes in CAP on selected outcome measures.

RESULTS

At baseline, a higher number of A1 phases per hour of sleep was significantly associated with worse behavioral functioning (caregiver Behavior Rating Inventory of Executive Function (BRIEF) Global Executive Composite (GEC): ρ = 0.24, p = 0.042; caregiver Conners' Rating Scale Global Index: ρ = 0.25, p = 0.036) and lower quality of life (OSA-18: ρ = 0.27, p = 0.022; PedsQL: ρ = -0.29, p = 0.015) in the subgroup of children with moderate OSA, but not across the entire sample. At 7-months follow-up, changes in CAP parameters were comparable between the eAT and WWSC arms. CAP changes did not account for significant proportions of variations in behavioral, cognitive, and quality-of-life performance measures at follow-up.

CONCLUSIONS

We show a significant association between the frequency of slow, high-amplitude waves with behavioral functioning, as well as the quality of life in children with moderate OSA. Early AT in children with mild-to-moderate OSA does not alter the microstructure of nonrapid eye movement sleep compared with watchful waiting after an approximately 7-month period of follow-up.

CLINICAL TRIAL

The study "A Randomized Controlled Study of Adenotonsillectomy for Children With Obstructive Sleep Apnea Syndrome" was registered at Clinicaltrials.gov (#NCT00560859).

Characterization of cyclic alternating pattern during sleep in older men and women using large population studies

STUDY OBJECTIVES

To assess the microstructural architecture of non-rapid eye movement (NREM) sleep known as cyclic alternating pattern (CAP) in relation to the age, gender, self-reported sleep quality, and the degree of sleep disruption in large community-based cohort studies of older people.

METHODS

We applied a high-performance automated CAP detection system to characterize CAP in 2,811 men from the Osteoporotic Fractures in Men Sleep Study (MrOS) and 426 women from the Study of Osteoporotic Fractures (SOF). CAP was assessed with respect to age and gender and correlated to obstructive apnea-hypopnea index, arousal index (AI-NREM), and periodic limb movements in sleep index. Further, we evaluated CAP across levels of self-reported sleep quality measures using analysis of covariance.

RESULTS

Age was significantly associated with the number of CAP sequences during NREM sleep (MrOS: p = 0.013, SOF = 0.051). CAP correlated significantly with AI-NREM (MrOS: ρ = 0.30, SOF: ρ = 0.29). CAP rate, especially the A2+A3 index, was inversely related to self-reported quality of sleep, independent of age and sleep disturbance measures. Women experienced significantly fewer A1-phases compared to men, in particular, in slow-wave sleep (N3).

CONCLUSIONS

We demonstrate that automated CAP analysis of large-scale databases can lead to new findings on CAP and its subcomponents. We show that sleep disturbance indices are associated with the CAP rate. Further, the CAP rate is significantly linked to subjectively reported sleep quality, independent from traditionally scored markers of sleep fragmentation. Finally, men and women show differences in the microarchitecture of sleep as identified by CAP, despite similar macro-architecture.

A Portable Wireless Device for Cyclic Alternating Pattern Estimation from an EEG Monopolar Derivation

Quality of sleep can be assessed by analyzing the cyclic alternating pattern, a long-lasting periodic activity that is composed of two alternate electroencephalogram patterns, which is considered to be a marker of sleep instability. Experts usually score this pattern through a visual examination of each one-second epoch of an electroencephalogram signal, a repetitive and time-consuming task that is prone to errors. To address these issues, a home monitoring device was developed for automatic scoring of the cyclic alternating pattern by analyzing the signal from one electroencephalogram derivation. Three classifiers, specifically, two recurrent networks (long short-term memory and gated recurrent unit) and one one-dimension convolutional neural network, were developed and tested to determine which was more suitable for the cyclic alternating pattern phase’s classification. It was verified that the network based on the long short-term memory attained the best results with an average accuracy, sensitivity, specificity and area under the receiver operating characteristic curve of, respectively, 76%, 75%, 77% and 0.752. The classified epochs were then fed to a finite state machine to determine the cyclic alternating pattern cycles and the performance metrics were 76%, 71%, 84% and 0.778, respectively. The performance achieved is in the higher bound of the experts’ expected agreement range and considerably higher than the inter-scorer agreement of multiple experts, implying the usability of the device developed for clinical analysis.

