Abstract: Sleep disturbances are common in Parkinson’s disease (PD). We used the Parkinson’s KinetiGraph (PKG), an objective movement recording system for PD to assess night time sleep in 155 people aged over 60 and without PD (controls), 72 people with PD (PwP) and 46 subjects undergoing a Polysomnogram (PSG: 36 with sleep disorder and 10 with normal sleep). The PKG system uses a wrist worn logger to capture acceleration and derive a bradykinesia score (BKS) every 2 min over 6 days. The BKS ranges from 0–160 with higher scores associated with lesser mobility. Previously we showed that BKS > 80 were associated with day time sleep and used this to produce scores for night time sleep: Efficiency (Percent time with BKS > 80), Fragmentation (Average duration of runs of BKS > 80) and Sleep Quality (BKS > 111 as a representation of atonia). There was a fair association with BKS score and sleep level as judged by PSG. Using these PKG scores, it was possible to distinguish between normal and abnormal PSG studies with good Selectivity (86%) and Sensitivity (80%). The PKG’s sleep scores were significantly different in PD and Controls and correlated with a subject’s self-assessment (PDSS 2) of the quality, wakefulness and restlessness. Using both the PDSS 2 and the PKG, it was apparent that sleep disturbances were apparent early in disease in many PD subjects and that subjects with poor night time sleep were more likely to have day time sleepiness. This system shows promise as a quantitative score for assessing sleep in Parkinson’s disease. A movement recording system reveals the occurrence of sleep disturbances in the early stages of Parkinson’s disease (PD). Malcolm Horne, a movement disorders expert at the University in Melbourne, and colleagues assessed night time sleep in 72 patients with PD using a wrist-worn device that captures movement patterns. The Parkinson’s KinetiGraph (PKG) system derives scores that are associated with sleep stages and correlate with patients’ self-assessment of sleep quality, wakefulness and restlessness. Significant differences between the PKG sleep scores of PD patients and age-matched healthy controls confirmed that night time sleep disturbances and day time sleepiness worsen as the disease progresses. Abnormal PKG scores were found in patients affected by the disease for only 3 years highlighting the extent to which sleep is disrupted in early-stage PD.

Abstract: The polysomnogram (PSG) analysis is considered the golden standard for sleep staging under the clinical environment. The electroencephalogram (EEG) signal is the most important signal for classification of sleep stages. However, in-vivo signal recording and analysis of EEG signal presents us with a few technical challenges. Electrocardiogram signals on the other hand, are easier to record, and can provide an attractive alternative for home sleep monitoring. In this paper we describe a method based on deep neural network (DNN), which can be used for the classification of the sleep stages into Wake (W), rapid-eye-movement (REM) and non-rapid-eye-movement (NREM) sleep stage. We apply the sleep stage stacked autoencoder to constitute a 4-layer DNN model. In order to test the accuracy of our method, eighteen PSGs from the MIT-BIH Polysomnographic Database were used. A total of 11 features were extracted from each electrocardiogram recording The experimental design employs cross-validation across subjects, ensuring the independence of the training and the test data. We obtained an accuracy of 77% and a Cohen's kappa coefficient of about 0.56 for the classification of Wake, REM and NREM.

Abstract: Long-term sleep scoring is very important in clinical settings to monitor patients' recovery and at homes for both children and adults. In a cost-effective manner, quality of sleep can often be assessed by the upper-body movement together with heartbeat and respiratory monitoring. Instead of the conventional polysomnogram (PSG) which is uncomfortable due to skin contact of sensors and electrodes, this paper presents radio frequency (RF) near-field coherent sensing (NCS) by a single passive RF identification (RFID) tag in the chest area without requiring skin touch, where heart rates, breath rhythm, and motion can be synchronously extracted. Motion classification is based on support vector machine (SVM) with semi-supervised learning. Sudden body jerk, tossing, and turning can be recognized correctly in 91.06% of the test cases. The heart rate detection is also improved after motion artifact correction.

