Periodic arousals or periodic limb movements during sleep?

A deep learning-based algorithm for detection of cortical arousal during sleep

STUDY OBJECTIVES

The frequency of cortical arousals is an indicator of sleep quality. Additionally, cortical arousals are used to identify hypopneic events. However, it is inconvenient to record electroencephalogram (EEG) data during home sleep testing. Fortunately, most cortical arousal events are associated with autonomic nervous system activity that could be observed on an electrocardiography (ECG) signal. ECG data have lower noise and are easier to record at home than EEG. In this study, we developed a deep learning-based cortical arousal detection algorithm that uses a single-lead ECG to detect arousal during sleep.

METHODS

This study included 1,547 polysomnography records that met study inclusion criteria and were selected from the Multi-Ethnic Study of Atherosclerosis database. We developed an end-to-end deep learning model consisting of convolutional neural networks and recurrent neural networks which: (1) accepted varying length physiological data; (2) directly extracted features from the raw ECG signal; (3) captured long-range dependencies in the physiological data; and (4) produced arousal probability in 1-s resolution.

RESULTS

We evaluated the model on a test set (n = 311). The model achieved a gross area under precision-recall curve score of 0.62 and a gross area under receiver operating characteristic curve score of 0.93.

CONCLUSION

This study demonstrated the end-to-end deep learning approach with a single-lead ECG has the potential to be used to accurately detect arousals in home sleep tests.

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.

Children show individual night-to-night variability of periodic limb movements in sleep

STUDY OBJECTIVE

Several studies have documented the occurrence of significant night-to-night variability of periodic limb movements in sleep (PLMS) in adults.The aim of this study was to investigate the night-tonight variability of PLMS in children.

DESIGN AND MEASUREMENTS

Two to 4 nights of polysomnography were performed as part of a multisite, placebo-controlled study investigating the effects of carbidopa/levodopa on attention-deficit/hyperactivity disorder in children who were not taking other medications that impacted the central nervous system. Baseline polysomnograms from all children and endpoint polysomnograms from children who were randomly assigned to a placebo group were scored using International Restless Legs Syndrome Study Group criteria for PLMS. PLMS indexes from 101 sleep studies of 36 children, aged 7 to 12 years, were compared.

INTERVENTIONS

N/A.

RESULTS

For all 36 children as a group, PLMS index on Night 1 was predictive of PLMS index on Night 2 (odds ratio 7.0, 95% confidence interval 1.4-38.4), suggesting that overall diagnostic classification (PLMS index above or below 5/h) was accurate. In addition, for the 15 children with 5 or more PLMS per hour on either night, there was no significant group difference on Night 1 versus Night 2 for mean PLMS index (10.6 vs 8.5/h, P = 0.92) or chance of having 5 or more PLMS per hour, indicating no first-night effect. When looking at individual data, however, 9 of these 15 children (60%) had PLMS indexes over and under the 5 per hour cutoff on these 2 nights. Of these 15, 10 had clinical diagnoses of restless legs syndrome and 5 of periodic limb movement disorder (PLMD). The PLMS indexes of all children who were medication free for a third and fourth night (n = 7) or just a third night (n = 2) and had not shown a PLMS index of 5 or greater on either of the first 2 nights remained under this threshold.

CONCLUSIONS

In this sample of children, considerable individual night-to-night variability of PLMS indexes was observed. This finding has important clinical relevance for the diagnosis of restless legs syndrome and PLMD and may have an impact on future studies that correlate individual PLMS severity with frequently associated symptoms, such as negative affect, fatigue, and inattention. Our data, however, also suggest that individual PLMS variability is random and not likely to skew the group-level analysis of treatment outcome studies.