Visual and automatic classification of the cyclic alternating pattern in electroencephalography during sleep

Cyclic alternating pattern (CAP) is a neurophysiological pattern that can be visually scored by international criteria. The aim of this study was to verify the feasibility of visual CAP scoring using only one channel of sleep electroencephalogram (EEG) to evaluate the inter-scorer agreement in a variety of recordings, and to compare agreement between visual scoring and automatic scoring systems. Sixteen hours of single-channel European data format recordings from four different sleep laboratories with either C4-A1 or C3-A2 channels and with different sampling frequencies were used in this study. Seven independent scorers applied visual scoring according to international criteria. Two automatic blind scorings were also evaluated. Event-based inter-scorer agreement analysis was performed. The pairwise inter-scorer agreement (PWISA) was between 55.5 and 84.3%. The average PWISA was above 60% for all scorers and the global average was 69.9%. Automatic scoring systems showed similar results to those of visual scoring. The study showed that CAP could be scored using only one EEG channel. Therefore, CAP scoring might also be integrated in sleep scoring features and automatic scoring systems having similar performances to visual sleep scoring systems.

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

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.

A knowledge discovery methodology from EEG data for cyclic alternating pattern detection

Background

Detection and quantification of cyclic alternating patterns (CAP) components has the potential to serve as a disease bio-marker. Few methods exist to discriminate all the different CAP components, they do not present appropriate sensitivities, and often they are evaluated based on accuracy (AC) that is not an appropriate measure for imbalanced datasets.

Methods

We describe a knowledge discovery methodology in data (KDD) aiming the development of automatic CAP scoring approaches. Automatic CAP scoring was faced from two perspectives: the binary distinction between A-phases and B-phases, and also for multi-class classification of the different CAP components. The most important KDD stages are: extraction of 55 features, feature ranking/transformation, and classification. Classification is performed by (i) support vector machine (SVM), (ii) k-nearest neighbors (k-NN), and (iii) discriminant analysis. We report the weighted accuracy (WAC) that accounts for class imbalance.

Results

The study includes 30 subjects from the CAP Sleep Database of Physionet. The best alternative for the discrimination of the different A-phase subtypes involved feature ranking by the minimum redundancy maximum relevance algorithm (mRMR) and classification by SVM, with a WAC of 51%. Concerning the binary discrimination between A-phases and B-phases, k-NN with mRMR ranking achieved the best WAC of 80%.

Conclusions

We describe a KDD that, to the best of our knowledge, was for the first time applied to CAP scoring. In particular, the fully discrimination of the three different A-phases subtypes is a new perspective, since past works tried multi-class approaches but based on grouping of different sub-types. We also considered the weighted accuracy, in addition to simple accuracy, resulting in a more trustworthy performance assessment. Globally, better subtype sensitivities than other published approaches were achieved.

Identifying Phase-Amplitude Coupling in Cyclic Alternating Pattern using Masking Signals

