Inter-rater agreement for visual discrimination of phasic and tonic electromyographic activity in sleep.

Study Objectives: The objective of this study was to determine the confidence of expert raters in discriminating phasic and tonic electromyographic (EMG) activity. We undertook this study because we suspected that even expert scorers may disagree on whether a given EMG segment contained phasic activity, tonic activity, or both. Methods: Six individuals holding either Fellowship status in the American Academy of Sleep Medicine or Board Certification in Sleep Medicine with at least 5 years experience in interpreting polysomnography visually examined 60 segments containing EMG activity. Raters determined their relative confidence that each segment contained phasic and tonic activity by noting whether they were highly certain or somewhat certain that the segment contained such activity or somewhat certain or highly certain that each segment did not contain such activity. Every segment was rated by every rater twice, once for phasic and once for tonic activity. Results: Substantial differences among raters existed in certainty regarding presence/absence of both phasic and tonic activity, although raters agreed on segments far above chance. Consensus was higher on certainty regarding presence of phasic, relative to tonic, activity. Conclusions: These findings indicate the limitations of visual analyses for discriminating abnormal muscle activity during sleep. Conversely, when expert judgments are combined with digitized measurements of EMG activity in sleep (e.g. REM atonia index), some allowance must be made for the unique contribution of visual analyses to such judgments, most notably for short duration EMG signals. These results may have relevance for polysomnographic interpretation in suspected synucleinopathies.

The Effects of an Afternoon Nap on Episodic Memory in Young and Older Adults.

Study Objectives: In young adults, napping is hypothesized to benefit episodic memory retention (eg, via consolidation). Whether this relationship is present in older adults has not been adequately tested but is an important question because older adults display marked changes in sleep and memory. Design: Between-subjects design. Setting: Sleep laboratory at Emory University School of Medicine. Participants: Fifty healthy young adults (18-29) and 45 community-dwelling older adults (58-83). Intervention: Participants were randomly assigned to a 90-minute nap opportunity or an equal interval of quiet wakefulness. Measurements and Results: Participants underwent an item-wise directed forgetting learning procedure in which they studied words that were individually followed by the instruction to "remember" or "forget." Following a 90-minute retention interval filled with quiet wakefulness or a nap opportunity, they were asked to free recall and recognize those words. Young adults retained significantly more words following a nap interval than a quiet wakefulness interval on both free recall and recognition tests. There was modest evidence for greater nap-related retention of "remember" items relative to "forget" items for free recall but not recognition. Older adults' memory retention did not differ across nap and quiet wakefulness conditions, although they demonstrated greater fragmentation, lower N3, and lower rapid eye movement duration than the young adults. Conclusions: In young adults, an afternoon nap benefits episodic memory retention, but such benefits decrease with advancing age.

Sleep correlates of trait executive function and memory in Parkinson's disease.

BACKGROUND: Sleep disturbance and cognitive impairment are correlated in non-diseased populations, but their association in Parkinson's disease (PD) is uncertain. Prior studies examining measures of cognition in relation to sleep have used either self-report or actigraphically measured sleep and have produced conflicting findings. OBJECTIVE: In this descriptive study, we correlated measurements of sleep in PD patients derived from the gold-standard measurement, in-lab polysomnography, with an extensive battery of cognitive measures. We hypothesized that poorer sleep would be related to relatively more impaired cognition. METHODS: Idiopathic PD patients (n = 34) completed a cognitive battery encompassing three broad domains (executive function, immediate memory and delayed memory), and underwent PSG for two nights. Scores for each domain from individual cognitive measures were converted to z-scores and then averaged to produce a composite score. We used second night PSG data and quantified measures of sleep architecture, sleep continuity, sleep apnea and nocturnal movement (periodic leg movements, PLMS). RESULTS: Lower executive function was associated with higher PLMS after controlling for chronological age, mini-mental state examination scores, and UPDRS motor subscale scores. These results were independent from psychomotor speed. There was a marginally significant positive correlation between the proportion of time spent in REM and immediate recall ability. Measures of sleep continuity and sleep apnea were unrelated to cognition in these patients. CONCLUSIONS: PLMS, known to be a frequent feature of PSG-measured sleep in PD, may be an important correlate of impaired executive function in PD. Whether treating this disorder of sleep results in improvement in cognition remains to be determined.

Wavelet analysis for detection of phasic electromyographic activity in sleep: influence of mother wavelet and dimensionality reduction.