Restless legs syndrome and periodic limb movement disorder in children and adolescents

Restless Legs Syndrome (RLS) has been recognized as a common and treatable neurologic disorder in adults for some time, but the occurrence of RLS in children and adolescents has seen relatively delayed acceptance. A large, population-based study has recently reported a 1.9% and 2% prevalence of RLS in children and adolescents, respectively. RLS in children is closely associated with periodic limb movement disorder (PLMD), and symptoms of both may range from mild to severe. An early, accurate diagnosis of RLS or PLMD provides substantial benefits to an individual's quality of life, especially in cases of poor-sleep related intellectual or emotional dysfunction. Treatment plans should use emerging knowledge of how RLS and PLMD affect children and adolescents to correctly identify these disorders and aim to reduce or eliminate symptoms. Best-fitting therapy will consider severity of symptoms, comorbid conditions, and phenotypic variables. Promising progress has been made in understanding the genetic components of RLS as well as the role of iron deficiency in exacerbating symptoms. A review of current research on RLS and PLMD in children and adolescents is presented.

CAP, epilepsy and motor events during sleep: the unifying role of arousal

Arousal systems play a topical neurophysiologic role in protecting and tailoring sleep duration and depth. When they appear in NREM sleep, arousal responses are not limited to a single EEG pattern but are part of a continuous spectrum of EEG modifications ranging from high-voltage slow rhythms to low amplitude fast activities. The hierarchic features of arousal responses are reflected in the phase A subtypes of CAP (cyclic alternating pattern) including both slow arousals (dominated by the <1Hz oscillation) and fast arousals (ASDA arousals). CAP is an infraslow oscillation with a periodicity of 20-40s that participates in the dynamic organization of sleep and in the activation of motor events. Physiologic, paraphysiologic and pathologic motor activities during NREM sleep are always associated with a stereotyped arousal pattern characterized by an initial increase in EEG delta power and heart rate, followed by a progressive activation of faster EEG frequencies. These findings suggest that motor patterns are already written in the brain codes (central pattern generators) embraced with an automatic sequence of EEG-vegetative events, but require a certain degree of activation (arousal) to become visibly apparent. Arousal can appear either spontaneously or be elicited by internal (epileptic burst) or external (noise, respiratory disturbance) stimuli. Whether the outcome is a physiologic movement, a muscle jerk or a major epileptic attack will depend on a number of ongoing factors (sleep stage, delta power, neuro-motor network) but all events share the common trait of arousal-activated phenomena.

Influence of sleep stage and wakefulness on spectral EEG activity and heart rate variations around periodic leg movements

OBJECTIVE

Typical changes in spectral electroencephalographic (EEG) activity and heart rate (HR) have been described in periodic leg movements (PLM) associated with or without microarousals (MA). We aimed to determine the effects of sleep stage and wakefulness on these responses to ascertain whether a common pattern of EEG and HR activation takes place.

METHODS

The time course of EEG spectral activity and HR variability associated with PLM was analysed in 13 patients during light NREM sleep, rapid-eye-movement (REM) sleep and wakefulness. The same analysis was also conducted for PLM without MA occurring in stage 2.

RESULTS

A significant EEG and electrocardiogram (ECG) activation was found associated with PLM during sleep, but not during wakefulness. While in light NREM sleep, an increase in delta and theta bands was detected before the PLM onset, in REM sleep the EEG activation occurred simultaneously with the PLM onset. Moreover, during stage 1 and REM sleep, alpha and fast frequencies tended to remain sustained after the PLM onset. In contrast, during wakefulness, a small and not significant increase in cerebral activity was present, starting at the PLM onset and persisting in the post-movement period. A typical pattern of cardiac response was present during NREM and REM sleep, the autonomic activation being lesser and prolonged during wakefulness.

CONCLUSIONS

We conclude that the EEG and HR responses to PLM differ between sleep stages and wakefulness with lesser changes found during wakefulness.

SIGNIFICANCE

These findings suggest that specific sleep state-dependent mechanisms may underlie the occurrence of PLM.