Judiciously classifying phase-A subtypes in cyclic alternating pattern (CAP) is critical for investigating sleep dynamics. Phase-amplitude coupling (PAC), one of the representative forms of neural rhythmic interaction, is defined as the amplitude of high-frequency activities modulated by the phase of low-frequency oscillations. To examine PACs under more or less synchronized conditions, we propose a nonlinear approach, named the masking phase-amplitude coupling (MPAC), to quantify physiological interactions between high (α/lowβ) and low (δ) frequency bands. The results reveal that the coupling intensity is generally the highest in subtype A1 and lowest in A3. MPACs among various physiological conditions/disorders (p < 0.0001) and sleep stages (p < 0.0001 except S4) are tested. MPACs are found significantly stronger in light sleep than deep sleep (p < 0.0001). Physiological conditions/disorders show similar order in MPACs. Phase-amplitude dependence between δ and α/lowβ oscillations are examined as well. δ phase tent to phase-locked to α/lowβ amplitude in subtype A1 more than the rest. These results suggest that an elevated δ-α/lowβ MPACs can reflect some synchronization in CAP. Therefore, MPAC can be a potential tool to investigate neural interactions between different time scales, and δ-α/lowβ MPAC can serve as a feasible biomarker for sleep microstructure.

Sleep cyclic alternating pattern in otherwise healthy overweight school-age children

STUDY OBJECTIVES

To compare sleep microstructure (cyclic alternating pattern, CAP) characteristics in otherwise healthy overweight (OW) and normal weight (NW) children.

DESIGN

Polysomnographic cross-sectional study.

SETTING

Sleep laboratory.

PARTICIPANTS

Fifty-eight (26 NW and 32 OW) 10-year-old children.

INTERVENTIONS

N/A.

MEASUREMENTS AND RESULTS

Participants were part of a longitudinal study beginning in infancy and free of sleep disorders. Groups were based on body-mass index (BMI) z-score. From polysomnographic overnight recordings, sleep-waking states were scored according to international criteria. CAP analysis was performed visually during NREM sleep. Conventional sleep parameters were similar between groups. BMI was positively related to CAP rate and CAP sequences but inversely related to CAP B phase duration. Differences between groups were confined to slow-wave sleep (SWS), with OW children showing higher CAP rate, CAP cycles, and CAP A1 number and index and shorter CAP cycles and B phase duration. They also showed more CAP class intervals shorter than 30 s, and a suggestive trend for fewer intervals longer than 30 s.

CONCLUSIONS

Cyclic alternating pattern characteristics in children related to nutritional status and were altered in overweight subjects during slow-wave sleep. We suggest that the more frequent oscillatory pattern of electroencephalographic slow activity in overweight subjects might reflect less stable slow-wave sleep episodes.

A review of signals used in sleep analysis

This article presents a review of signals used for measuring physiology and activity during sleep and techniques for extracting information from these signals. We examine both clinical needs and biomedical signal processing approaches across a range of sensor types. Issues with recording and analysing the signals are discussed, together with their applicability to various clinical disorders. Both univariate and data fusion (exploiting the diverse characteristics of the primary recorded signals) approaches are discussed, together with a comparison of automated methods for analysing sleep.

On separability of A-phases during the cyclic alternating pattern

A statistical analysis of the separability of EEG A-phases, with respect to basal activity, is presented in this study. A-phases are short central events that build up the Cyclic Alternating Pattern (CAP) during sleep. The CAP is a brain phenomenon which is thought to be related to the construction, destruction and instability of sleep stages dynamics. From the EEG signals, segments obtained around the onset and offset of the A-phases were used to evaluate the separability between A-phases and basal sleep stage oscillations. In addition, a classifier was trained to separate the different A-phase types (A1, A2 and A3). Temporal, energy and complexity measures were used as descriptors for the classifier. The results show a percentage of separation between onset and preceding basal oscillations higher than 85 % for all A-phases types. For Offset separation from following baseline, the accuracy is higher than 80 % but specificity is around 75%. Concerning to A-phase type separation, A1-phase and A3-phase are well separated with accuracy higher than 80, while A1 and A2-phases show a separation lower than 50%. These results encourage the design of automatic classifiers for Onset detection and for separating among A-phases type A1 and A3. On the other hand, the A-phase Offsets present a smooth transition towards the basal sleep stage oscillations, and A2-phases are very similar to A1-phases, suggesting that a high uncertainty may exist during CAP annotation.

