Detection Of Sleep Apnea Syndrome Research Articles

Explainable Non-Contact Sleep Apnea Syndrome Detection Based on Comparison of Random Forests

Explainable Non-Contact Sleep Apnea Syndrome Detection Based on Comparison of Random Forests

Convolutional neural network based on photoplethysmography signals for sleep apnea syndrome detection.

The current method of monitoring sleep disorders is complex, time-consuming, and uncomfortable, although it can provide scientifc guidance to ensure worldwide sleep quality. This study aims to seek a comfortable and convenient method for identifying sleep apnea syndrome. In this work, a one-dimensional convolutional neural network model was established. To classify this condition, the model was trained with the photoplethysmographic (PPG) signals of 20 healthy people and 39 sleep apnea syndrome (SAS) patients, and the influence of noise on the model was tested by anti-interference experiments. The results showed that the accuracy of the model for SAS classifcation exceeds 90%, and it has some antiinterference ability. This paper provides a SAS detection method based on PPG signals, which is helpful for portable wearable detection.

Cross-cultural adaptation and validation of the STOP-bang Questionnaire from English to Spanish as a tool for the early detection of sleep apnea syndrome in the surgical patient and in the general population.

Obstructive Sleep Apnea Syndrome (OSAS) or Apnea-Hypoapnea Syndrome (SAHS) is one of the most prevalent sleep disorders in the general population. It is associated with an increase in the prevalence of difficult orotracheal intubation and postoperative complications. The application of validated early detection tests, such the STOPbang test in English (STBC), is recommended; a test of high methodological quality, sensitivity and specificity in the early detection of SAHS in both surgical and general populations. The validation, translation, cross-cultural adaptation of the STBC to the Spanish population. The transcultural adaptation of the STBC to Spanish was carried out and a subsequent validation study with 77 consecutive patients was carried out. The statistical analysis evaluated the reliability, validity and feasibility of the translated and culturally adapted version. 44% of women and 56% of men were included, with a mean age of 53.58 ± 12.88 years. The reliability results were: a Cronbach's Alpha Coefficient of 0.767, a Pearson correlation r = 0.777 (P < .001) and a Sperman correlation rho = 0.455 (P = .044). The feasibility of the study was 100%. Criterion validity was evaluated using the Kappa coefficient, which was 0.444. For a score >3 of the questionnaire adapted to Spanish, the results of sensitivity, specificity according to the different cut-off levels of the apnea hypopnea index (AHI) >5, >15, >30) were: Sensitivity 87%, 91% and 100% respectively and Specificity of 50%, 31% and 22%. The STBC questionnaire translated, adapted and validated into Spanish, evaluated in the present study, is reliable and valid with respect to the original design of the questionnaire. It is a useful tool that is easy to understand and implement, which can be used rigorously to stratify surgical risk and carry out adequate perioperative planning of those patients at risk of SAHS.

Sleep Apnea Syndrome Detection Based on Degree of Convexity of Logarithmic Spectrum Calculated from Overnight Bio-vibration Data of Mattress Sensor.

This paper proposes the novel Sleep Apnea Syndrome (SAS) detection method based on the frequency analysis of the overnight bio-vibration data acquired from mattress sensor. Concretely, this paper designs the index called Degree of Convexity of the Logarithmic Spectrum (DCLS), which quantifies the degree of convexity by computing the difference between the waveform of the averaged logarithmic spectrum and the waveform of its approximation formula, and employs it to detect SAS. Through the human subject experiment on the SAS detection, the following implications have been revealed: (1) the SAS subjects tend to have the large density around 3Hz, and the average of DCLS in SAS subjects and healthy subjects are 98.6±10.1 and 48.2±6.8 respectively, which succeeds to correctly separate the nine SAS subjects and the nine healthy subjects; and (2) the characteristics of the WAKE stage are different between the SAS and healthy subjects.

Sleep apnea syndrome detection and classification of severity level from ECG and SpO2 signals

Sleep apnea syndrome detection and classification of severity level from ECG and SpO2 signals

ANALYSIS OF PHOTOPLETHYSMOGRAPHIC MORPHOLOGY IN SLEEP APNEA SYNDROME PATIENTS USING CURVE FITTING AND SUPPORT VECTOR MACHINE

This paper develops a time-saving, simple, and comfortable method for detecting Sleep Apnea Syndrome (SAS). Seventy SAS patients and 17 healthy persons were randomly selected in this study, and nine analytical parameters (i.e., [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] of healthy persons and SAS patients during five sleep stages (i.e., W, R, N1, N2, and N3) were obtained to construct a SAS classification model based on logarithmic normal analytical parameters using the Support Vector Machine (SVM) method to fit Photoplethysmographic (PPG) signals. The results show that there were no statistical differences among the five sleep stages for either the healthy or SAS patients. However, there were significant differences in the measured logarithmic normal analytical parameters between the healthy persons and the SAS patients in each of the five sleep stages. The accuracies of the SAS classification model were 95.00%, 90.00%, 84.00%, 94.67%, and 90.77%, corresponding to the five sleep stages, respectively. The SAS classification model based on the SVM method of logarithmic normal analysis parameters can achieve higher classification accuracy for each of the five sleep stages. It can be considered to collect the patient’s pulse wave during the awake period, but not necessarily during the sleep period to classify and identify the SAS; it provides an idea for a convenient and comfortable SAS detection.

