Andrea Grassi

Paradoxical insomnia: The role of CAP and arousals in sleep misperception

OBJECTIVE The relationship between CAP parameters and subjective time perception during sleep in primary insomnia was investigated. PATIENTS AND METHODS Data collected from all-night PSG recordings of 20 patients with a diagnosis of paradoxical insomnia (misperceptors) were compared with those of 20 normal gender- and age-matched subjects (controls). Besides sleep staging, scoring measures included CAP parameters and EEG arousals. RESULTS Patients and controls presented non-significant differences in the amounts of objective sleep time (464 min vs. 447 min) and objective sleep latency (9 min vs. 8 min). Compared to controls, misperceptors reported a significantly shorter time of perceived sleep (285 min vs. 461 min) and a significantly longer duration of perceived sleep latency (51 min vs. 22 min). In spite of the 11 objective awakenings, misperceptors reported only 4 subjective awakenings, while controls described 2 of the 5 objective awakenings. Arousal index (31.7/h vs. 18.6/h) and total CAP rate (58.1% vs. 35.5%) were significantly higher in misperceptors. In the sleep period between objective and subjective sleep onset, CAP rate was 64.4% in misperceptors and 45.1% in controls (p<0.002). Insomniacs showed significantly higher amounts of CAP rate in stage 1 (62.7% vs. 37.5%) and in stage 2 (53.3% vs. 33.1%), but not in slow wave sleep. CAP phase A2 subtypes were significantly increased in misperceptors (31% vs. 24%). CONCLUSIONS The study points out the topical role of enhanced activation and arousal instability not only in the first part of the night (mismatch between objective and subjective sleep onset) but also in the misperception of consecutive objective awakenings which are subjectively grouped together as a single prolonged event.

Distinctive polysomnographic traits in nocturnal frontal lobe epilepsy.

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).

Assessment of the EEG complexity during activations from sleep

The present study quantitatively analyzes the EEG characteristics during activations (Act) that occur during NREM sleep, and constitute elements of sleep microstructure (i.e. the Cyclic Alternating Pattern). The fractal dimension (FD) and the sample entropy (SampEn) measures were used to study the different sleep stages and the Act that build up the sleep structure. Polysomnographic recordings from 10 good sleepers were analyzed. The complexity indexes of the Act were compared with the non-activation (NAct) periods during non-REM sleep. In addition, complexity measures among the different Act subtypes (A1, A2 and A3) were analyzed. A3 presented a quite similar complexity independently of the sleep stage, while A1 and A2 showed higher complexity in light sleep than during deep sleep. The current results suggest that Act present a hierarchic complexity between subtypes A3 (higher), A2 (intermediate) and A1 (lower) in all sleep stages.

Sleep and respiratory sleep disorders in idiopathic pulmonary fibrosis.

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.

Effects of antiepileptic treatment on sleep and seizures in nocturnal frontal lobe epilepsy.

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.

Cyclic Alternating Patterns in Normal Sleep and Insomnia: Structure and Content Differences

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.

Efficient automatic classifiers for the detection of A phases of the cyclic alternating pattern in sleep

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.

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.

Acute shift of a case of moderate obstructive sleep apnea syndrome towards one of severe central sleep apnea syndrome after an ischemic stroke.

Article history: Received 21 December 2011 Received in revised form 12 January 2012 Accepted 27 January 2012 Available online 22 March 2012

Cyclic alternating pattern in polysomnography: what is it and what does it mean?

Purpose of review The relevance of sleep instability is poorly appreciated among the metrics of sleep physiology. The cyclic alternating pattern (CAP) is a periodic electroencephalogram activity of non-REM sleep, characterized by sequences of transient electrocortical events that are distinct from the tonic background and recur at up to 1-min intervals. In the dynamic organization of sleep, CAP expresses a condition of instability that reflects the brains effort in preserving and regulating the physiological structure of sleep. Recent findings CAP quantification is a topical feature in the evaluation of sleep quality. In addition to duration, depth, and continuity, sleep restorative properties depend on the brains capacity to determine the periods of sustained stable sleep. This issue is not confined only to the electroencephalogram activities but reverberates upon the ongoing autonomic and behavioral functions, which are mutually entrained in a synchronized oscillation. As a master clock involved in the dynamic organization of sleep, CAP plays a crucial role in numerous sleep disorders and is powerfully influenced by medication and appropriate treatment. Summary This article reviews the scoring, significance, and clinical applications of CAP.