EEG segmentation for improving automatic CAP detection

OBJECTIVE

The aim of this study is to provide an improved method for the automatic classification of the Cyclic Alternating Pattern (CAP) sleep by applying a segmentation technique to the computation of descriptors from the EEG.

METHODS

A dataset of 16 polysomnographic recordings from healthy subjects was employed, and the EEG traces underwent first an automatic isolation of NREM sleep portions by means of an Artificial Neural Network and then a segmentation process based on the Spectral Error Measure. The information content of the descriptors was evaluated by means of ROC curves and compared with that of descriptors obtained without the use of segmentation. Finally, the descriptors were used to train a discriminant function for the automatic classification of CAP phases A.

RESULTS

A significant improvement with respect to previous scoring methods in terms of both information content carried by the descriptors and accuracy of the classification was obtained.

CONCLUSIONS

EEG segmentation proves to be a useful step in the computation of descriptors for CAP scoring.

SIGNIFICANCE

This study provides a complete method for CAP analysis, which is entirely automatic and allows the recognition of A phases with a high accuracy thanks to EEG segmentation.

Sleep Microstructure Is Not Altered in Children With Attention-Deficit/Hyperactivity Disorder (ADHD)

The high rate of occurrence of sleep disturbances in children with attention-deficit/hyperactivity disorder (ADHD) prompted the idea that structural and neurotransmitter changes might give rise to specific sleep pattern abnormalities. The aim of this study was to evaluate the microstructure of sleep in children with ADHD who had no polysomnographically diagnosed sleep disorder, had never been treated for ADHD, and were free from any psychiatric comorbidity. Participants were 14 patients with ADHD (12 boys and 2 girls aged 7-12 years, mean age 9.6±1.6). ADHD was diagnosed according to DSM-IV criteria (Diagnostic and statistical manual of mental disorders). Psychiatric comorbidities were ruled out by detailed psychiatric examination. The patients underwent two consecutive overnight video-polysomnographic (PSG) recordings, with the sleep microstructure (cyclic alternating pattern – CAP) scoring during the second night. The data were compared with age- and sex-matched controls. Sleep microstructure analysis using CAP revealed no significant differences between the ADHD group and the controls in any of the parameters under study. In conclusions, no ADHD-specific alterations were found in the sleep microstructure.

Type 2 diabetes and pre-diabetes are associated with obstructive sleep apnea in extremely obese subjects: a cross-sectional study

Background

Obstructive sleep apnea (OSA) is a common yet underdiagnosed condition. The aim of our study is to test whether prediabetes and type 2 diabetes are associated with obstructive sleep apnea (OSA) in extremely obese (BMI ≥ 40 kg/m²) subjects.

Methods

One hundred and thirty seven consecutive extremely obese patients (99 females) from a controlled clinical trial [MOBIL-study (Morbid Obesity treatment, Bariatric surgery versus Intensive Lifestyle intervention Study) (ClinicalTrials.gov number NCT00273104)] underwent somnography with Embletta^® and a 2-hour oral glucose tolerance test (OGTT). OSA was defined by an apnea-hypopnea index (AHI) ≥ 5 events/hour. Patients were categorized into three groups according to criteria from the American Diabetes Association: normal glucose tolerance, pre-diabetes and type 2 diabetes. Multiple logistic regression analysis was used to identify possible determinants of OSA.

Results

The patients had a mean (SD) age of 43 (11) years and a body mass index (BMI) of 46.9 (5.7) kg/m². Males had significantly higher AHI than females, 29 (25) vs 12 (17) events/hour, p < 0.001. OSA was observed in 81% of men and in 55% of women, p = 0.008. Twenty-nine percent of subjects had normal glucose tolerance, 42% had pre-diabetes and 29% had type 2 diabetes. Among the patients with normal glucose tolerance 33% had OSA, while 67% of the pre-diabetic patients and 78% of the type 2 diabetic patients had OSA, p < 0.001. After adjusting for age, gender, BMI, high sensitive CRP and HOMA-IR, both pre-diabetes and type 2 diabetes were still associated with OSA, odds ratios 3.18 (95% CI 1.00, 10.07), p = 0.049 and 4.17 (1.09, 15.88), p = 0.036, respectively. Mean serum leptin was significantly lower in the OSA than in the non-OSA group, while other measures of inflammation did not differ significantly between groups.

