Miki Nikolic

Sleep spindle alterations in patients with Parkinson’s disease

The aim of this study was to identify changes of sleep spindles (SS) in the EEG of patients with Parkinsons disease (PD). Five sleep experts manually identified SS at a central scalp location (C3-A2) in 15 PD and 15 age- and sex-matched control subjects. Each SS was given a confidence score, and by using a group consensus rule, 901 SS were identified and characterized by their (1) duration, (2) oscillation frequency, (3) maximum peak-to-peak amplitude, (4) percent-to-peak amplitude, and (5) density. Between-group comparisons were made for all SS characteristics computed, and significant changes for PD patients vs. control subjects were found for duration, oscillation frequency, maximum peak-to-peak amplitude and density. Specifically, SS density was lower, duration was longer, oscillation frequency slower and maximum peak-to-peak amplitude higher in patients vs. controls. We also computed inter-expert reliability in SS scoring and found a significantly lower reliability in scoring definite SS in patients when compared to controls. How neurodegeneration in PD could influence SS characteristics is discussed. We also note that the SS morphological changes observed here may affect automatic detection of SS in patients with PD or other neurodegenerative disorders (NDDs).

EMGTools, an Adaptive and Versatile Tool for Detailed EMG Analysis

We have developed an electromyography (EMG) decomposition system called EMGTools that can extract the constituent MUAPs and firing patterns (FPs) for quantitative analysis from the EMG signal recorded at slight effort for clinical evaluation. The aim was to implement a robust system able to handle the challenges and variations in clinically recorded signals. The system extracts MUAPs recorded by concentric needle electrodes and resolves superimposed MUAPs to produce FPs. Thus, critical fixed thresholds/parameters are avoided and replaced with adaptive solutions. The decomposition algorithm consists of three stages: segmentation, clustering, and resolution of compound segments. The results are validated using three different methods, comparing mean MUAP duration with previous methods, comparing dual channel recordings, and assessing the residual signal after decomposition. The advantages and limitations of the system are discussed.

Automatic SLEEP staging: From young aduslts to elderly patients using multi-class support vector machine

Aging is a process that is inevitable, and makes our body vulnerable to age-related diseases. Age is the most consistent factor affecting the sleep structure. Therefore, new automatic sleep staging methods, to be used in both of young and elderly patients, are needed. This study proposes an automatic sleep stage detector, which can separate wakefulness, rapid-eye-movement (REM) sleep and non-REM (NREM) sleep using only EEG and EOG. Most sleep events, which define the sleep stages, are reduced with age. This is addressed by focusing on the amplitude of the clinical EEG bands, and not the affected sleep events. The age-related influences are then reduced by robust subject-specific scaling. The classification of the three sleep stages are achieved by a multi-class support vector machine using the one-versus-rest scheme. It was possible to obtain a high classification accuracy of 0.91. Validation of the sleep stage detector in other sleep disorders, such as apnea and narcolepsy, should be considered in future work.

Rapid eye movement sleep behavior disorder as an outlier detection problem.

Objective: Idiopathic rapid eye movement (REM) sleep behavior disorder is a strong early marker of Parkinson’s disease and is characterized by REM sleep without atonia and/or dream enactment. Because these measures are subject to individual interpretation, there is consequently need for quantitative methods to establish objective criteria. This study proposes a semiautomatic algorithm for the early detection of Parkinson’s disease. This is achieved by distinguishing between normal REM sleep and REM sleep without atonia by considering muscle activity as an outlier detection problem. Methods: Sixteen healthy control subjects, 16 subjects with idiopathic REM sleep behavior disorder, and 16 subjects with periodic limb movement disorder were enrolled. Different combinations of five surface electromyographic channels, including the EOG, were tested. A muscle activity score was automatically computed from manual scored REM sleep. This was accomplished by the use of subject-specific features combined with an outlier detector (one-class support vector machine classifier). Results: It was possible to correctly separate idiopathic REM sleep behavior disorder subjects from healthy control subjects and periodic limb movement subjects with an average validation area under the receiver operating characteristic curve of 0.993 when combining the anterior tibialis with submentalis. Additionally, it was possible to separate all subjects correctly when the final algorithm was tested on 12 unseen subjects. Conclusions: Detection of idiopathic REM sleep behavior disorder can be regarded as an outlier problem. Additionally, the EOG channels can be used to detect REM sleep without atonia and is discriminative better than the traditional submentalis. Furthermore, based on data and methodology, arousals and periodic limb movements did only have a minor influence on the quantification of the muscle activity. Analysis of muscle activity during nonrapid eye movement sleep may improve the separation even further.

Increased Motor Activity During REM Sleep Is Linked with Dopamine Function in Idiopathic REM Sleep Behavior Disorder and Parkinson Disease.

STUDY OBJECTIVES Rapid eye movement (REM) sleep behavior disorder (RBD) is a parasomnia characterized by impaired motor inhibition during REM sleep, and dream-enacting behavior. RBD is especially associated with α-synucleinopathies, such as Parkinson disease (PD). Follow-up studies have shown that patients with idiopathic RBD (iRBD) have an increased risk of developing an α-synucleinopathy in later life. Although abundant studies have shown that degeneration of the nigrostriatal dopaminergic system is associated with daytime motor function in Parkinson disease, only few studies have investigated the relation between this system and electromyographic (EMG) activity during sleep. The objective of this study was to investigate the relationship between the nigrostriatal dopamine system and muscle activity during sleep in iRBD and PD. METHODS 10 iRBD patients, 10 PD patients with PD, 10 PD patients without RBD, and 10 healthy controls were included and assessed with (123)I-N-omega-fluoropropyl-2-beta-carboxymethoxy-3beta-(4-iodophenyl) nortropane ((123)I-FP-CIT) Single-photon emission computed tomography (SPECT) scanning ((123)I-FP-CIT SPECT), neurological examination, and polysomnography. RESULTS iRBD patients and PD patients with RBD had increased EMG-activity compared to healthy controls. (123)I-FP-CIT uptake in the putamen-region was highest in controls, followed by iRBD patients, and lowest in PD patients. In iRBD patients, EMG-activity in the mentalis muscle was correlated to (123)I-FP-CIT uptake in the putamen. In PD patients, EMG-activity was correlated to anti-Parkinson medication. CONCLUSIONS Our results support the hypothesis that increased EMG-activity during REM sleep is at least partly linked to the nigrostriatal dopamine system in iRBD, and with dopamine function in PD.

