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Idiopathic pulmonary fibrosis (IPF) is an interstitial lung disease (ILD) characterized by inflammation and progressive scarring of the lung parenchyma. IPF profoundly affects the quality of life (QoL) and fatigue is a frequently disabling symptom. The cause of fatigue is not well understood but patients with IPF often report extremely poor sleep quality and sleep-related breathing disorders (SRBD) that correlate with QoL. IPF patients present alterations in sleep architecture, including decreased sleep efficiency, slow wave sleep and rapid eye movement (REM) sleep, and increased sleep fragmentation. Moreover, sleep related hypoventilation during the vulnerable REM sleep period and obstructive sleep apnea-hypopnea syndrome (OSAHS) are frequent, but remain usually underdiagnosed. These SRBD in IPF are associated with alterations of the sleep structure, reduction of QoL and increased risk of mortality. In the absence of an effective therapy for IPF, optimizing the QoL could become the primary therapeutic goal. In this perspective the diagnosis and treatment of SRBD could significantly improve the QoL of IPF patients., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

An analysis of the EEG signal during the B-phase and A-phases transitions of the cyclic alternating pattern (CAP) during sleep is presented. CAP is a sleep phenomenon composed by consecutive sequences of A-phases (each A-phase could belong to a possible group A1, A2 or A3) observed during the non-REM sleep. Each A-phase is separated by a B-phase which has the basal frequency of the EEG during a specific sleep stage. The patterns formed by these sequences reflect the sleep instability and consequently help to understand the sleep process. Ten recordings from healthy good sleepers were included in this study. The current study investigates complexity, statistical and frequency signal properties of electroencephalography (EEG) recordings at the transitions: B-phase--A-phase. In addition, classification between the onset-offset of the A-phases and B-phase was carried out with a kNN classifier. The results showed that EEG signal presents significant differences (p < 0.05) between A-phases and B-phase for the standard deviation, energy, sample entropy, Tsallis entropy and frequency band indices. The A-phase onset showed values of energy three times higher than B-phase at all the sleep stages. The statistical analysis of variance shows that more than 80% of the A-phase onset and offset is significantly different from the B-phase. The classification performance between onset or offset of A-phases and background showed classification values over 80% for specificity and accuracy and 70% for sensitivity. Only during the A3-phase, the classification was lower. The results suggest that neural assembles that generate the basal EEG oscillations during sleep present an over-imposed coordination for a few seconds due to the A-phases. The main characteristics for automatic separation between the onset-offset A-phase and the B-phase are the energy at the different frequency bands.

Purpose: The scoring of American Academy of Sleep Medicine (AASM) arousal is mandatory for the definition of respiratory event-related arousal (RERA). However there are other EEG activation phenomena, such as A phases of cyclic alternating pattern (CAP) which are associated with respiratory events in non rapid eye movements (NREM) sleep. This study aims at quantifying the additional value of CAP for the definition of respiratory events and sleep alterations in OSAS., Methods: Analysis of polysomnographic recordings from nineteen OSAS patients was carried out. Scoring was focused on investigation of the cerebral response to flow limitation (FL) events. For this purpose we used both CAP rules and AASM arousal criteria., Main Results: While no difference was demonstrated in the arousal index between mild and moderate-severe OSAS patients, CAP time showed a progressive enhancement from normal subjects (152.5±20.76) to mild (180.64±34.76) and moderate-severe (282.27±58.02) OSAS patients. In NREM sleep, only 41.1% of FL events met the criteria for the definition of RERA, while, 75.5% of FL events ended with a CAP A phase and most FL CAP (69.1%) terminated with a CAP phase A3 subtype., Conclusions: Our data indicate that the RERA scoring has a limited accuracy in the detection of FL events. In NREM sleep, CAP rules provided more information than AASM arousal for the definition of respiratory events and sleep alterations in OSAS.

Insomnia is a condition that affects the nervous and muscular system. Thirty percent of the population between 18 and 60 years suffers from insomnia. The effects of this disorder involve problems such as poor school or job performance and traffic accidents. In addition, patients with insomnia present changes in the cardiac function during sleep. Furthermore, the structure of electroencephalographic A-phases, which builds up the Cyclic Alternating Pattern during sleep, is related to the insomnia events. Therefore, the relationship between these brain activations (A-phases) and the autonomic nervous system would be of interest, revealing the interplay of central and autonomic activity during insomnia. With this goal, a study of the relationship between A-phases and heart rate fluctuations is presented. Polysomnography recording of five healthy subjects, five sleep misperception patients and five patients with psychophysiological insomnia were used in the study. Detrended Fluctuation Analysis (DFA) was used in order to evaluate the heart rate dynamics and this was correlated with the number of A-phases. The results suggest that pathological patients present changes in the dynamics of the heart rate. This is reflected in the modification of A-phases dynamics, which seems to modify of heart rate dynamics.