Conclusions

Type 2 diabetes and pre-diabetes are associated with OSA in extremely obese subjects.

Trial registration

MOBIL-study (Morbid Obesity treatment, Bariatric surgery versus Intensive Lifestyle intervention Study) (ClinicalTrials.gov number NCT00273104)

Cyclic alternating pattern (CAP): the marker of sleep instability

Cyclic alternating pattern CAP is the EEG marker of unstable sleep, a concept which is poorly appreciated among the metrics of sleep physiology. Besides, duration, depth and continuity, sleep restorative properties depend on the capacity of the brain to create periods of sustained stable sleep. This issue is not confined only to the EEG activities but reverberates upon the ongoing autonomic activity and behavioral functions, which are mutually entrained in a synchronized oscillation. CAP can be identified both in adult and children sleep and therefore represents a sensitive tool for the investigation of sleep disorders across the lifespan. The present review illustrates the story of CAP in the last 25 years, the standardized scoring criteria, the basic physiological properties and how the dimension of sleep instability has provided new insight into pathophysiolology and management of sleep disorders.

Sleep cyclic alternating pattern analysis in healthy children during the first year of life: a daytime polysomnographic study

We evaluated the cyclic alternating pattern (CAP) during the first year of life in order to obtain information on the maturation of arousal mechanisms during NREM sleep and to provide normative data for CAP parameters in this age range (5-16months). Eleven healthy children (mean age 7.9±3.3months, seven boys) were studied while they slept in the morning. They underwent a 3-h video-EEG-polysomnographic recording at the Pediatric Sleep Unit of Sant'Andrea Hospital in Rome, Italy. Sleep was scored visually for sleep architecture and CAP analysis using standard criteria. Our results were complemented by CAP data from a previous sample of healthy infants (2-4months), studied when they slept during the morning, in order to correlate CAP parameters with age. The total sample comprised 24 children. The sleep period was approximately 2h, with a first REM latency of about 30min, and a clear distinction between stages N1, N2, and N3. The arousal index was 12±2.1 events/hour of sleep. The total CAP rate was 23.7±7.6%, and it increased progressively with the deepness of sleep; the highest values were observed during stage N3 and the lowest values during stage N1. A1 phases were the most numerous (78.2%), followed by A2 (14%) and A3 (7.7%) phases. The A1 index was higher than the A2 and A3 indices, whereas the mean duration of B was higher than that of A. The correlation showed that the CAP rate, A1, A2, A3 indices, A2, A3 percentages, and the average duration of B increased with age, whereas the A1 percentage decreased. We provide the first data on CAP analysis in children aged 5-16months, studied when they slept during the morning. Our results confirm the trend toward an increase in CAP rate during the first year of life. In addition, we observed a progressive increase in CAP rate with deepness of sleep, and with age, reflecting maturation of slow-wave activity. The decreased percentage of A1 subtypes may reflect the maturation of arousability.

Characterization of A phases during the cyclic alternating pattern of sleep

OBJECTIVE

This study aims to identify, starting from a single EEG trace, quantitative distinctive features characterizing the A phases of the Cyclic Alternating Pattern (CAP).

METHODS

The C3-A2 or C4-A1 EEG leads of the night recording of eight healthy adult subjects were used for this analysis. CAP was scored by an expert and the portions relative to NREM were selected. Nine descriptors were computed: band descriptors (low delta, high delta, theta, alpha, sigma and beta); Hjorth activity in the low delta and high delta bands; differential variance of the EEG signal. The information content of each descriptor in recognizing the A phases was evaluated through the computation of the ROC curves and the statistics sensitivity, specificity and accuracy.

