A Novel Approach to Assess Sleep-Related Rhythmic Movement Disorder in Children Using Automatic 3D Analysis

Persistent combined type sleep-related rhythmic movement disorder into adolescence: a case report

Sleep-related rhythmic movement disorder is characterized by repetitive, stereotyped, rhythmic movements of large muscle groups, primarily occurring at the onset of sleep and during sleep. Common in infancy and early childhood, its persistence into adolescence or adulthood is rare. Combined type is rare. This article reviews and analyzes the diagnosis and treatment of a case with combined type sleep-related rhythmic movement disorder persisting for 15 years aimed at enhancing the level of diagnosis and treatment of the disorder, and reducing misdiagnosis and missed diagnosis.

CITATION

Zhao Y, Wang F, Wang X, Zhao W, Liu Z. Persistent combined type sleep-related rhythmic movement disorder into adolescence: a case report. J Clin Sleep Med. 2024;20(8):1391-1394.

Sleep-related rhythmic movement disorder in adults - A systematic review with a case report

Sleep-related rhythmic movement disorder is characterised by stereotyped and repetitive rhythmic movements involving large muscle groups during sleep with frequencies between 0.5 and 2 Hz. Most of the published studies on sleep-related rhythmic movement disorder have focussed on children. Therefore, we performed a systematic review on this topic focussing on the adult population. The review is followed by a case report. The review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines. A total of seven manuscripts (n = 32 individuals) were included in the review. The clinical manifestation of body or head rolling predominated in the majority of included cases (53.13% and 43.75%, respectively). In n = 11 (34.37%) cases, a combination of rhythmic movements was observed. The literature review also revealed a wide spectrum of co-morbidities: insomnia, restless leg syndrome, obstructive sleep apnea, ischaemic stroke, epilepsy, hypertension, alcohol and drug dependency, mild depression, and diabetes mellitus. The case report presented a 33-year-old female who was referred to the sleep laboratory due to a suspicion of sleep bruxism and obstructive sleep apnea. Although the patient was initially suspected of having obstructive sleep apnea and sleep bruxism, after conducting video-polysomnography she met the criteria for sleep-related rhythmic movement disorder as she presented body rolling, which were surprisingly most evident during the rapid eye movement sleep stage. In summary, the prevalence of sleep-related rhythmic movement disorder among adults has not been determined yet. The present review and case report is a good starting point for discussion regarding rhythmic movement disorder in adults and further research on this topic.

Sleep-related rhythmic movement disorder in children: a mini-review

Sleep-related rhythmic movement disorder (SRRMD) occurs in both infants and children. This disorder rarely occurs or persists in adolescence or adulthood. Rhythmic movement during sleep in children is often asymptomatic and considered a benign condition. It is classified as SRRMD when movement significantly disrupts sleep, results in daytime functional impairment, or causes self-inflicted body injury. Several studies have demonstrated that SRRMD occurs in all sleep stages. Few studies have investigated rhythmic movement disorder (RMD) in children. SRRMD is a clinical diagnosis supported by home video recordings. When the clinical history is insufficient to provide a definitive diagnosis of SRRMD, and other sleep-related conditions or seizure disorders are suspected, video-polysomnography is indicated. There are currently no clinical guidelines for treating SRRMD.

Human activity recognition of children with wearable devices using LightGBM machine learning

Human activity recognition (HAR) using machine learning (ML) methods has been a continuously developed method for collecting and analyzing large amounts of human behavioral data using special wearable sensors in the past decade. Our main goal was to find a reliable method that could automatically detect various playful and daily routine activities in children. We defined 40 activities for ML recognition, and we collected activity motion data by means of wearable smartwatches with a special SensKid software. We analyzed the data of 34 children (19 girls, 15 boys; age range: 6.59–8.38; median age = 7.47). All children were typically developing first graders from three elementary schools. The activity recognition was a binary classification task which was evaluated with a Light Gradient Boosted Machine (LGBM) learning algorithm, a decision tree based method with a threefold cross validation. We used the sliding window technique during the signal processing, and we aimed at finding the best window size for the analysis of each behavior element to achieve the most effective settings. Seventeen activities out of 40 were successfully recognized with AUC values above 0.8. The window size had no significant effect. In summary, the LGBM is a very promising solution for HAR. In line with previous findings, our results provide a firm basis for a more precise and effective recognition system that can make human behavioral analysis faster and more objective.