Abstract: We have developed an automatic sleep stage classification algorithm based on deep residual neural networks and raw polysomnogram signals. Briefly, the raw data is passed through 50 convolutional layers before subsequent classification into one of five sleep stages. Three model configurations were trained on 1850 polysomnogram recordings and subsequently tested on 230 independent recordings. Our best performing model yielded an accuracy of 84.1% and a Cohen’s kappa of 0.746, improving on previous reported results by other groups also using only raw polysomnogram data. Most errors were made on non-REM stage 1 and 3 decisions, errors likely resulting from the definition of these stages. Further testing on independent cohorts is needed to verify performance for clinical use.

Abstract: Sleep is a primary constituent of human life. It is important to maintain good sleep efficiency because some problems occur when sleep efficiency is low. Sleep efficiency is calculated by the ratio of sleep stages. Sleep stages can be classified using Polysomnogram (PSG), which includes information of EEG, EMG and EOG. There have been many studies to classify sleep stages automatically using EEG signal. They, however, have difficulty in classifying several sleep stages because of the resemblance of EEG signals, especially, REM and Non-REM1 (N1) stage. We propose to use EMG signal in addition to EEG signal to improve the accuracy of sleep stage classification. EMG signal is useful for classifying REM stage and Non-REM stages. We propose a machine learning model of Support Vector Machine (SVM) using EEG and EMG signal. The proposed model shows higher classification rate for REM and N1 stage than EEG only model.

Abstract: Sleep apnea is a sleep disorder that causes decreasing or even stopping of breathing during sleep. One way to detect whether a person has the disorder or not, then it can be done by conducting a sleep test (polysomnogram). Polysomnogram provides overall body activity during sleep. Polysomnogram records every process of breath changes, muscle tension, brain waves, eye movements that occur in sleep from awake to the patient has dreams and finally wakes up. Once polysomnogram is obtained, then the doctor will check it. One of the targets of the analysis conducted is sleep stage classification. It takes a long time if done manually. Therefore, it needs an application that automatically to make classification efficiently. It is the main reason for this research that must be done. Specifically, this research applies Multi-Layer Perceptron (MLP) to classify the sleep stage. The results show that MLP has a higher performance than Naive Bayes, Bayesian Networks, K-Nearest Neighbours, and Decision Tree.

Abstract: We have developed an automatic sleep stage classification algorithm based on deep residual neural networks and raw polysomnogram signals. Briefly, the raw data is passed through 50 convolutional layers before subsequent classification into one of five sleep stages. Three model configurations were trained on 1850 polysomnogram recordings and subsequently tested on 230 independent recordings. Our best performing model yielded an accuracy of 84.1% and a Cohen's kappa of 0.746, improving on previous reported results by other groups also using only raw polysomnogram data. Most errors were made on non-REM stage 1 and 3 decisions, errors likely resulting from the definition of these stages. Further testing on independent cohorts is needed to verify performance for clinical use.

Abstract: Study Objectives:Polysomnography (PSG) surveillance recommendations are not being met for children with neuromuscular disease (NMD) because of limited diagnostic facilities. We evaluated the diagno...

Abstract: Study objectives Adaptive servo-ventilation (ASV) devices provide anticyclic pressure support for the treatment of central and/or complex sleep apnea, including heart failure patients. Variability in responses in the clinic and negative clinical trials motivated assessment of standard and novel signal biomarkers for ASV efficacy. Methods Multiple clinical databases were queried to assess potential signal biomarkers of ASV effectiveness, including the following: (1) attended laboratory adaptive ventilation titrations: 108, of which 66 had mainstream ETCO2 measurements; (2) AirView data in 98 participants, (3) complete data, from diagnostic polysomnogram (PSG) through review and prospective analysis of on-therapy data using SleepyHead freeware in 44 participants; and (4) hemodynamic data in the form of beat-to-beat blood pressure during ASV titration, using a Finometer in five participants. Results Signal biomarkers of reduced ASV efficacy were noted as follows: (1) an arousal index which markedly exceeded the respiratory event index during positive pressure titration; (2) persistent pressure cycling during long-term ASV therapy, visible in online review systems or reviewing data using freeware; (3) the ASV-associated pressure cycling induced arousals, sleep fragmentation, and blood pressure surges; and (4) elevated ratios of 95th percentile to median tidal volume, minute ventilation, and respiratory rate were associated with pressure cycling. High intraclass coefficients (>0.8) for machine apnea-hypopnea index and other extractable metrics were consistent with stability of patterns over multiple nights of use. Global clinical outcomes correlated negatively with pressure cycling. Conclusions Potential polysomnographic- and device-related signal biomarkers of ASV efficacy are described and may allow improved estimation of therapeutic effectiveness of adaptive ventilation.

Abstract: The invention provides a sleep monitoring feedback and sleep auto-enhancement device and relates to the technical field of sleep monitoring equipment. The sleep monitoring feedback and sleep auto-enhancement device comprises a micromotion sensitivity sensor, a brain wave sensor, a sleep mattress sleep pillow, a brain wave acquisition module, a control module, a language induction/ sleep music module and a transcranial microcurrent stimulation treatment module. The micromotion sensitivity sensor is arranged in the sleep mattress sleep pillow and is connected to the control module. The brain wave sensor is connected to the brain wave acquisition module which is connected to the control module, and the control module is connected to the language induction/ sleep music module and the transcranial microcurrent stimulation treatment module respectively. The device can be used for sleep monitoring on napping after lunch, multiple sleep latency test, and night split-sleep respiration monitoring, thereby making up for the deficiency that a traditional polysomnogram requires all-night monitoring; the device can be used not only in psychology industry but also in other different industries.

Abstract: Objectives To match responsive neurostimulator (RNS) and polysomnographic data to determine if RNS detections and stimulations correlate with measurements of sleep disordered breathing and continuous glucose measurements (CGM). Materials and methods In a patient with an RNS with detection/stimulation leads implanted bi-temporally detection-stimulation counts were matched by time with coinciding polysomnogram and CGM data. Results Temporal dispersion of RNS DSC were independent of measures of sleep apnea, hypopnea or glucose. Conclusion Hippocampal nighttime responsive neurostimulation therapies did not appear to worsen measures of normal or abnormal sleep.

Abstract: Nasal obstruction and oral breathing may play an important role in the pathophysiology of obstructive sleep apnea (OSA). This study aims to better understand the link between oral breathing, nasal obstruction, and the spectrum of sleep-disordered breathing. Prospective study. Prospective study of patients who presented to the Otolaryngology clinic and underwent polysomnogram (PSG) from 2015 to 2016. Patients were divided into two groups based on the severity of their OSA as defined by PSG results. Both apnea-hypopnea index (AHI) and supine and REM AHI (SUP-REMe AHI), a parameter that takes into account both sleep position and sleep stage, were recorded. The primary outcome was awake nasal-oral forced expiratory volume in 1-s (FEV1) ratio as measured by handheld spirometry. A total of 21 patients were included in the study. We found that nasal-oral FEV1 ratio was significantly different between patients with minimal and substantial OSA as stratified by SUP-REMe AHI, while not significant when stratified by AHI. Patients with substantial OSA as determined by SUP-REMe AHI are more likely to have decreased awake nasal airflow as measured by nasal-oral FEV1. SUP-REMe AHI may represent an improved metric of OSA severity by taking into account sleep position and sleep stage. Handheld spirometers have the potential to become an important office tool by allowing for easy and reliable measurement of nasal airflow.

Abstract: Study objectives Periodic limb movements (PLMs) are routinely measured during polysomnogram (PSG) testing. During the early years of sleep testing, physical movements were identified and over time, consensus ultimately led to the current definitions of movement disorders including criteria used to measure PLMs on PSG testing. There has been considerable debate about the clinical importance of the PLMs measured during PSG testing. Over the last decade, the author has observed significant variations in the actual visible physical movements observed with a PLM event. This report is the result of work to quantify the amount of movement and the frequency of movements observed among individuals who have PLMs. Methods/principal findings Consecutive PSGs performed in a suburban sleep center for an initial diagnosis of a sleep disorder were retrospectively reviewed to identify those with measured PLMs. Of 646 studies on patients >18 years, 460 met criteria for inclusion. Visual assessment of movements was carried out on all of those with PLM events measured using American Academy of Sleep Medicine guidelines. The movements were quantified based on the number of extremities observed to move. PLMs were observed in 237 of the 460 studies that met inclusion criteria (52%). As expected, the PLMs occurred more frequently in older individuals. PLMs occurred with equal frequency in both sexes. Apnea occurred with equal frequency in those with and without observed physical movements. Of those with PLMs, 62% (147) demonstrated observable physical movements. Significant movements involving three or four extremities occurred in 16% of individuals with PLMs. No physical movements were observed in 38%. Conclusion In this uncontrolled, nonrandom, observational series, visual physical movements with a PLM event identify a unique subset of individuals with PLMs. The presence of any visual movements or more pronounced visual movements involving multiple extremities may represent markers for PLM disorder, for clinically significant PLMs with other disorders, or for other clinical conditions or physiologic variables.

Abstract: Pase et al.1 stated changes in REM sleep may not be a marker of prodromal dementia, but may actually be protective against cognitive decline, and further studies were needed. The authors used a model to account for confounding factors including antidepressant use, which is known to suppress REM sleep in healthy and depressed patients.2 If REM sleep is protective for dementia, it would be interesting to perform a longitudinal prospective cohort study with polysomnogram data to analyze if exposure to medications that reduce REM sleep has an increased incidence of dementia. This may be confounded by psychiatric factors, such as heightened anxiety being implicated as a possible risk factor for dementia.3 However, medications, such as duloxetine, are often used for nonpsychiatric indications (e.g., neurogenic pain).

Abstract: Taking a sleep history can at first seem a daunting task. After all, much of what we are asking about is occurring when the patient is asleep. As a result, it can be tempting to bypass the history and rely on objective sleep studies such as the polysomnogram (PSG). However, there is no substitute for a good history, and the majority of sleep disorders can be diagnosed on history alone, with objective studies often being used simply for confirmation or to look for complicating and exacerbating factors.

Abstract: A population of appropriately selected patients does not respond, or does not achieve cure, with hypoglossal nerve stimulation (HGNS). We describe the case of nonresponder whose obstructive sleep apnea (OSA) resolved with the addition of chin strap. After initial placement and titration of HGNS implant, follow-up sleep study demonstrated persistent moderate OSA. Drug-induced sleep endoscopy demonstrated supraglottic collapse with activate neurostimulation. With mouth closure and change of stimulation settings to unipolar from bipolar, the airway collapse and desaturations improved. The follow-up polysomnogram with (HGNS) therapy and chin strap demonstrated resolution of sleep apnea. Laryngoscope, 128:1727-1729, 2018.

Abstract: Disordered sleep breathing is a common complication of diabetic peripheral neuropathy (DPN) manifested by excessive daytime sleepiness, morning headache, morning dizziness, cognitive decline, and mood changes. This study was performed on 30 non-obese type 2 diabetic patients; 20 with clinically evident DPN and 10 without. Ten age-, sex-, and body mass index-matched healthy control subjects were also included. Patients and control were subjected to history taking, neurological examination, glycated hemoglobin, and clinical assessment of the sensori-motor manifestations by the neuropathy symptom score and neuropathy disability score. The autonomic nervous system was evaluated clinically by the systolic blood pressure response to standing and heart rate response to each of standing, Valsalva, and deep breath. Finally, sleep was assessed by one-night polysomnogram (PSG) followed by multiple sleep latency test in the next day. The study showed significant increase in sleep apnea syndromes in diabetic peripheral neuropathy patients compared to diabetic neuropathy free patients and healthy control (p < 0.0001). The sleep apnea was mainly obstructive and to a little extent mixed (obstructive/central) sleep apnea. The severity of sleep PSG abnormalities was positively correlated with the severities of sensory, motor, and autonomic manifestations. Non-obese type 2 diabetic patients complicated by peripheral neuropathy especially those having dysautonomia are at increased risk of developing sleep disordered breathing resulting in their excessive daytime sleepiness, decreased productivity, and poor glycemic control.