RESULTS

The ROC curves show that all the descriptors have a certain significance in characterizing A phases. The average accuracy obtained by thresholding the descriptors ranges from 59.89 (sigma descriptor) to 72.44 (differential EEG variance).

CONCLUSIONS

The results show that it is possible to attribute a significant quantitative value to the information content of the descriptors.

SIGNIFICANCE

This study gives a mathematical confirm to the features of CAP generally described qualitatively, and puts the bases for the creation of automatic detection methods.

Automatic detection of a phases of the cyclic alternating pattern during sleep

This study aimed to develop an automatic algorithm to detect the activation phases (A phases) of the Cyclic Alternating Pattern. The sleep EEG microstructure of 4 adult, healthy subjects was scored by a sleep medicine expert. Features were calculated from each of the six EEG bands (low delta, high delta, theta, alpha, sigma and beta), and three additional characteristics were computed: the Hjorth activity in the low delta and high delta bands, and the differential variance of the raw EEG signal. The correlation between couples of features was analyzed to find redundancies for the automatic analysis. The features were used to train an Artificial Neural Network to automatically find the A phases of CAP. The data were divided into training, validation and testing set, and the visual scoring provided by the clinician was used as the desired output. The statistics on the second by second classification show an average sensitivity equal to 76%, specificity equal to 83% and accuracy equal to 82%. The results obtained are encouraging, since an automatic classification of the A phases could benefit the practice in clinics, preventing the physician from the time-consuming activity of visually scoring the sleep microstructure over the whole eight-hour sleep recordings. Moreover, it would provide an objective criterion capable of overcoming the problems of inter-scorer variability.

Automatic detection of CAP on central and fronto-central EEG leads via Support Vector Machines

The aim of this study is to implement a high-accuracy automatic detector of the Cyclic Alternating Pattern (CAP) during sleep. EEG data from four healthy subjects were used. Both the C4-A1 and the F4-C4 leads were analyzed for this study. Seven features were extracted from each of the two leads and two separate studies were performed for each set of descriptors. For both sets, a Support Vector Machine was trained and tested on the data with the Leave One Out cross-validation method. The two final classifications obtained on the two sets were merged, by considering a CAP A phase scored only if it had been recognized both on the central and on the frontal lead. The length of the A phase was then determined by the result on the fronto-central lead. This method leads to encouraging results, with a classification sensitivity on the whole dataset equal to 73.82%, specificity equal to 85.93%, accuracy equal to 84,05% and Cohen's kappa equal to 0.50.

Sleep characteristics in children with growth hormone deficiency

BACKGROUND/AIMS

Growth hormone (GH) is preferentially secreted during slow wave sleep and the interactions between human sleep and the somatotropic system are well documented, although only few studies have investigated the sleep EEG in children with GH deficiency (GHD). The aim of this study was to evaluate the sleep structure of children with dysregulation of the GH/insulin-like growth factor axis.

METHODS

Laboratory polysomnographic sleep recordings were obtained from 10 GHD children and 20 normal healthy age-matched children. The classical sleep parameters were evaluated together with sleep microstructure, by means of the cyclic alternating pattern (CAP), in GHD patients and compared to the control group.

RESULTS

GHD children showed a significant decrease in total sleep time, sleep efficiency, movement time and in non-rapid eye movement sleep stage 2. Although some indicators of sleep fragmentation were increased in GHD children, we found a general decrease in EEG arousability represented by a significant global decrease in the CAP rate, involving all CAP A phase subtypes.

CONCLUSIONS

The analysis of sleep microstructure by means of CAP, in children with GHD, showed a reduction of transient EEG amplitude oscillations. Further studies are needed in order to better clarify whether GH therapy is able to modify sleep microstructure in GHD children, and the relationships between sleep microstructure, hormonal secretion and neurocognitive function in these patients.