Detection of sleep apnea syndrome by CNN based on ECG

Sleep apnea syndrome is a sleep disease that may lead to sudden death. Long term apnea syndrome can cause chronic cerebral hypoxia, hypertension, cardiovascular and cerebrovascular complications. At present, PSG is the most reliable method for diagnosis. But the diagnosis of PSG is complex and expensive. Electrocardiograph(ECG) and portable medical equipment have been widely used nowadays, which makes the acquisition of ECG signal more and more popular and convenient. In this paper, a convolution neural network based on ECG signal is proposed to predict apnea syndrome, the accuracy and sensitivity of this CNN model for apnea syndrome classification are 94% and 88% respectively. The results show that this method has the advantages of low cost and low complexity.

Validation of Withings Sleep for the Detection of Sleep Apnea Syndrome

Validation of Withings Sleep for the Detection of Sleep Apnea Syndrome

Obstructive sleep apnea syndrome detection based on ballistocardiogram via machine learning approach.

Obstructive sleep apnea (OSA) is a common sleep-related respiratory disease that affects people's health, especially in the elderly. In the traditional PSG-based OSA detection, people's sleep may be disturbed, meanwhile the electrode slices are easily to fall off. In this paper, we study a sleep apnea detection method based on non-contact mattress, which can detect OSA accurately without disturbing sleep. Piezoelectric ceramics sensors are used to capture pressure changes in the chest and abdomen of the human body. Then heart rate and respiratory rate are extracted from impulse waveforms and respiratory waveforms that converted by filtering and processing of the pressure signals. Finally, the Heart Rate Variability (HRV) is obtained by processing the obtained heartbeat signals. The features of the heartbeat interval signal and the respiratory signal are extracted over a fixed length of time, wherein a classification model is used to predict whether sleep apnea will occur during this time interval. Model fusion technology is adopted to improve the detection accuracy of sleep apnea. Results show that the proposed algorithm can be used as an effective method to detect OSA.

Detection of sleep apnea syndrome for atrial fibrillation patients using ECG signal

Detection of sleep apnea syndrome for atrial fibrillation patients using ECG signal

A Novel Method for the Detection of Sleep Apnea Syndrome Based on Single-Lead ECG Signal

The diagnosis of sleep apnea syndrome (SAS) has important clinical significance for the prevention of hypertension, coronary heart disease, arrhythmias, stroke and other diseases. In this paper, a novel method for the detection of SAS based on single-lead Electrocardiogram (ECG) signal was proposed. Firstly, the R-peak points of ECG recordings were pre-detected to calculate RR interval series and ECG-derived respiratory signal (EDR). Then 40 time- and spectral-domain features were extracted and normalized. Finally, support vector machine (SVM) was employed to these features as a classifier to detect SAS events. The performance of the presented method was evaluated using the MIT-BIH Apnea-ECG database, results show that an accuracy of 95% in train sets and an accuracy of 88% in test sets are achievable.

Diagnostic Accuracy of Nocturnal Oximetry for Detection of Sleep Apnea Syndrome in Stroke Rehabilitation

Sleep apnea syndrome (SAS) is a common sleep disorder in stroke patients and is associated with decreased recovery and increased risk of recurrent stroke and mortality. The standard diagnostic test for SAS is poly(somno)graphy, but this is often not feasible in stroke rehabilitation settings. This study investigated the diagnostic value of nocturnal oximetry for screening SAS in stroke rehabilitation. Fifty-six stroke patients underwent nocturnal polygraphy and oximetry. Sensitivity, specificity, and positive and negative predictive values for the oxygen desaturation index were calculated. Patient and sleep characteristics were used to develop a predictive model of apnea-hypopnea index. Forty-six percent of the stroke patients had SAS. The majority of SAS patients was male, older, and had a higher body mass index than patients without SAS. Sensitivity, specificity, and positive and negative predictive values for the oxygen desaturation index ≥15 were, respectively, 77%, 100%, 100%, and 83%. Oxygen desaturation index predicted 87% of the variance in the apnea-hypopnea index. Patient characteristics did not add significantly to the prediction model. Nocturnal oximetry is an accurate diagnostic screening instrument for the detection of SAS in stroke patients.

Clinical significance of nocturnal oximeter monitoring for detection of sleep apnea syndrome in the elderly

Objectives: The sensitivity and specificity of overnight monitoring of arterial oxygen saturation (SaO 2) using an oximeter were evaluated in elderly subjects who are being investigated for possible sleep apnea syndrome (SAS). Methods: Seventy-five consecutive elderly subjects with habitual snores (47 men, 28 women; mean (±SE) age 75.5±0.9 years (range 65–94 years)) were studied. The SaO 2 was measured with an oximeter and a chart recorder during the night immediately before detailed polysomnographic studies. The SaO 2 recordings were classified by two observers as positive or negative using a number of significant oxyhemoglobin desaturation (SDS) of more than 2, 4, and 6%. The sensitivity of the oximeter alone for the recognition of the SAS was calculated as the number of true positive SaO 2 records divided by the total number of positive definitive (polysomnographic) records. The specificity was defined as the number of true negative SaO 2 records divided by the total number of negative definitive records. Results: Of the 75 subjects, 24 had moderate SAS (apnea index (AI)>15) and 55 had mild to moderate SAS (AI>5). The sensitivity and specificity of the dosimeter as a screening test were determined with the two diagnostic thresholds of the AI. For AI exceeding 5 or 15, the respective sensitivity by using the criterion of SDS of more than 4% was 85.5 or 91.7%, with corresponding specificity of 85.0 or 92.2%. Conclusions: The nocturnal oximeter monitoring allows recognition of elderly subjects with a mild to moderate SAS, and 4% desaturation of SaO 2 is a candidate index to detect a significant number of apneas in elderly SAS patients with an oximeter.