Humans ; Child ; Sleep Apnea Syndromes/complications* ; Medulla Oblongata/pathology* ; Neoplasms/complications*

Sleep-related breathing disorder (SRBD) is a sleep disease with high incidence and many complications. However, patients are often unaware of their sickness. Therefore, SRBD harms health seriously. At present, home SRBD monitoring equipment is a popular research topic to help people get aware of their health conditions. This article fully compares recent state-of-art research results about home SRBD monitors to clarify the advantages and limitations of various sensing techniques. Furthermore, the direction of future research and commercialization is pointed out. According to the system design, novel home SRBD monitors can be divided into two types: wearable and unconstrained. The two types of monitors have their own advantages and disadvantages. The wearable devices are simple and portable, but they are not comfortable and durable enough. Meanwhile, the unconstrained devices are more unobtrusive and comfortable, but the supporting algorithms are complex to develop. At present, researches are mainly focused on system design and performance evaluation, while high performance algorithm and large-scale clinical trial need further research. This article can help researchers understand state-of-art research progresses on SRBD monitoring quickly and comprehensively and inspire their research and innovation ideas. Additionally, this article also summarizes the existing commercial sleep respiratory monitors, so as to promote the commercialization of novel home SRBD monitors that are still under research.
Humans ; Polysomnography ; Sleep ; Sleep Apnea Syndromes/diagnosis* ; Sleep Wake Disorders

The impact of respiratory function on children's craniofacial growth has received increasing attention from orthodontists and parents. There is a higher pediatric sleep-disordered breathing risk prevalence in the orthodontic population compared with a general population sample, and orthodontic practitioners need to pay close attention to the respiratory function of their pediatric patients. For children with upper airway obstruction and related dentofacial and functional abnormalities, clinicians should comprehensively consider the site and severity of upper airway obstruction, the clinical feature of malocclusion and other factors to develop an individual, multidisciplinary treatment plan, providing favorable conditions for the development of the children's craniofacial morphology and the whole body.
Airway Obstruction/therapy* ; Child ; Humans ; Malocclusion/therapy* ; Prevalence ; Sleep Apnea Syndromes/therapy*

OBJECTIVE: To study the clinical features of sleep-disordered breathing (SDB) in children with neuromuscular disease (NMD). METHODS: A retrospective analysis was performed on the medical data of 18 children who were diagnosed with NMD and underwent polysomnography (PSG) (NMD group). Eleven children without NMD who had abnormal sleeping habit and normal sleep structure on PSG were enrolled as the control group. The two groups were compared in terms of the daily and nocturnal symptoms of SDB, incidence rate of obstructive sleep apnea (OSA), pulmonary function, end-tidal partial pressure of carbon dioxide (PetCO RESULTS: In the NMD group, 16 children (89%) had related daily and nocturnal symptoms of SDB, and the youngest age was 1 year at the onset of such symptoms. Compared with the control group, the NMD group had significant reductions in total sleep time and sleep efficiency ( CONCLUSIONS There is a high proportion of children with SDB among the children with NMD, and SDB can be observed in the early stage of NMD, which results in the damage of sleep structure and the reduction in sleep efficiency. Respiratory events are mainly obstructive events, and oxygen reduction events are mainly observed during REM sleep.
Child ; Humans ; Neuromuscular Diseases/complications* ; Polysomnography ; Retrospective Studies ; Sleep ; Sleep Apnea Syndromes/etiology*

Sleep apnea (SA) detection method based on traditional machine learning needs a lot of efforts in feature engineering and classifier design. We constructed a one-dimensional convolutional neural network (CNN) model, which consists in four convolution layers, four pooling layers, two full connection layers and one classification layer. The automatic feature extraction and classification were realized by the structure of the proposed CNN model. The model was verified by the whole night single-channel sleep electrocardiogram (ECG) signals of 70 subjects from the Apnea-ECG dataset. Our results showed that the accuracy of per-segment SA detection was ranged from 80.1% to 88.0%, using the input signals of single-channel ECG signal, RR interval (RRI) sequence, R peak sequence and RRI sequence + R peak sequence respectively. These results indicated that the proposed CNN model was effective and can automatically extract and classify features from the original single-channel ECG signal or its derived signal RRI and R peak sequence. When the input signals were RRI sequence + R peak sequence, the CNN model achieved the best performance. The accuracy, sensitivity and specificity of per-segment SA detection were 88.0%, 85.1% and 89.9%, respectively. And the accuracy of per-recording SA diagnosis was 100%. These findings indicated that the proposed method can effectively improve the accuracy and robustness of SA detection and outperform the methods reported in recent years. The proposed CNN model can be applied to portable screening diagnosis equipment for SA with remote server.
Electrocardiography ; Humans ; Machine Learning ; Neural Networks, Computer ; Sensitivity and Specificity ; Sleep Apnea Syndromes/diagnosis*

