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

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*

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

The aim of this study was to analyze the changes and improvements in symptoms of sleep-disordered breathing (SDB) using semi-rapid maxillary expansion (SRME) in children with narrow maxilla and SDB symptoms. Subjects were 15 patients with sleep disorder (apnea-hypopnea index, AHI ≥ 1) and narrow maxillary arch between 7 and 9 years of age. Before the SRME was applied, all subjects underwent pediatric sleep questionnaires (PSQ), lateral cephalometry, and portable sleep monitoring before expansion (T0). All subjects were treated with SRME for 2 months, followed by maintenance for the next 3 months. All subjects had undergone PSQ, lateral cephalometry, and portable sleep monitoring after expansion (T1). Adenoidal-nasopharyngeal ratio (ANR), upper airway width and hyoid bone position were measured by lateral cephalometry. The data before and after SRME were statistically analyzed with frequency analysis and Wilcoxon signed rank test. As reported by PSQ, the total PSQ scale was declined significantly from 0.45 (T0) to 0.18 (T1) (p = 0.001). Particularly, snoring, breathing, and inattention hyperactivity were significantly improved (p = 0.001). ANR significantly decreased from 0.63 (T0) to 0.51 (T1) (p = 0.003). After maxillary expansion, only palatopharyngeal airway width was significantly increased (p = 0.035). There was no statistically significant difference in position of hyoid bone after expansion (p = 0.333). From analysis of portable sleep monitoring, changes in sleep characteristics showed a statistically significant decrease in AHI and ODI, and the lowest oxygen desaturation was significantly increased after SRME (p = 0.001, 0.004, 0.023).In conclusion, early diagnosis with questionnaires and portable sleep monitoring is important. Treatment using SRME will improve breathing of children with SDB.
Cephalometry ; Child ; Diagnosis ; Early Diagnosis ; Humans ; Hyoid Bone ; Maxilla ; Oxygen ; Palatal Expansion Technique ; Polysomnography ; Respiration ; Sleep Apnea Syndromes ; Sleep Wake Disorders ; Snoring

Obstructive sleep apnea (OSA) is a chronic sleep-related breathing disorder that requires long-term management. If OSA remains untreated, it can result in serious health consequences, including increased risk of both cardiovascular and cerebrovascular diseases. Polysomnography is considered to be the gold standard for diagnosing OSA; however, it is relatively expensive, time-consuming and technically complex. Thus, there is a growing interest in the use of simple and efficient screening tools for OSA. Although screening questionnaires such as the Berlin Questionnaire, the STOP Questionnaire, and the STOP-Bang Questionnaire are widely used to assess the presence of OSA, the findings regarding their diagnostic accuracy are not consistent. This review provides a descriptive summary of the scientific studies evaluating the accuracy of diagnostic tests for OSA.
Berlin ; Cerebrovascular Disorders ; Diagnosis ; Diagnostic Tests, Routine ; Mass Screening ; Polysomnography ; Respiration ; Sensitivity and Specificity ; Sleep Apnea Syndromes ; Sleep Apnea, Obstructive ; Surveys and Questionnaires

BACKGROUND: Sleep apnea is a common sleep disorder. Since polysomnography is essential for the diagnosis of sleep apnea, patient screening or selection is an important issue in the sleep clinic. The purpose of this study was to investigate the clinical risk factors of sleep apnea in a representative sleep clinic in South Korea. METHODS: The medical records of the 7,559 adult patients who visited the sleep clinic from 2009 to 2018 were reviewed. We investigated the demographic data and the results of the sleep questionnaires and polysomnography to determine clinical risk factors of sleep apnea for patients at the sleep clinic. Apnea-hypopnea index over 15 was regarded as clinically significant sleep apnea. RESULTS: A total of 4,581 patients were finally analyzed. In order of significance, age (odds ratio [OR]=1.224 from 50 to 64, p=0.027; OR=1.858 in 65 or more, p<0.001), sex (male) (OR=5.900, p<0.001), body mass index (OR=2.833 from 25 to 29.9 kg/m², p<0.001; OR=9.388 over 30 kg/m², p<0.001) and hypertension (OR=1.537, p<0.001) were independent risk factors of sleep apnea. CONCLUSIONS: In South Korea, it is necessary to specify the risk factors of sleep apnea according to the health related characteristics of Koreans. Further research to develop new instruments for screening sleep apnea in Korean sleep clinics is needed.
Adult ; Body Mass Index ; Diagnosis ; Humans ; Hypertension ; Korea ; Mass Screening ; Medical Records ; Polysomnography ; Risk Factors ; Sleep Apnea Syndromes ; Sleep Wake Disorders

INTRODUCTIONPatients with obstructive sleep apnoea (OSA) often present with excessive daytime sleepiness (EDS) as measured by the Epworth Sleepiness Scale (ESS). However, the relationship between EDS and OSA severity as measured by the apnoea-hypopnoea index (AHI) remains inconsistent. We hypothesise that this may be due to the usage and equal weightage of apnoea and hypopnoea events used in determining AHI and that apnoea and hypopnoea load as measured by their total durations may be a better metric to use. We sought to investigate if apnoea or hypopnoea load can display better correlation with ESS.