Sleep in intensive care unit: The role of environment ☆ ☆☆

Purpose: To determine if improving intensive care unit (ICU) environment would enhance sleep quality, assessed by polysomnography (PSG), in critically ill mechanically ventilated patients. Materials and methods: Randomized controlled trial, crossover design. The night intervention “quiet routine” protocol was directed toward improving ICU environment between 10 pm and 6 am. Noise levels during control and intervention nights were recorded. Patients on mechanical ventilation and able to give consent were eligible for the study. We monitored sleep by PSG.The standard (American Association of Sleep Medicine) sleep scoring criteria were insufficient for the assessment of polysomnograms. Modified classification for sleep scoring in critically ill patients, suggested by Watson et al. (Crit Care Med 2013;41:1958‐1967), was used. Results: Sound level analysis showed insignificant effect of the intervention on noise reduction (P = .3). The analysis of PSGs revealed that only 53% of the patients had identifiable characteristics of normal sleep, whereas 47% showed only pathologic patterns. Conclusions: Characteristics of normal sleep were absent in many of the PSG recordings in these critically ill patients. We were not able to further reduce the already existing low noise levels in the ICU and did not find any association between the environmental intervention and the presence of normal sleep characteristics in the PSG.

SLEEP phenomena as an early biomarker for Parkinsonism

Idiopathic Rapid-Eye-Movement (REM) sleep Behavior Disorder (iRBD) is one of the most potential biomarkers for Parkinsons Disease (PD) and some atypical PD (AP). It is characterized by REM sleep with abnormal high surface EMG (sEMG) activity. Some twitching during REM sleep is normal, but no one has defined what normal is, and no well-defined methodology for measuring muscle activity in REM sleep exists. The purpose of this study is to investigate the possibility of detecting abnormal high muscle activity during REM sleep in subjects diagnosed with iRBD. This has been achieved by considering the abnormal high muscle activity during REM sleep in iRBD subjects as an outlier detection problem, while exploiting that iRBD muscle activity is more grouped. It was possible to correctly discriminate all iRBD subjects from healthy elderly control subjects and subjects diagnosed with periodic limb movement (PLM) disorder. However, not all PD subjects were classified as having abnormal muscle activity, which is assumed to support the fact that not all PD subjects develop RBD.

Automatic detection of REM sleep in subjects without atonia

Idiopathic Rapid-Eye-Movement (REM) sleep Behavior Disorder (iRBD) is a strong early marker of Parkinsons Disease and is characterized by REM sleep without atonia (RSWA) and increased phasic muscle activity. Current proposed methods for detecting RSWA assume the presence of a manually scored hypnogram. In this study a full automatic REM sleep detector, using the EOG and EEG channels, is proposed. Based on statistical features, combined with subject specific feature scaling and post-processing of the classifier output, it was possible to obtain an mean accuracy of 0.96 with a mean sensititvity and specificity of 0.94 and 0.96 respectively.

Challenges in computerized MUAP analysis

Publisher Summary This chapter reviews that conventional quantitative motor unit action potential (MUAP) analysis is an investigation technique that compares mean MUAP duration and amplitude with healthy controls matched for age. It discusses that the MUAP analysis has evolved from manually measuring the basic parameters of the MUAP, reflecting its morphology to measuring the stability of the MUAP at consecutive discharges and determination of its firing pattern (FP). Most modern computerized MUAP analysis systems (CMAS) offer high quality recordings with good audio and visual feedback, and automatic extraction of MUAPs and measurement of relevant parameters. The chapter also discusses that in more advanced CMAS, where the detection of MUAPs is based on the criteria for a valid MUAP, such as minimum amplitude or maximal rise time, some valid MUAPs may remain undetected. This is often experienced with the clinic-based commercial equipment by observing small MUAPs on the oscilloscope screen that are undetected and left out of the analysis. This leads to difficulty in quantitatively documenting a myopathy from a MUAP analysis.

Early automatic detection of Parkinson's disease based on sleep recordings.

Summary: Idiopathic rapid-eye-movement (REM) sleep behavior disorder (iRBD) is most likely the earliest sign of Parkinsons Disease (PD) and is characterized by REM sleep without atonia (RSWA) and consequently increased muscle activity. However, some muscle twitching in normal subjects occurs during REM sleep. Purpose: There are no generally accepted methods for evaluation of this activity and a normal range has not been established. Consequently, there is a need for objective criteria. Method: In this study we propose a full-automatic method for detection of RSWA. REM sleep identification was based on the electroencephalography and electrooculography channels, while the abnormal high muscle activity was detected from the electromyography channels, in this case the submentalis combined with left and right anterior tibialis. RSWA was identified by considering it an outlier problem, in which the number of outliers during REM sleep was used as a quantitative measure of muscle activity. Results: The proposed method was able to automatically separate all iRBD test subjects from healthy elderly controls and subjects with periodic limb movement disorder. Conclusion: The proposed work is considered a potential automatic method for early detection of PD.