The clinical neurophysiology of the restless legs syndrome and periodic limb movements. Part I: diagnosis, assessment, and characterization

OBJECTIVE

The restless legs syndrome is a common sensorimotor disorder impacting on sleep which has been known for centuries, but only recently become recognized as a significant clinical and pathophysiological problem. The definition of RLS has evolved until certain key clinical features have been defined as diagnostic, while others are strongly associated: the urge to move is seen as primary. Epidemiology suggests ethnic variation with highest frequency in populations of European origin; family and genetic studies support a genetic basis to many idiopathic cases while links to secondary disorders usually involving low iron stores are also known. Abnormalities of brain iron transport and consequent dysfunction of the dopamine system are suspected sources of the disorder.

METHODS

The literature was searched for all references relating clinical neurophysiologic investigations to the diagnosis, assessment, and characterization of RLS.

RESULTS

RLS is defined clinically and diagnosed by medical history while its frequent concomitant, periodic limb movements (PLM), must be diagnosed by polysomnography or movement recording. Severity of RLS is generally assessed by subjective measures, but sleep recording and measurement of PLM frequency and association with sleep disruption are also used to measure severity. A provocative test, the suggested immobilization test, can also be used with both subjective and movement recording. RLS and PLM in RLS are both associated with the circadian cycle and are maximal early in the sleep period. PLM appear to be associated both with unstable EEG phases involving the cyclic alternating pattern and cyclical autonomic changes whose initiation may precede the muscle activity.

CONCLUSIONS

While RLS remains a subjective disorder, neurophysiologic measures have been important, especially for assessment. Ambulatory methodologies may offer the most accurate and economical means of assessing motor activity as a key marker of RLS and of accurately measuring PLM from night to night. As the pathophysiology of RLS is better understood, more focused techniques may be developed to measure its presence and severity in individual patients.

The nature of arousal in sleep

The role of arousals in sleep is gaining interest among both basic researchers and clinicians. In the last 20 years increasing evidence shows that arousals are deeply involved in the pathophysiology of sleep disorders. The nature of arousals in sleep is still a matter of debate. According to the conceptual framework of the American Sleep Disorders Association criteria, arousals are a marker of sleep disruption representing a detrimental and harmful feature for sleep. In contrast, our view indicates arousals as elements weaved into the texture of sleep taking part in the regulation of the sleep process. In addition, the concept of micro-arousal (MA) has been extended, incorporating, besides the classical low-voltage fast-rhythm electroencephalographic (EEG) arousals, high-amplitude EEG bursts, be they like delta-like or K-complexes, which reflects a special kind of arousal process, mobilizing parallely antiarousal swings. In physiologic conditions, the slow and fast MA are not randomly scattered but appear structurally distributed within sleep representing state-specific arousal responses. MA preceded by slow waves occurs more frequently across the descending part of sleep cycles and in the first cycles, while the traditional fast type of arousals across the ascending slope of cycles prevails during the last third of sleep. The uniform arousal characteristics of these two types of MAs is supported by the finding that different MAs are associated with an increasing magnitude of vegetative activation ranging hierarchically from the weaker slow EEG types (coupled with mild autonomic activation) to the stronger rapid EEG types (coupled with a vigorous autonomic activation). Finally, it has been ascertained that MA are not isolated events but are basically endowed with a periodic nature expressed in non-rapid eye movement (NREM) sleep by the cyclic alternating pattern (CAP). Understanding the role of arousals and CAP and the relationship between physiologic and pathologic MA can shed light on the adaptive properties of the sleeping brain and provide insight into the pathomechanisms of sleep disturbances. Functional significance of arousal in sleep, and particularly in NREM sleep, is to ensure the reversibility of sleep, without which it would be identical to coma. Arousals may connect the sleeper with the surrounding world maintaining the selection of relevant incoming information and adapting the organism to the dangers and demands of the outer world. In this dynamic perspective, ongoing phasic events carry on the one hand arousal influences and on the other elements of information processing. The other function of arousals is tailoring the more or less stereotyped endogenously determined sleep process driven by chemical influences according to internal and external demands. In this perspective, arousals shape the individual course of night sleep as a variation of the sleep program.