MATERIALS AND METHODSRetrospective analysis of 821 patients with AHI ≥5, who underwent in-laboratory polysomnogram for suspected OSA from January 2015-December 2015, was performed. Objective factors on polysomnogram were correlated with ESS.

RESULTSESS was correlated with age (r = -0.148, <0.001), number of apnoeas (r = 0.096, = 0.006), apnoea load (r = 0.102, = 0.003), apnoea index (r = 0.075, = 0.032), number of desaturations (r = 0.081, = 0.020), minimum SpO (r = -0.071, = 0.041), time SpO <85% (r = 0.075, = 0.031) and REM sleep duration (r = 0.099, = 0.004). Linear regression analysis found age ( <0.001), apnoea load ( = 0.005), REM ( = 0.021) and stage 1 sleep duration ( = 0.042) as independent factors correlated to ESS. The apnoea load calculated using duration in apnoea correlate with ESS in patients with severe OSA by AHI criteria compared to the mild category.

CONCLUSIONAHI does not correlate with ESS. Younger age, longer apnoea, stage 1 and REM sleep were independently related to higher ESS though the correlations were weak. Apnoea load should be taken into account when determining OSA severity.

Adult ; Age Factors ; Disorders of Excessive Somnolence ; diagnosis ; etiology ; physiopathology ; Female ; Humans ; Male ; Middle Aged ; Polysomnography ; methods ; Retrospective Studies ; Severity of Illness Index ; Singapore ; Sleep Apnea Syndromes ; physiopathology ; Sleep Apnea, Obstructive ; complications ; diagnosis ; physiopathology ; Sleep, REM ; physiology ; Statistics as Topic

BACKGROUND AND OBJECTIVES: Respiratory scoring guidelines for children and adults have been used for evaluating adolescents both in the 2007 and 2012 American Academy of Sleep Medicine (AASM) scoring manuals. We compared the scoring methods of polysomnography used in these scoring manuals, where pediatric and adult scoring rules were adopted for the diagnosis of sleep apnea in adolescents. SUBJECTS AND METHOD: 106 Korean subjects aged between 13 and 18 years were enrolled. All subjects underwent overnight polysomnography in a sleep laboratory. Data were scored according to both pediatric and adult guidelines in the 2007 and 2012 AASM scoring manuals. RESULTS: Both pediatric and adult apnea hypopnea index (AHI) using the 2012 method were significantly higher than those using the 2007 method. The difference in AHI compared between pediatric and adult scores with the 2012 AASM scoring system was markedly decreased from that with the 2007 method. There was a significant discordance in sleep apnea diagnosis between pediatric and adult scoring rules in the 2012 method. CONCLUSION: Both pediatric and adult rules were used for the diagnosis of adolescent sleep apnea in the 2012 method. However, there was significant discordance in the diagnosis between pediatric and adult scoring guidelines in the 2012 AASM manual, probably due to different cut-off values of AHI for the diagnosis of sleep apnea in pediatric (≥1) and adult (≥5) patients. Further studies are needed to determine a more reasonable cut-off value for the diagnosis of sleep apnea in adolescents.
Adolescent* ; Adult ; Apnea ; Child ; Diagnosis* ; Humans ; Methods ; Polysomnography* ; Research Design* ; Sleep Apnea Syndromes*

BACKGROUND AND OBJECTIVES: Obesity is one of the most important risk factors for obstructive sleep apnea (OSA). There is limited evidence regarding the obesity-related anthropometric characteristics of Korean patients. MATERIALS AND METHOD: Medical records of 984 patients referred to 3 tertiary referral hospitals for habitual snoring or sleep apnea were analyzed. We defined OSA as apnea-hypopnea index (AHI) ≥5 and analyzed data to determine the anthropometric characteristics of patients with OSA such as neck circumference (NC), waist circumference (WC), hip circumference (HC), and waist to hip ratio (WHR). RESULTS: A total of 952 patients (719 men) were included in the analysis. The main findings were: 1) BMI, WC, NC, HC, and WHR were greater among patients with OSA than among controls (AHI < 5); 2) for both sexes, the proportion of patients with an OSA diagnosis increased with age; it increased steeply for women aged >50 years; 3) WC and WHR were most strongly correlated with AHI for men and women, respectively. CONCLUSION: OSA is associated with anthropometric characteristics, although different patterns were observed between men and women. OSA was more strongly associated with NC or WC among men and with WHR among women.
Body Mass Index ; Diagnosis ; Female ; Hip ; Humans ; Male ; Medical Records ; Methods ; Neck ; Obesity ; Risk Factors ; Sleep Apnea Syndromes ; Sleep Apnea, Obstructive* ; Snoring ; Tertiary Care Centers ; Waist Circumference ; Waist-Hip Ratio