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.

Causality of cortical and cardiovascular activity during cyclic alternating pattern in non-rapid eye movement sleep

The dynamic interplay between central and autonomic nervous system activities plays a pivotal role in orchestrating sleep. Macrostructural changes such as sleep-stage transitions or phasic, brief cortical events elicit fluctuations in neural outflow to the cardiovascular system, but the causal relationships between cortical and cardiovascular activities underpinning the microstructure of sleep are largely unknown. Here, we investigate cortical–cardiovascular interactions during the cyclic alternating pattern (CAP) of non-rapid eye movement sleep in a diverse set of overnight polysomnograms. We determine the Granger causality in both 507 CAP and 507 matched non-CAP sequences to assess the causal relationships between electroencephalography (EEG) frequency bands and respiratory and cardiovascular variables (heart period, respiratory period, pulse arrival time and pulse wave amplitude) during CAP. We observe a significantly stronger influence of delta activity on vascular variables during CAP sequences where slow, low-amplitude EEG activation phases (A1) dominate than during non-CAP sequences. We also show that rapid, high-amplitude EEG activation phases (A3) provoke a more pronounced change in autonomic activity than A1 and A2 phases. Our analysis provides the first evidence on the causal interplay between cortical and cardiovascular activities during CAP. Granger causality analysis may also be useful for probing the level of decoupling in sleep disorders. This article is part of the theme issue ‘Advanced computation in cardiovascular physiology: new challenges and opportunities’.

P166 Measures of overnight sleep stability in patients with hypersomnolence

Introduction Hypersomnolence causes significant impairment of daytime functioning. The multiple sleep latency test (MSLT) measures objective hypersomnolence (OH). Patients with hypersomnolence with a normal MSLT are said to have subjective hypersomnolence (SH). The mechanisms of hypersomnolence in such patients is uncertain. This study describes differences in measures of sleep stability derived from the overnight polysomnography (PSG) in patients with OH and SH. Methods A retrospective analysis of 100 patients undergoing PSG/MSLT for investigation of hypersomnolence was performed. Patients were classified as OH (MSLT≤8 min) or SH (MSLT>8min). Sleep stage distribution and PSG-derived markers of sleep stability including cardiopulmonary coupling (CPC), cyclic alternating pattern (CAP) and sleep stage shifts were compared between the two groups. Results When compared to OH patients (N=50), SH patients (N=50) had significantly more sleep stage shifts, more shifts to stage N1 and longer PSG sleep latency. Small but significantly lower sleep efficiency, higher stage N1 and N3 proportions were also observed in SH patients. OH patients had a small but significantly higher CAP rate and CAP index compared to SH patients. There were no significant differences in CPC metrics between the two groups. Conclusion Several PSG-derived markers of sleep stability indicated that patients with SH experienced more unstable sleep than OH patients. This may provide insight into the underlying pathophysiological mechanisms which differentiate these patient groups and may serve as a future therapeutic target.

Automated Characterization of Cyclic Alternating Pattern Using Wavelet-Based Features and Ensemble Learning Techniques with EEG Signals

Sleep is highly essential for maintaining metabolism of the body and mental balance for increased productivity and concentration. Often, sleep is analyzed using macrostructure sleep stages which alone cannot provide information about the functional structure and stability of sleep. The cyclic alternating pattern (CAP) is a physiological recurring electroencephalogram (EEG) activity occurring in the brain during sleep and captures microstructure of the sleep and can be used to identify sleep instability. The CAP can also be associated with various sleep-related pathologies, and can be useful in identifying various sleep disorders. Conventionally, sleep is analyzed using polysomnogram (PSG) in various sleep laboratories by trained physicians and medical practitioners. However, PSG-based manual sleep analysis by trained medical practitioners is onerous, tedious and unfavourable for patients. Hence, a computerized, simple and patient convenient system is highly desirable for monitoring and analysis of sleep. In this study, we have proposed a system for automated identification of CAP phase-A and phase-B. To accomplish the task, we have utilized the openly accessible CAP sleep database. The study is performed using two single-channel EEG modalities and their combination. The model is developed using EEG signals of healthy subjects as well as patients suffering from six different sleep disorders namely nocturnal frontal lobe epilepsy (NFLE), sleep-disordered breathing (SDB), narcolepsy, periodic leg movement disorder (PLM), insomnia and rapid eye movement behavior disorder (RBD) subjects. An optimal orthogonal wavelet filter bank is used to perform the wavelet decomposition and subsequently, entropy and Hjorth parameters are extracted from the decomposed coefficients. The extracted features have been applied to different machine learning algorithms. The best performance is obtained using ensemble of bagged tress (EBagT) classifier. The proposed method has obtained the average classification accuracy of 84%, 83%, 81%, 78%, 77%, 76% and 72% for NFLE, healthy, SDB, narcolepsy, PLM, insomnia and RBD subjects, respectively in discriminating phases A and B using a balanced database. Our developed model yielded an average accuracy of 78% when all 77 subjects including healthy and sleep disordered patients are considered. Our proposed system can assist the sleep specialists in an automated and efficient analysis of sleep using sleep microstructure.