As a low-load physiological monitoring technology, wearable devices can provide new methods for monitoring, evaluating and managing chronic diseases, which is a direction for the future development of monitoring technology. However, as a new type of monitoring technology, its clinical application mode and value are still unclear and need to be further explored. In this study, a central monitoring system based on wearable devices was built in the general ward (non-ICU ward) of PLA General Hospital, the value points of clinical application of wearable physiological monitoring technology were analyzed, and the system was combined with the treatment process and applied to clinical monitoring. The system is able to effectively collect data such as electrocardiogram, respiration, blood oxygen, pulse rate, and body position/movement to achieve real-time monitoring, prediction and early warning, and condition assessment. And since its operation from March 2018, 1 268 people (657 patients) have undergone wearable continuous physiological monitoring until January 2020, with data from a total of 1 198 people (632 cases) screened for signals through signal quality algorithms and manual interpretation were available for analysis, accounting for 94.48 % (96.19%) of the total. Through continuous physiological data analysis and manual correction, sleep apnea event, nocturnal hypoxemia, tachycardia, and ventricular premature beats were detected in 232 (36.65%), 58 (9.16%), 30 (4.74%), and 42 (6.64%) of the total patients, while the number of these abnormal events recorded in the archives was 4 (0.63%), 0 (0.00%), 24 (3.80%), and 15 (2.37%) cases. The statistical analysis of sleep apnea event outcomes revealed that patients with chronic diseases were more likely to have sleep apnea events than healthy individuals, and the incidence was higher in men (62.93%) than in women (37.07%). The results indicate that wearable physiological monitoring technology can provide a new monitoring mode for inpatients, capturing more abnormal events and provide richer information for clinical diagnosis and treatment through continuous physiological parameter analysis, and can be effectively integrated into existing medical processes. We will continue to explore the applicability of this new monitoring mode in different clinical scenarios to further enrich the clinical application of wearable technology and provide richer tools and methods for the monitoring, evaluation and management of chronic diseases.
Heart Rate ; Humans ; Monitoring, Physiologic ; Movement ; Sleep Apnea Syndromes ; Wearable Electronic Devices

Humans ; Polysomnography ; Sleep Apnea Syndromes/therapy*

In order to study the effects of sleep-disordered breathing on human physiology and function, and to accurately and objectively evaluate the level of human sleep quality at night, it can help patients with respiratory disorders improve sleep quality. This paper elaborates the development process of a new multi-parameter sleep quality evaluation system from the hardware circuit design, software and algorithm analysis design of the system. The system hardware platform collects the physiological signals of the 11-channel nighttime sleep period, and displays and stores them in real time on the monitoring platform. After collecting the sleep data of the whole night, it can effectively assist the sleep doctor to sleep by combining the judgment of the sleep-time respiratory disorder, the determination of the sleep cardiovascular event, the determination of the sleep-aware response event, and the sleep structure staging. The quality of sleep in patients with disorders was deeply evaluated.
Algorithms ; Humans ; Sleep ; Sleep Apnea Syndromes ; Software

Sleep posture recognition is the core index of diagnosis and treatment of positional sleep apnea syndrome. In order to detect body postures noninvasively, we developed a portable approach for sleep posture recognition using BCG signals with their morphological difference. A type of piezo-electric polymer film sensor was applied to the mattress to acquire BCG, the discrete wavelet transform with cubic B-spline was used to extract characteristic parameters and a naive Bayes learning phase was adapted to predict body postures. Eleven healthy subjects participated in the sleep simulation experiments. The results indicate that the mean error obtained from heart rates was 0.04±1.3 beats/min (±1.96 SD). The final recognition accuracy of four basic sleep postures exceeded 97%, and the average value was 97.9%. This measuring system is comfortable and accurate, which can be streamlined for daily sleep monitoring application.
Bayes Theorem ; Beds ; Humans ; Polysomnography ; instrumentation ; Posture ; Sleep ; Sleep Apnea Syndromes ; diagnosis

Sleep-disordered breathing (SDB) induces dysfunction of the orofacial muscles, leading to morphologic alteration of the face and dental malalignment. Early diagnosis and treatment of SDB is required in pediatric patients to ensure normal facial growth. Myofunctional therapy (MFT) is a modality for the treatment of SDB and prefabricated appliances can be used. Herein 2 cases of malocclusion with SDB, in which MFT with a prefabricated appliance was used for orthodontic treatment, have been described. SDB was diagnosed based on clinical symptoms taken by interview and home respiratory polygraphy. In both cases, SDB was improved using prefabricated appliance for MFT. However, resolution of crowding depended on the degree of crowding.
Child ; Crowding ; Early Diagnosis ; Humans ; Malocclusion ; Muscles ; Myofunctional Therapy ; Sleep Apnea Syndromes