Acute dopamine-agonist treatment in restless legs syndrome: effects on sleep architecture and NREM sleep instability

STUDY OBJECTIVES

To analyze cyclic alternating pattern (CAP) in restless legs syndrome (RLS) and the eventual changes induced by the acute administration of pramipexole.

SETTING

Sleep clinic in a scientific research institute.

INTERVENTIONS

Placebo or pramipexole 0.25 mg.

METHODS

Thirty-four patients were included: 19 patients received 0.25 mg of pramipexole and 15 were given placebo. The control group included 13 normal subjects. Nocturnal polysomnography was carried out in all subjects, and a second night was recorded after pramipexole or placebo was administered to patients with RLS. Sleep stages, CAP, and leg movement activity were scored following standard criteria.

MEASUREMENTS AND RESULTS

At baseline, rapid eye movement sleep latency was significantly longer in patients with RLS than in normal control subjects, and the periodic leg movement during sleep index (PLMS) was also significantly higher. On the contrary, many CAP parameters appeared to be significantly different, with a general increase in CAP rate in patients with RLS. Acute administration of pramipexole induced moderate changes in sleep architecture (increased number of stage shifts/h, sleep efficiency, and percentage of stage 2 sleep; decreased wakefulness after sleep onset; and a lower PLMS index. No effects of treatment on CAP were observed.

CONCLUSION

Patients with RLS show significant abnormalities in sleep microstructure, represented by an excessive sleep instability/discontinuity. Acute pramipexole administration seems to exert no action on these abnormalities; the moderate effects seen on sleep architecture might be interpreted as the beneficial consequence of the removal of presleep RLS symptoms and PLMS.

Commentary from the Italian Association of Sleep Medicine on the AASM manual for the scoring of sleep and associated events: for debate and discussion

In 2007, the American Academy of Sleep Medicine (AASM) completed a new manual for the scoring of sleep and associated events. The AASM manual is divided into separate sections relative to the parameters reported for polysomnography. The present commentary, accomplished by a Task Force of the Italian Association of Sleep Medicine, focuses on sleep scoring data, arousal rules, movement and respiratory events. Comparisons with the previous Rechtschaffen and Kales system are detailed and a number of methodological weaknesses are pointed out. Major comments address the 30-s scoring epochs, the restrictive approach to arousals and EEG activating patterns, the incomplete quantification of motor events and the thresholds for the definition of hypopnea. Since the new AASM manual is an iterative process, proposals for discussion and re-examination of the agreed criteria with other national and international organizations are encouraged.

Non-REM sleep instability in patients with major depressive disorder: Subjective improvement and improvement of non-REM sleep instability with treatment (Agomelatine)

OBJECTIVE

To assess the importance of non-rapid eye movement (NREM) sleep disturbance in major depressive disorder (MDD) patients using cyclic alternating pattern (CAP) analysis, and to determine the usefulness of CAP analysis in evaluating treatment effect.

METHODS

Baseline sleep-staging data and CAP analysis of NREM sleep was compared in 15 MDD patients (Hamilton depression scale score>20) and normal controls. Longitudinal evaluation of sleep changes using similar analysis during a treatment trial was also performed.

ANALYSIS

A single-blinded researcher scored and analyzed the sleep of MDD and age-matched normal controls at baseline and during a treatment trial using the international scoring system as well as CAP analysis.

RESULTS

MDD patients had evidence of disturbed sleep with both analyses, but CAP analysis revealed more important changes in NREM sleep of MDD patients at baseline than did conventional sleep staging. There was a significant decrease in CAP rate, time, and cycle and disturbances of phase A subtype of CAP. NREM abnormalities, observed by CAP analysis, during the treatment trial paralleled subjective responses. Analysis of subtype A phase of CAP demonstrated better sleep improvement.

CONCLUSION

CAP analysis demonstrated the presence of more important NREM sleep disturbances in MDD patients than did conventional sleep staging, suggesting the involvement of slow wave sleep (SWS) in the sleep impairment of MDD patients. Improvement of NREM sleep paralleled subjective mood improvement and preceded REM sleep improvement. CAP analysis allowed objective investigation of the effect of treatment on sleep disturbances.

Visual and automatic cyclic alternating pattern (CAP) scoring: inter-rater reliability study

The classification of short duration events in the EEG during sleep, as the A stage of the cyclic alternating pattern (CAP) is a tedious and error prone task. The number of events under normal conditions is large (several hundreds), and it is necessary to mark the limits of the events with precision, otherwise the time sensitive classification of the CAP phases (A and B) and specially the scoring of different types of A phases will be compromised. The objective of this study is to verify the feasibility of visual CAP scoring with only one channel of EEG, the evaluation of the inter-scorer agreement in a variety of recordings, and the comparison of the visual scorings with a known automatic scoring system. Sixteen hours of one channel (C4-A1 or C3-A2) of NREM sleep were extracted from eight whole night recordings in European Data Format and presented to the different scorers. The average inter-scorer agreement for all scorers is above 70%, the pair wise inter-scorer agreement found was between 69% up to 77.5%. These values are similar to what has been reported in different type studies. The automatic scoring system has similar performance of the visual scorings. The study also has shown that it is possible to classify the CAP using only one channel of EEG.

All-night EEG power spectral analysis of the cyclic alternating pattern components in young adult subjects

OBJECTIVE

To analyze in detail the frequency content of the different EEG components of the Cyclic Alternating Pattern (CAP), taking into account the ongoing EEG background and the nonCAP (NCAP) periods in the whole night polysomnographic recordings of normal young adults.

METHODS

Sixteen normal healthy subjects were included in this study. Each subject underwent one polysomnographic night recording; sleep stages were scored following standard criteria. Subsequently, each CAP A phase was detected in all recordings, during NREM sleep, and classified into 3 subtypes (A1, A2, and A3). The same channel used for the detection of CAP A phases (C3/A2 or C4/A1) was subdivided into 2-s mini-epochs. For each mini-epoch, the corresponding CAP condition was determined and power spectra calculated in the frequency range 0.5-25 Hz. Average spectra were obtained for each CAP condition, separately in sleep stage 2 and SWS, for each subject. Finally, the first 6h of sleep were subdivided into 4 periods of 90 min each and the same spectral analysis was performed for each period.

RESULTS

During sleep stage 2, CAP A subtypes differed from NCAP periods for all frequency bins between 0.5 and 25 Hz; this difference was most evident for the lowest frequencies. The B phase following A1 subtypes had a power spectrum significantly higher than that of NCAP, for frequencies between 1 and 11 Hz. The B phase after A2 only differed from NCAP for a small but significant reduction in the sigma band power; this was evident also after A3 subtypes. During SWS, we found similar results. The comparison between the different CAP subtypes also disclosed significant differences related to the stage in which they occurred. Finally, a significant effect of the different sleep periods was found on the different CAP subtypes during sleep stage 2 and on NCAP in both sleep stage 2 and SWS.

CONCLUSIONS

CAP subtypes are characterized by clearly different spectra and also the same subtype shows a different power spectrum, during sleep stage 2 or SWS. This finding underlines a probable different functional meaning of the same CAP subtype during different sleep stages. We also found 3 clear peaks of difference between CAP subtypes and NCAP in the delta, alpha, and beta frequency ranges which might indicate the presence of 3 frequency components characterizing CAP subtypes, in different proportion in each of them. The B component of CAP differs from NCAP because of a decrease in power in the sigma frequency range.

SIGNIFICANCE

This study shows that A components of CAP might correspond to periods in which the very-slow delta activity of sleep groups a range of different EEG activities, including the sigma and beta bands, while the B phase of CAP might correspond to a period in which this activity is quiescent or inhibited.