Sleep-Related Rhythmic Movement Disorder in Young Children with Down Syndrome: Prevalence and Clinical Features

Sleep-related Rhythmic Movement Disorder (RMD) affects around 1% of UK pre-school children. Little is known about RMD in Down syndrome (DS). We aimed to determine: (a) the prevalence of RMD in children with DS aged 1.5–8 years; (b) phenotypic and sleep quality differences between children with DS and RMD and sex- and age-matched DS controls; and (c) night-to-night variability in rhythmic movements (RMs). Parents who previously reported RMs from a DS research registry of 202 children were contacted. If clinical history suggested RMD, home videosomnography (3 nights) was used to confirm RMs and actigraphy (5 nights) was used to assess sleep quality. Phenotype was explored by demographic, strengths and difficulties, Q-CHAT-10/social communication and life events questionnaires. Eight children had confirmed RMD. Minimal and estimated maximal prevalence were 4.10% and 15.38%, respectively. Sleep efficiency was significantly lower in RMD-cases (69.1%) versus controls (85.2%), but there were no other phenotypic differences. There was considerable intra-individual night-to-night variability in RMs. In conclusion, RMD has a high prevalence in children with DS, varies from night to night and is associated with poor sleep quality but, in this small sample, no daytime phenotypic differences were found compared to controls. Children with DS should be screened for RMD, which is amenable to treatment.

3D Camera and Pulse Oximeter for Respiratory Events Detection

OBJECTIVE

The purpose of this study was to derive a respiratory movement signal from a 3D time-of-flight camera and to investigate if it can be used in combination with SpO₂ to detect respiratory events comparable to polysomnography (PSG) based detection.

METHODS

We derived a respiratory signal from a 3D camera and developed a new algorithm that detects reduced respiratory movement and SpO₂ desaturation to score respiratory events. The method was tested on 61 patients' synchronized 3D video and PSG recordings. The predicted apnea-hypopnea index (AHI), calculated based on total sleep time, and predicted severity were compared to manual PSG annotations (manualPSG). Predicted AHI evaluation, measured by intraclass correlation (ICC), and severity classification were performed. Furthermore, the results were evaluated by 30-second epoch analysis, labelled either as respiratory event or normal breathing, wherein the accuracy, sensitivity, specificity and Cohen's kappa were calculated.

RESULTS

The predicted AHI scored an ICC r = 0.94 (0.90 - 0.96 at 95% confidence interval, p < 0.001) compared to manualPSG. Severity classification scored 80% accuracy, with no misclassification by more than one severity level. Based on 30-second epoch analysis, the method scored a Cohen's kappa = 0.72, accuracy = 0.88, sensitivity = 0.80, and specificity = 0.91.

CONCLUSION

Our detection method using SpO₂ and 3D camera had excellent reliability and substantial agreement with PSG-based scoring.

SIGNIFICANCE

This method showed the potential to reliably detect respiratory events without airflow and respiratory belt sensors, sensors that can be uncomfortable to patients and susceptible to movement artefacts.

Transfer Learning for Clinical Sleep Pose Detection Using a Single 2D IR Camera

Sleep quality is an important determinant of human health and wellbeing. Novel technologies that can quantify sleep quality at scale are required to enable the diagnosis and epidemiology of poor sleep. One important indicator of sleep quality is body posture. In this paper, we present the design and implementation of a non-contact sleep monitoring system that analyses body posture and movement. Supervised machine learning strategies applied to noncontact vision-based infrared camera data using a transfer learning approach, successfully quantified sleep poses of participants covered by a blanket. This represents the first occasion that such a machine learning approach has been used to successfully detect four predefined poses and the empty bed state during 8-10 hour overnight sleep episodes representing a realistic domestic sleep situation. The methodology was evaluated against manually scored sleep poses and poses estimated using clinical polysomnography measurement technology. In a cohort of 12 healthy participants, we find that a ResNet-152 pre-trained network achieved the best performance compared with the standard de novo CNN network and other pre-trained networks. The performance of our approach was better than other video-based methods for sleep pose estimation and produced higher performance compared to the clinical standard for pose estimation using a polysomnography position sensor. It can be concluded that infrared video capture coupled with deep learning AI can be successfully used to quantify sleep poses as well as the transitions between poses in realistic nocturnal conditions, and that this non-contact approach provides superior pose estimation compared to currently accepted clinical methods.

Sensory stimulation in the treatment of children with sleep-related rhythmic movement disorder: a feasibility and acceptability study

Background

Sleep-related rhythmic movement disorder is characterized by repetitive gross-motor movements at sleep onset or during sleep, which result in clinical consequences such as impact on daytime functioning and injury. No well-established therapies exist today. Substituting the patient’s movements with external sensory stimulation may offer a treatment modality. The aim of the current study was to test the feasibility and acceptability of vestibular stimulation using a rocking bed (Somnomat) in children with rhythmic movement disorder and to assess children’s movement preference.

Methods

Children with rhythmic movement disorder (n = 6, Age: 5–14 years) were studied over three nights in a sleep laboratory: adaptation night (normal bed) and randomised-order baseline (Somnomat) and intervention nights (Somnomat). Child’s preferred movement direction (head-to-toe or side-to-side) and frequency (between 0.25 and 2 Hz), determined during an afternoon protocol, were applied using the Somnomat for 1 h after lights out, and in response to subsequent episodes of rhythmic movement during intervention nights. Comfort assessed using a questionnaire, and objective sleep parameters assessed using videosomnography, were compared.

Results

The participants’ sometimes violent rhythmic movements did not disturb device performance. All children rated intervention nights equally or more comfortable than baseline nights. Self-reported sleep quality, as well as the number and duration of movement episodes did not significantly differ between baseline and intervention nights.

Conclusions

Providing rocking movements using the Somnomat is both technically feasible and acceptable to the target population. The therapeutic value of this novel stimulus substitution for rhythmic movement disorder should now be evaluated in a larger sample over a longer period in the home setting.

Trial registration

The trial was retrospectively registered at clinicaltrials.gov (NCT03528096) on May 17th 2018.

Disturbed nighttime sleep in children and adults with rhythmic movement disorder

Study Objectives

Rhythmic movements (RMs) during sleep are frequent and often considered as benign in children. Disabling forms are diagnosed as RM disorder and may persist in adulthood. Whether RMs severely impact sleep architecture in patients with RM disorder remain unclear. We performed a case–control study to characterize the clinical and polysomnographic patterns of children and adults with a diagnosis of RM disorder in comparison to controls, and to assess the associations between the RMs and the sleep architecture.

Methods

All consecutive patients (n = 50; 27 children, 35 males) with RM disorder from a single sleep clinic (from 2006 to 2019) underwent a comprehensive clinical evaluation and a polysomnographic recording in comparison to 75 controls (42 children and 53 males).

Results

About 82% of children and adult patients had a complaint of disturbed nighttime sleep. Comorbid neurodevelopmental, affective or sleep disorders were found in 92% of patients. While RM sequences defined by video polysomnographic criteria were observed in 82% of patients (in wakefulness and in all sleep stages), no similar sequences were observed in controls. Patients had altered sleep continuity, with low sleep efficiency, increased wake time after sleep onset, and frequent periodic leg movements and apnea events. The severity of RMs was associated with disrupted nighttime sleep, even after controlling for comorbid motor and respiratory events.

Conclusions

RM disorder is a rare, highly comorbid and disabling condition both in children and adults with frequent disturbed nighttime sleep that may contribute to the burden of the disease.