Phasic electromyographic (EMG) activity during sleep is characterized by brief muscle twitches (duration 100-500ms, amplitude four times background activity). High rates of such activity may have clinical relevance. This paper presents wavelet (WT) analyses to detect phasic EMG, examining both Symlet and Daubechies approaches. Feature extraction included 1s epoch processing with 24 WT-based features and dimensionality reduction involved comparing two techniques: principal component analysis and a feature/variable selection algorithm. Classification was conducted using a linear classifier. Valid automated detection was obtained in comparison to expert human judgment with high (>90%) classification performance for 11/12 datasets.

Modulation of vigilance in the primary hypersomnias by endogenous enhancement of GABAA receptors.

The biology underlying excessive daytime sleepiness (hypersomnolence) is incompletely understood. After excluding known causes of sleepiness in 32 hypersomnolent patients, we showed that, in the presence of 10 µM γ-aminobutyric acid (GABA), cerebrospinal fluid (CSF) from these subjects stimulated GABA(A) receptor function in vitro by 84.0 ± 40.7% (SD) relative to the 35.8 ± 7.5% (SD) stimulation obtained with CSF from control subjects (Student's t test, t = 6.47, P < 0.0001); CSF alone had no effect on GABA(A) signaling. The bioactive CSF component had a mass of 500 to 3000 daltons and was neutralized by trypsin. Enhancement was greater for α2 subunit- versus α1 subunit-containing GABA(A) receptors and negligible for α4 subunit-containing ones. CSF samples from hypersomnolent patients also modestly enhanced benzodiazepine (BZD)-insensitive GABA(A) receptors and did not competitively displace BZDs from human brain tissue. Flumazenil--a drug that is generally believed to antagonize the sedative-hypnotic actions of BZDs only at the classical BZD-binding domain in GABA(A) receptors and to lack intrinsic activity--nevertheless reversed enhancement of GABA(A) signaling by hypersomnolent CSF in vitro. Furthermore, flumazenil normalized vigilance in seven hypersomnolent patients. We conclude that a naturally occurring substance in CSF augments inhibitory GABA signaling, thus revealing a new pathophysiology associated with excessive daytime sleepiness.

COMPUTER DETECTION APPROACHES FOR IDENTIFICATION OF PHASIC ELECTROMYOGRAPHIC (EMG) ACTIVITY DURING HUMAN SLEEP.

BACKGROUND: Examination of spontaneously occurring phasic muscle activity from the human polysomnogram may have considerable clinical importance for patient care, yet most attempts to quantify the detection of such activity have relied upon laborious and intensive visual analyses. We describe in this study innovative signal processing approaches to this issue. METHODS: We examined multiple features of surface electromyographic signals based on 16,200 individual 1-second intervals of low impedance sleep recordings. We validated which of those features most closely mirrored the careful judgments of trained human observers in making discriminations of the presence of short-lived (100-500 msec) phasic activity, and also examined which features provided maximal differences across 1-second intervals and which features were least susceptible to residual levels of amplifier noise. RESULTS: Our data suggested particularly promising and novel features (e.g., Non-linear energy, 95(th) percentile of Spectral Edge Frequency) for developing automated systems for quantifying muscle activity during human sleep. CONCLUSIONS: The EMG signals recorded from surface electrodes during sleep can be processed with techniques that reflect the visually based analyses of the human scorer but also offer potential for discerning far more subtle effects, Future studies will explore both the clinical utility of these techniques and their relative susceptibility to and/or independence from signal artifacts.

Phasic Electromyographic Metric detection based on wavelet analysis.

The Phasic Electromyographic Metric (PEM) has been recently introduced as a sensitive indicator to differentiate Parkinson's Disease (PD) patients from controls, non-PD patients with a history of Rapid Eye Movement Disorder (RBD) from controls, and PD patients with early and late stage disease. However, PEM assessment through visual inspection is a cumbersome and time consuming process. Therefore, a reliable automated approach is required so as to increase the utilization of PEM as a reliable and efficient clinical tool to track PD progression. In this study an automated method for the detection of PEM is presented, based on the use of signal analysis and pattern recognition techniques. The results are promising indicating that an automatic PEM identification procedure is feasible.

Automated polysomnogram artifact compensation using the generalized singular value decomposition algorithm.

Manual/visual polysomnogram (psg) analysis is a standard and commonly implemented procedure utilized in the diagnosis and treatment of sleep related human pathologies. Current technological trends in psg analysis focus upon translating manual psg analysis into automated/computerized approaches. A necessary first step in establishing efficient automated human sleep analysis systems is the development of reliable pre-processing tools to discriminate between outlier/artifact instances and data of interest. This paper investigates the application of an automated approach, using the generalized singular value decomposition algorithm, to compensate for specific psg artifacts.