scholarly journals Automatic Detection of Obstructive Sleep Apnea Events Using a Deep CNN-LSTM Model

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Junming Zhang ◽  
Zhen Tang ◽  
Jinfeng Gao ◽  
Li Lin ◽  
Zhiliang Liu ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Short Term Memory ◽  
Single Channel ◽  
Final Decision ◽  
Respiratory Disorder ◽  
Monitoring Model ◽  
Proposed Model ◽  
Obstructive Sleep ◽  
Fully Connected

Obstructive sleep apnea (OSA) is a common sleep-related respiratory disorder. Around the world, more and more people are suffering from OSA. Because of the limitation of monitor equipment, many people with OSA remain undetected. Therefore, we propose a sleep-monitoring model based on single-channel electrocardiogram using a convolutional neural network (CNN), which can be used in portable OSA monitor devices. To learn different scale features, the first convolution layer comprises three types of filters. The long short-term memory (LSTM) is used to learn the long-term dependencies such as the OSA transition rules. The softmax function is connected to the final fully connected layer to obtain the final decision. To detect a complete OSA event, the raw ECG signals are segmented by a 10 s overlapping sliding window. The proposed model is trained with the segmented raw signals and is subsequently tested to evaluate its event detection performance. According to experiment analysis, the proposed model exhibits Cohen’s kappa coefficient of 0.92, a sensitivity of 96.1%, a specificity of 96.2%, and an accuracy of 96.1% with respect to the Apnea-ECG dataset. The proposed model is significantly higher than the results from the baseline method. The results prove that our approach could be a useful tool for detecting OSA on the basis of a single-lead ECG.

Rapid Maxillary Expansion in Obstructive Sleep Apnea in Young Patients: Cardio-Respiratory Monitoring

2017 ◽  
Vol 41 (4) ◽  
pp. 312-316 ◽  
Author(s):  
Alfio Buccheri ◽  
Fabio Chinè ◽  
Giovanni Fratto ◽  
Licia Manzon
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Young Patients ◽  
Rapid Maxillary Expansion ◽  
Respiratory Monitoring ◽  
Respiratory Disorder ◽  
Maxillary Expansion ◽  
Mean Values ◽  
Obstructive Sleep ◽  
The Mean

Objective(s): Obstructive sleep apnea syndrome (OSAS) is a respiratory disorder which affects from 1 to 3 % of people during development. OSAS treatment may be pharmacological, surgical or based on application of intraoral devices to increase nasal respiratory spaces. The purpose of this study was to determine the efficacy of the Rapid Maxillary Expander in OSAS young patients by measuring cardio-respiratory monitoring parameters (AHI, the average value of complete and incomplete obstructed respiration per hour of sleep, and SAO2, the percentage of oxygen saturation). Study design: The study was conducted on 11 OSAS young subjects (mean age 6.9±1.04 years), all treated with rapid maxillary expansion (RME). Cardio-respiratory monitoring (8-channel Polymesam) was performed at the beginning (diagnostic, T0) and after 12 months of treatment. Results: The mean values of cardio-respiratory parameters at TO were: AHI=6.09±3.47; SAO2=93.09%±1.60. After 12 months of treatment, the mean values of the same polysomnographic parameters were: AHI=2.36 ± 2.24;SAO2=96.81% ±1.60. These changes were associated with an improvement in clinical symptoms, such as reduction of snoring and sleep apnea. Conclusion(s): This study confirms the therapeutic efficacy of RME in OSAS young patients. This orthopedic-orthodontic treatment may represent a good option in young patients affected by this syndrome.

scholarly journals The Effect of Acupressure Therapy in Obstructive Sleep Apnea

2019 ◽  
Vol 2 (4) ◽  
Author(s):  
Galih Jatnika ◽  
Susilawati Hartanto
Keyword(s):  
Obstructive Sleep Apnea ◽  
High School Students ◽  
Sleep Apnea ◽  
Epworth Sleepiness Scale ◽  
Upper Airway ◽  
P Value ◽  
Respiratory Disorder ◽  
School Students ◽  
Obstructive Sleep ◽  
Quasi Experimental

Obstructive Sleep Apnea (OSA) is a respiratory disorder during sleep with characteristics of temporary breathing cessation and upper airway obstruction that recurred partially or completely. Obesity has been recorded as one important risk factor in OSA. The purpose of this research was to assess the effect of acupressure therapy in OSA. This is a quasi-experimental study with pre-test and post-test one group design. Respondents were 18 high school students with BMI > 25 kg/m2. OSA was assesed using Epworth Sleepiness Scale. Acupressure therapy was done at 10 acupoints for 10 consecutive days. Data was analyzed using dependent t test. The results showed Epworth Sleepiness Scale score decreased significantly after acupressure therapy procedure (6.78 vs 5.28; p value 0.004). Conclusion, acupressure therapy can reduce the severity of OSA.   Keywords: acupressure, obesity, obstructive sleep apnea

scholarly journals Electrocardiographic Associations Seen with Obstructive Sleep Apnea

2019 ◽  
Vol 2019 ◽  
pp. 1-5
Author(s):  
Shyam Shankar ◽  
Sushilkumar Satish Gupta ◽  
Geurys Rojas-Marte ◽  
Selma Demir ◽  
Abhinav Saxena ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Final Analysis ◽  
Cost Effective ◽  
Respiratory Disorder ◽  
Continuous Variables ◽  
Chi Square ◽  
Multivariate Tests ◽  
S Waves ◽  
Obstructive Sleep