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.

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., (Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.)

Objective: To study the effects of antiepileptic treatment on sleep parameters and video-polysomnography (VPSG) seizures in nocturnal frontal lobe epilepsy (NFLE)., Methods: Twenty patients with a clinical and VPSG diagnosis of NFLE (baseline polysomnography [PSG]) underwent a clinical follow-up and performed a second VPSG after effective antiepileptic treatment lasting for at least 6 months. Conventional sleep measures, cyclic alternating pattern (CAP) parameters, and objective VPSG seizures were assessed in NFLE patients before and after treatment and were compared with the results of 20 age- and gender-matched control subjects., Results: Antiepileptic treatment determined a partial reduction of objective VPSG seizures of approximately 25% compared to baseline condition. Alterations of most conventional sleep measures recovered normal values, but nonrapid eye movement (NREM) sleep instability remained pathologically enhanced (CAP rate, +26% compared to controls) and was associated with persistence of daytime sleepiness., Conclusions: Residual epileptic events and high levels of unstable NREM sleep can define a sort of objective resistance of both seizures and disturbed arousal system to the therapeutic purpose of the antiepileptic drugs in NFLE. This finding could determine the need for new therapeutic options in this particular form of epilepsy., (Copyright © 2013 Elsevier B.V. All rights reserved.)

Atypical cardiorespiratory patterns can be found during routine clinical use of portable monitoring for diagnosis of sleep-disordered breathing (SDB). Over 1,000 consecutive portable recordings were analysed to study the potential ictal nature of stereotyped cardiorespiratory and motor patterns. Snoring, airflow, thoracic effort, pulse rate, body position, oxygen saturation and activity of the anterior tibialis muscles were quantified. Recordings showing stereotyped polygraphic patterns recurring throughout the night, but without the features of sleep apnoea (apnoea/hypopnoea index <5 events·h(-1)), were selected for investigation. Once included in the study, patients underwent attended nocturnal video polysomnography. A total of 15 recordings showing repeated polygraphic patterns characterised by a sequence of microphone activation, respiratory activity atypical for sleep and wakefulness, heart rate acceleration and limb movements, followed by body position change, were selected for investigation. Once included in the study, patients underwent attended nocturnal video polysomnography that showed frontal epileptic discharges triggering periodic electroencephalographic arousals, autonomic activation and stereotyped motor patterns. A diagnosis of nocturnal frontal lobe epilepsy (NFLE) was established for all patients. NFLE should be taken into consideration in patients with stereotyped and recurrent behavioural features during portable monitoring carried out for diagnosis of SDB.

Pitt-Hopkins syndrome is a rare genetic form of severe psychomotor delay, caused by mutations in transcription cell factor-4 gene and characterized by distinctive dysmorphic features and abnormal breathing pattern. The current report describes the polygraphic features of the syndrome's typical breathing pattern in a patient both in wakefulness and in sleep. The control of these breathing alterations is important to prevent the neurological sequelae linked to chronic cerebral hypoxemia in early ages. No data are available on effective treatment options for breathing abnormalities of Pitt-Hopkins syndrome. The authors polygraphically documented a reduction of apneic and hypopneic phenomena, with a significant improvement in saturation values, after the introduction of sodium valproate.

This work aims to investigate new markers for the quantitative characterization of insomnia, in the context of sleep microstructure, as expressed by cyclic alternating pattern (CAP) sleep. The study group includes 11 subjects with normal sleep and 10 subjects with diagnosed primary insomnia. Differences between normal sleepers and insomniacs are investigated, in terms of dynamics and content of CAP events. The overall rate of CAP and of different phases is considered. The dynamic in the structure and alternation of CAP events is further studied in different scales by use of wavelet analysis, and calculation of energy/entropy features. The content of CAP events is studied in terms of electroencephalography (EEG) complexity analysis for the different types of events. Statistically significant differences are highlighted, both in structure and content. Besides confirming the increase in CAP rate, main findings regarding the microstructure difference in insomnia include: 1) as regards the deep sleep building phases, more irregular activation-deactivation patterns, with bigger deactivation time, i.e., distance between consecutive activation events, and appearing with higher EEG complexity in deactivation, and 2) a bigger duration of desynchronisation phases, with increased EEG complexity and more irregular patterns. This analysis extends previous findings on the relation between CAPrate increase and sleep instability mechanisms, proposing specific features of CAP that seem to play a role in insomnia (as consistently presented via classification analysis). This opens new perspectives for the understanding of the role of CAP in the quantitative characterization of sleep and its disorders.