Obstructive Sleep Apnea Syndrome and Features of the Neurophysiological Sleep Pattern

The high prevalence of obstructive sleep apnea syndrome (OSA) causes a steady interest in this pathology. In recent years, one of the urgent problems in modern somnology is the assessment of the main mechanisms of neuronal dysfunction during the day and at night in OSA, the ideas about which, to a large extent, remain contradictory and not fully understood. One of the modern methods for assessing neuronal dysfunction during sleep is the study of the sleep microstructure, and for its assessment, the method of analysis of cyclic alternating pattern (CAP), an EEG marker of unstable sleep, is used. The cyclic alternating pattern is found both in the sleep of adults and children with various sleep disorders and, in particular, with OSAS, therefore, it is a sensitive tool for studying sleep disorders throughout life. With the elimination of night hypoxia against the background of CPAP therapy, the sleep microstructure is restored, the spectral characteristics of the EEG change, and a decrease in the number of EEG arousals after treatment leads to the restoration of daytime functioning. Understanding the role of short-term EEG activations of the brain during sleep can provide significant data on sleep functions in health and disease. Despite the improving diagnosis of sleep disorders using machine algorithms, assessing the relationship of structures and functions of the brain during sleep, neurophysiological data are not entirely clear, which requires further research. In this review, we tried to analyze the results of the main studies of the neurophysiological sleep pattern in OSA against the background of respiratory support during sleep.

391 Upper Airway Resistance Syndrome is associated with high Cyclic Alternating Pattern

Introduction Upper Airway Resistance Syndrome (UARS) is suspected in individuals with excessive daytime sleepiness, fatigue, and sleep fragmentation associated with increased respiratory effort. UARS can negatively impact daytime function. Conventional polysomnography parameters do not demonstrate significant abnormalities in UARS patients but increase in RERAs and arousal index. Cyclic alternating pattern (CAP) is a periodic electroencephalogram activity of non-REM sleep that expresses a condition of sleep instability. The objective of the study was to compare CAP components between UARS patients and health individuals. Methods Fifteen subjects with UARS and 15 age- and sex- matched controls had their sleep study blinded analyzed. UARS criteria were the presence of sleepiness (Epworth Sleepiness Scale – ESS - ≥ 10) and/or fatigue (Modified Fatigue Impact Scale ≥ 38) associated with an apnea/hypopnea index (AHI) ≤ 5 and a respiratory disturbance index (RDI) > 5 events/hour of sleep, and/or flow limitation in more than 30% of total sleep time. Control group criteria were AHI < 5 events/hour, RDI ≤ 5 events/hour and < 30% of TST with flow limitation and ESS < 10, without sleep, clinical, neurological, or psychiatric disorder. CAP electroencephalogram of both groups was analyzed. Results We found higher CAP rate (p = 0.05) and CAP index in N1 stage (p < 0.001) and in N3 stage (p < 0.001) in UARS patients compared to control group. Considering only CAP phase A1 analysis, UARS patients presented higher CAP rate (p = 0.05) and CAP index in N1 stage (p < 0.001) as well as CAP index in N3 stage (p < 0.001) compared to control group. Considering only CAP phase A2 analysis, UARS patients also presented higher number of CAP in N1 stage (p = 0.05). There was no significant difference for CAP phase A3 between groups. Conclusion Although UARS is associated with high arousal index, we found increase in CAP phase A1 and A2, which do not include necessarily AASM arousals, suggesting not only sleep fragmentation but also sleep instability. Support (if any) Associação Fundo de Incentivo à Pesquisa (AFIP) and Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP).