Clinical efficacy and mechanism study of mid-frequency anti-snoring device in treating moderate obstructive sleep apnea-hypopnea syndrome

BACKGROUND Obstructive sleep apnea-hypopnea syndrome(OSAHS)is primarily caused by airway obstruction due to narrowing and blockage in the nasal and nasopha-ryngeal,oropharyngeal,soft palate,and tongue base areas.The mid-frequency anti-snoring device is a new technology based on sublingual nerve stimulation.Its principle is to improve the degree of oropharyngeal airway stenosis in OSAHS patients under mid-frequency wave stimulation.Nevertheless,there is a lack of clinical application and imaging evidence.METHODS We selected 50 patients diagnosed with moderate OSAHS in our hospital between July 2022 and August 2023.They underwent a 4-wk treatment regimen involving the mid-frequency anti-snoring device during nighttime sleep.Following the treatment,we monitored and assessed the sleep apnea quality of life index and Epworth Sleepiness Scale scores.Additionally,we performed computed tomo-graphy scans of the oropharynx in the awake state,during snoring,and while using the mid-frequency anti-snoring device.Cross-sectional area measurements in different states were taken at the narrowest airway point in the soft palate posterior and retrolingual areas.RESULTS Compared to pretreatment measurements,patients exhibited a significant reduction in the apnea-hypopnea index,the percentage of time with oxygen saturation below 90%,snoring frequency,and the duration of the most prolonged apnea event.The lowest oxygen saturation showed a notable increase,and both sleep apnea quality of life index and Epworth Sleepiness Scale scores improved.Oropharyngeal computed tomography scans revealed that in OSAHS patients cross-sectional areas of the oropharyngeal airway in the soft palate posterior area and retrolingual area decreased during snoring compared to the awake state.Conversely,during mid-frequency anti-snoring device treatment,these areas increased compared to snoring.CONCLUSION The mid-frequency anti-snoring device demonstrates the potential to enhance various sleep parameters in patients with moderate OSAHS,thereby improving their quality of life and reducing daytime sleepiness.These therapeutic effects are attributed to the device’s ability to ameliorate the narrowing of the oropharynx in OSAHS patients.

Dual ultra-wideband(UWB)radar-based sleep posture recognition system:Towards ubiquitous sleep monitoring

Sleep posture monitoring is an essential assessment for obstructive sleep apnea(OSA)patients.The objective of this study is to develop a machine learning-based sleep posture recognition system using a dual ultra-wideband radar system.We collected radiofrequency data from two radars positioned over and at the side of the bed for 16 patients performing four sleep postures(supine,left and right lateral,and prone).We proposed and evaluated deep learning approaches that streamlined feature extraction and classification,and the traditional machine learning approaches that involved different combinations of feature extractors and classifiers.Our results showed that the dual radar system performed better than either single radar.Predetermined statistical features with random forest classifier yielded the best accuracy(0.887),which could be further improved via an ablation study(0.938).Deep learning approach using transformer yielded accuracy of 0.713.

Healthcare Algorithms by Wearable Inertial Sensors: A Survey

Wearable smart devices, such as smart watch, wristband are becoming increasingly popular recently. They generally integrate the MEMS-designed inertial sensors, including accelerometer, gyroscope and compass, which provide a convenient and inexpensive way to collect motion data of users. Such rich, continuous motion data provide great potential for remote healthcare and decease diagnosis. Information processing algorithms play the critical role in these approaches, which is to extract the motion signatures and to access different kinds of judgements. This paper reviews key algorithms in these areas. In particular, we focus on three kinds of applications: 1) gait analysis; 2) fall detection and 3) sleep monitoring. They are the most popular healthcare applications based on the inertial data. By categorizing and introducing the key algorithms, this paper tries to build a clear map of how the inertial data are processed; how the inertial signatures are defined, extracted, and utilized in different kinds of applications. This will provide a valuable guidance for users to understand the methodologies and to select proper algorithm for specifi c application purpose.

Inhibition of central Na＋/H＋ exchanger type 3 can alleviate sleep apnea in Sprague-Dawley rats

Background Recent studies showed the central Na＋/H＋ exchanger type 3 （NHE3） has a close relationship with ventilation control.The objective of the study is to investigate the role of NHE3 in sleep apnea in Sprague-Dawley （SD） rats.Methods A sleep study was performed on 20 male SD rats to analyze the correlation between the sleep apneic events and total NHE3 protein content and inactive NHE3（pS552） in the brainstem measured by Western blotting.Another 20 adult male SD rats received 3 days of sleep and respiration monitoring for 6 hours a day,with adaption on the first day,0.5％ DMSO microinjection into the fourth ventricle on the second day,and AVE0657 （specific inhibitor of NHE3） microinjection on the third day.Rats were divided into two groups with injection of 5 μmol/L or 8 μmol/L AVE0657 before the sleep study.The effects of AVE0657 on sleep apnea and sleep structure of rats were analyzed through self-control.Results The total post-sigh apnea index （TPSAI） and post-sigh apnea index in non-rapid eye movement （NREM） sleep （NPSAI） and total apnea index （AI） in NREM sleep （NAI） were negatively correlated with NHE3（pS552） protein contents in the brainstem （r=-0.534,-0.547 and-0.505,respectively,P＜0.05）.The spontaneous apnea index in REM sleep （RSPAI） was positively correlated with the level of NHE3（pS552） protein expression in the brainstem （r=0.556,P＜0.05）.However,the sleep AI had no relationship with total NHE3 protein.Compared with the blank control and microinjection of 0.5％ DMSO,5 μmol/L AVE0657 significantly reduced the total AI and NPSAI （both P＜0.05） without a significant effect on sleep architecture.In contrast to blank control and microinjection of 0.5％ DMSO,injection of 8 μmol/L AVE0657 significantly reduced the AI and PSAI in NREM and REM sleep （all P＜0.05）.Conclusions The severity of sleep apnea was negatively correlated with central inactive NHE3.A specific inhibitor of NHE3 decreased the sleep AI.Thus,our results indicate that central NHE3 might be a molecular target for sleep apnea treatment,whose inhibitors may be potential therapeutic drugs for sleep apnea.

Overview of smartphone applications for sleep analysis

Objective:To review and assess the current selection of sleep analysis smartphone applications (apps) available for download. Methods:The iOS and Google Play mobile app store were searched for sleep analysis apps tar-geted for consumer use. Alarm clock, sleep-aid, snoring and sleep-talking recorder, fitness tracker apps, and apps geared towards health professionals were excluded. App information and features were obtained from in-store descriptions, and the app developer website. Results:A total of 51 unique sleep apps in both iOS and Google Play stores were included. The apps were rated 3.8/5 in both stores, and had an average price of$1.12 in the iOS store and$0.58 in the Google Play store.>65%of sleep apps report on sleep structure, including dura-tion, time awake, and time in light/deep sleep, while reporting of REM was limited. The avail-ability of extra features was variable, ranging from 4%to 73%of apps. Conclusions:There are a variety of sleep analysis apps with a range of functionality. The apps with the most reviews from the each store are featured. Many apps provide data on sleep structure;however the algorithms are not validated by scientific literature or studies. Since patients may inquire about their sleep habits from these apps, it is necessary for physicians to be aware of the most common apps and the features offered and their limitations in order to properly counsel patients. Copyright a 2016 Chinese Medical Association. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).