Purpose: To describe the polysomnographic features and distribution of epileptic motor events, in relation to conventional sleep measures and cyclic alternating pattern (CAP) parameters, in 40 untreated patients with nocturnal frontal lobe epilepsy (NFLE)., Methods: We analyzed the basal polysomnographic recordings of 40 patients (20 male and 20 female; mean age: 31 ± 10 years) with a diagnosis of nocturnal frontal lobe epilepsy. Conventional sleep measures and CAP parameters were assessed. Polysomnographic recordings were subdivided in sleep cycles. The distribution of the epileptic motor events (including minor motor events, paroxysmal arousals, tonic-dystonic, or hyperkinetic seizures and epileptic nocturnal wandering) was analyzed throughout: total sleep time, non-rapid eye movement (NREM) and REM sleep, light sleep (S1 + S2), slow wave sleep (SWS), each sleep cycle, CAP or non-CAP sleep, phase A and phase B of CAP. Only clear epileptic motor events supported by video-polysomnographic evidence were taken into consideration. Polysomnographic findings of patients with NFLE were compared with those of 24 age- and gender-balanced healthy subjects without sleep complaints., Key Findings: Compared to controls, patients with NFLE showed a significant increase in wake after sleep onset, SWS duration, and REM latency, whereas REM sleep duration was significantly lower in NFLE patients. The patients with NFLE showed a significant increase of CAP time, CAP rate (72% vs. 32% in control group), CAP cycles, and mean duration of a CAP sequence. These findings were associated with a significant enhancement of all subtypes of the A phases of CAP (mainly subtype A1). A total of 139 epileptic motor events supported by video-polysomnographic evidence were counted: 98% of all seizures occurred in NREM sleep and 72% of NREM seizures emerged from SWS, the latter being particularly collected in the first sleep cycles and decreasing in frequency together with the progressive decline of deep sleep. Ninety percent of total NREM seizures occurred during a CAP sequence, and CAP-related seizures occurred in association with a phase A., Significance: Significant polysomnographic alterations seem to emerge in patients with NFLE (increased REM latency, epileptic fragmentation of SWS, and increase of CAP rate). The analysis of seizure distribution showed that most epileptic events occurred in SWS, with predominance in the first sleep cycle and decreasing in frequency together with the homeostatic decline of SWS across the night. Within the NREM sleep, CAP is a manifestation of unstable sleep and represents a powerful predisposing condition for the occurrence of nocturnal motor seizures, which arise in concomitance with a phase A., (Wiley Periodicals, Inc. © 2012 International League Against Epilepsy.)

The aim of this study was to arrange an automatic quantitative measure of the electroencephalographic (EEG) signal amplitude variability during non-rapid eye movement (NREM) sleep, correlated with the visually extracted cyclic alternating pattern (CAP) parameters. Ninety-eight polysomnographic EEG recordings of normal controls were used. A new algorithm based on the analysis of the EEG amplitude variability during NREM sleep was designed and applied to all recordings, which were also scored visually for CAP. All measurements obtained with the new algorithm correlated positively with corresponding CAP parameters. In particular, total CAP time correlated with total NREM variability time (r = 0.596; P < 1E-07), light sleep CAP time with light sleep variability time (r = 0.597; P < 1E-07) and slow wave sleep CAP time with slow wave sleep variability time (r = 0.809; P < 1E-07). Only the duration of CAP A phases showed a low correlation with the duration of variability events. Finally, the age-related modifications of CAP time and of NREM variability time were found to be very similar. The new method for the automatic analysis of NREM sleep amplitude variability presented here correlates significantly with visual CAP parameters; its application requires a minimum work time, compared to CAP analysis, and might be used in large studies involving numerous recordings in which NREM sleep EEG amplitude variability needs to be assessed., (© 2011 European Sleep Research Society.)

This study aims to develop an automatic detector of the A phases of the cyclic alternating pattern, periodic activity that generally occurs during non-REM (NREM) sleep. Eight polysomnographic recordings from healthy subjects were examined. From EEG recordings, five band descriptors, an activity descriptor and a variance descriptor were extracted and used to train different machine-learning algorithms. A visual scoring provided by an expert clinician was used as golden standard. Four alternative mathematical machine-learning techniques were implemented: (1) discriminant classifier, (2) support vector machines, (3) adaptive boosting, and (4) supervised artificial neural network. The results of the classification, compared with the visual analysis, showed average accuracies equal to 84.9 and 81.5% for the linear discriminant and the neural network, respectively, while AdaBoost had a slightly lower accuracy, equal to 79.4%. The SVM leads to accuracy of 81.9%. The performance achieved by the automatic classification is encouraging, since an efficient automatic classifier would benefit the practice in everyday clinics, preventing the physician from the time-consuming activity of the visually scoring of the sleep microstructure over whole 8-h sleep recordings. Finally, the classification based on learning algorithms would provide an objective criterion, overcoming the problems of inter-scorer disagreement.

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., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

This study proposes a novel method to assist the detection of the components that build up the Cyclic Alternating Pattern (CAP). CAP is a sleep phenomenon formed by consecutive sequences of activations (A1, A2, A3) and non-activations during nonREM sleep. The main importance of CAP evaluation is the possibility of defining the sleep process more accurately. Ten recordings from healthy and good sleepers were included in this study. The method is based on inferential statistics to define the initial and ending points of the CAP components based only on an initialization point given by the expert. The results show concordance up to 95% for A1, 85% for A2 and 60% for A3, together with an overestimation of 1.5 s in A1, 1.3 s in A2 and 0 s in A3. The total CAP rate presents a total underestimation of 7 min. Those results suggest that the method is able to accurately detect the initial and ending points of the activations, and may be helpful for the physicians by reducing the time dedicated to the manual inspection task.

This study analyzes the nonlinear properties of the EEG at transition points of the sequences that build the Cyclic Alternating Pattern (CAP). CAP is a sleep phenomenon built up by consecutive sequences of activations and non-activations observed during the sleep time. The sleep condition can be evaluated from the patterns formed by these sequences. Eleven recordings from healthy and good sleepers were included in this study. We investigated the complexity properties of the signal at the onset and offset of the activations. The results show that EEG signals present significant differences (p&#60;0.05) between activations and non-activations in the Sample Entropy and Tsallis Entropy indices. These indices could be useful in the development of automatic methods for detecting the onset and offset of the activations, leading to significant savings of the physician's time by simplifying the manual inspection task.

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., (Copyright © 2011 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.)

Objective: To investigate the regulation NREM sleep at baseline and in morning recovery sleep after partial and total sleep deprivation (SD) in narcolepsy-cataplexy (NC) using cyclic alternating pattern (CAP)., Methods: Daytime sleep under either increased (no sleep in the previous night) or decreased sleep pressure (allowing 4h of sleep, 23:00-3:00 h) was recorded in ten drug-free, HLA-positive, hypocretin deficient NC patients and ten age, gender and body mass index matched healthy controls. Baseline sleep was also recorded and used for comparison purposes. CAP parameters were scored and analyzed for each subject., Results: Narcolepsy patients had significantly lower CAP rate, CAP index, CAP time, number of CAP cycles, A1 index and number of A1 cycles in comparison to healthy controls at baseline as well as after partial and total SD. In both narcolepsy patients and healthy control subjects there was a significant decrease in these parameters after partial and total SD but the changes followed a similar pattern., Conclusion: The persistence of baseline differences in CAP parameters between narcolepsy patients and healthy controls and their similar behavior after partial and total SD suggests similar homeostatic NREM sleep regulation but on a different level., Significance: CAP analysis demonstrates that NREM sleep homeostasis although altered, is functional in narcolepsy patients., (Copyright © 2011. Published by Elsevier Ireland Ltd.)

Rhythmic masticatory muscle activity (RMMA) is the characteristic electromyographic pattern of sleep bruxism (SB), a sleep-related motor disorder associated with sleep arousal. Sleep arousals are generally organised in a clustered mode known as the cyclic alternating pattern (CAP). CAP is the expression of sleep instability between sleep maintaining processes (phase A1) and stronger arousal processes (phases A2 and A3). This study aimed to investigate the role of sleep instability on RMMA/SB occurrence by analysing CAP and electroencephalographic (EEG) activities. The analysis was performed on the sleep recordings of 8 SB subjects and 8 controls who received sensory stimulations during sleep. Baseline and experimental nights were compared for sleep variables, CAP, and EEG spectral analyses using repeated measure ANOVAs. Overall, no differences in sleep variables and EEG spectra were found between SB subjects and controls. However, SB subjects had higher sleep instability (more phase A3) than controls (P= 0·05). The frequency of phase A3 was higher in the pre-REM sleep periods (P < 0·001), where peaks in RMMA/SB activity were also observed (P = 0·05). When sleep instability was experimentally increased by sensory stimuli, both groups showed an enhancement in EEG theta and alpha power (P = 0·04 and 0·02, respectively) and significant increases in sleep arousal and all CAP variables. No change in RMMA/SB index was found within either groups (RMMA/SB occurred in all SB subjects and only one control during the experimental night). These findings suggest that CAP phase A3 may act as a permissive window rather than a generator of RMMA/SB activity in predisposed individuals., (© 2011 Blackwell Publishing Ltd.)