Background. Obstructive sleep apnea (OSA) is a chronic respiratory disorder associated with repeated nocturnal partial or complete collapse that is often underdiagnosed and associated with multiple comorbidities. The association between specific features on an electrocardiogram and OSA has not been well studied. This retrospective study attempts to bridge this gap in knowledge.Methods. A total of 265 patients’ medical records were reviewed retrospectively. Specific features of their electrocardiograms and their association with the severity of OSA were studied from April 2014 to May 2016. 215 patients were included in the final analysis. Tests of group difference between OSA patients and controls were done using student’s t-tests for continuous variables and using chi-square tests for categorical outcomes. Multivariate tests of differences between OSA and control patients were done using logistic regression to control for possible confounding factors.Results. A total of 215 patients with diagnosed OSA and 41 controls in whom OSA was ruled out using polysomnography were compared. Males were more likely to present with OSA than females (93 % versus 76 %; p < 0.001). OSA patients were also significantly older: 52.18 ± 14.04 versus 44.55 ± 14.64; p = 0.002. Deep S waves in V5-6 (p=0.014) and RS pattern with Deep S waves in leads I and AVF (p=0.017) were both significantly associated with OSA based on univariate comparisons. These findings lost significance in the multivariate analysis.Conclusion. The idea of using an electrocardiogram in aiding in the assessment of OSA is attractive and feasible, as it is a safe, noninvasive, and cost-effective method. Our results can be used for early risk stratification in patients with OSA.

scholarly journals Automatic Home-based Screening of Obstructive Sleep Apnea using Single Channel Electrocardiogram and SPO2 Signals

2021 ◽  
Vol 12 (06) ◽  
pp. 47-63
Author(s):  
Hosna Ghandeharioun
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Closed Loop ◽  
Single Channel ◽  
Upper Airway ◽  
Sleep Monitoring ◽  
Combination Methods ◽  
Home Based ◽  
Obstructive Sleep ◽  
On Line

Obstructive sleep apnea (OSA) is one of the most widespread respiratory diseases today. Complete or relative breathing cessations due to upper airway subsidence during sleep is OSA. It has confirmed potential influence on Covid-19 hospitalization and mortality, and is strongly associated with major comorbidities of severe Covid-19 infection. Un-diagnosed OSA may also lead to a variety of severe physical and mental side-effects. To score OSA severity, nocturnal sleep monitoring is performed under defined protocols and standards called polysomnography (PSG). This method is time-consuming, expensive, and requiring professional sleep technicians. Automatic home-based detection of OSA is welcome and in great demand. It is a fast and effective way for referring OSA suspects to sleep clinics for further monitoring. On-line OSA detection also can be a part of a closed-loop automatic control of the OSA therapeutic/assistive devices. In this paper, several solutions for online OSA detection are introduced and tested on 155 subjects of three different databases. The best combinational solution uses mutual information (MI) analysis for selecting out of ECG and SpO2-based features. Several methods of supervised and unsupervised machine learning are employed to detect apnoeic episodes. To achieve the best performance, the most successful classifiers in four different ternary combination methods are used. The proposed configurations exploit limited use of biological signals, have online working scheme, and exhibit uniform and acceptable performance (over 85%) in all the employed databases. The benefits have not been gathered all together in the previous published methods.

scholarly journals Portable Sleep Apnea Syndrome Screening and Event Detection Using Long Short-Term Memory Recurrent Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6067
Author(s):  
Hung-Chi Chang ◽  
Hau-Tieng Wu ◽  
Po-Chiun Huang ◽  
Hsi-Pin Ma ◽  
Yu-Lun Lo ◽  
...  
Keyword(s):  
Neural Network ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Apnea Hypopnea Index ◽  
Short Term ◽  
Term Memory ◽  
Obstructive Sleep ◽  
Long Short Term Memory

Obstructive sleep apnea/hypopnea syndrome (OSAHS) is characterized by repeated airflow partial reduction or complete cessation due to upper airway collapse during sleep. OSAHS can induce frequent awake and intermittent hypoxia that is associated with hypertension and cardiovascular events. Full-channel Polysomnography (PSG) is the gold standard for diagnosing OSAHS; however, this PSG evaluation process is unsuitable for home screening. To solve this problem, a measuring module integrating abdominal and thoracic triaxial accelerometers, a pulsed oximeter (SpO2) and an electrocardiogram sensor was devised in this study. Moreover, a long short-term memory recurrent neural network model is proposed to classify four types of sleep breathing patterns, namely obstructive sleep apnea (OSA), central sleep apnea (CSA), hypopnea (HYP) events and normal breathing (NOR). The proposed algorithm not only reports the apnea-hypopnea index (AHI) through the acquired overnight signals but also identifies the occurrences of OSA, CSA, HYP and NOR, which assists in OSAHS diagnosis. In the clinical experiment with 115 participants, the performances of the proposed system and algorithm were compared with those of traditional expert interpretation based on PSG signals. The accuracy of AHI severity group classification was 89.3%, and the AHI difference for PSG expert interpretation was 5.0±4.5. The overall accuracy of detecting abnormal OSA, CSA and HYP events was 92.3%.

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