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Query: UMLS:C0037315 (sleep apnea)
 8,000 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

The aim of this study is to elucidate whether the results of recurrence quantification analysis (RQA) of sleep EEGs in sleep apnea syndrome are valuable for analyzing sleep EEGs in sleep apnea syndrome. We investigated the ability of RQA to discriminate sleep stages and to characterize the different behaviors of sleep EEGs in sleep apnea syndrome. RQA was applied to EEG signals during sleep stages 1, 2, slow wave sleep (SWS), REM and the stage 'awake.' The sleep EEG signals were obtained from the MIT-BIH polysomnographic database. To examine the differences in the RQA measures for all sleep stages, one-way analysis of variance (ANOVA) and post hoc analysis were performed. From the results, all sleep stages could be distinctly discriminated by means of the RQA measure of %RATIO. We observed that stage 1 and REM had fewer recurrences, and that stage 2 was more autocorrelated than the other stages. The different dynamic behaviors of wakefulness and sleep EEG were also observed. Of significant interest was the observation that RQA was able to distinguish stage 1 from REM. In conclusion, we suggest that the information obtained from RQA of sleep EEGs in sleep apnea syndrome is valuable for its analysis, and that RQA constitutes a useful tool for analyzing sleep EEGs in subjects with sleep apnea syndrome.
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PMID:Recurrence quantification analysis of sleep electoencephalogram in sleep apnea syndrome in humans. 1527 36

Quantification of the fractal scaling properties of human sleep EEG dynamics was sought and each normal sleep stage was compared with that of sleep apnea. The fractal scaling exponents that quantify power-law correlations were computed using detrended fluctuation analysis. Six healthy subjects, aged 30-35 years, participated and six recordings of the apnea were acquired from MIT/BIH polysomnography database. The data were 8-h baseline recordings (23:00-07:00 h). The EEG signals from the C4-A1 derivation were acquired with a resolution of 250 Hz. The sleep stages were visually scored for 30 s epochs, according to the criteria of Rechtschaffen and Kales. The mean scaling exponents increased from the awake stage to stages 1, 2 and 3-4, but decreased during rapid eye movement (REM) sleep. The scaling exponents of the apnea were lower than those of the healthy subject for all the stages. The scaling exponents could be attributed to the fractal nature of EEG, which would be more appropriate for describing the complexity of EEG due to its assumption of non-stationarity.
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PMID:Nonlinear-analysis of human sleep EEG using detrended fluctuation analysis. 1556 14

The diagnosis of sleep apnea syndrome (SAS) has a significant importance in clinic for preventing diseases of hypertention, coronary heart disease, arrhythmia and cerebrovascular disorder, etc. This study presents a novel method for SAS detection based on single-channel electrocardiogram (ECG) signal. The method preprocessed ECG and detected QRS waves to get RR signal and ECG-derived respiratory (EDR) signal. Then 40 time- and spectral-domain features were extracted to normalize the signals. After that support vector machine (SVM) was used to classify the signals as "apnea" or "normal". Finally, the performance of the method was evaluated by the MIT-BIH Apnea-ECG database, and an accuracy of 95% in train sets and an accuracy of 88% in test sets were achieved.
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PMID:[An algorithm based on ECG signal for sleep apnea syndrome detection]. 2445 59

The current gold standard for detecting and distinguishing between types of sleep apnea is expensive and invasive. This paper aims to examine the potential of inexpensive and unobtrusive thermal cameras in the identification and distinction between types of sleep apnea. A thermal camera was used to gather video of a subject performing regular nasal breathing, nasal hyperventilation and an additional trial simulating one type of sleep apnea. Simultaneously, a respiratory inductance plethysmography (RIP) band gathered respiratory data. Thermal video of all three trials were subjected to Eulerian Video Magnification; a procedure developed at MIT for enhancing subtle color variations in video data. Post magnification, nasal regions of interest were defined and mean region intensities were found for each frame of each trial. These signals were compared to determine the best performing region and compared to RIP data to validate breathing behavior. While some regions performed better, all region intensity signals depicted correct breathing behavior. The mean intensity signals for normal breathing and hyperventilation were correct and correlated well with RIP data. Furthermore, the RIP data resulting from the sleep apnea simulation clearly depicted chest movement while the corresponding mean intensity signal depicted lack of cyclical air flow. These results indicate that a subject's breathing behavior can be captured using thermal video and suggest that, with further development and additional equipment, thermal video can be used to detect and distinguish between types of sleep apnea.
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PMID:The detection of breathing behavior using Eulerian-enhanced thermal video. 2673 20

This paper describes a system for the recognition of sleep apnoea episodes from ECG signals using a committee of extreme learning machine (ELM) classifiers. RR-interval parameters (heart rate variability) have been used as the identifying features as they are directly affected by sleep apnoea. The MIT PhysioNet Apnea-ECG database was used. A committee of five ELM classifiers has been employed to classify one-minute epochs of ECG into normal or apnoeic epochs. Our results show that the classification performance from the committee of networks was superior to the results of a single ELM classifier for fan-outs from 1 to 100. Classification performance reached a plateau at a fan-out of 10. The maximum accuracy was 82.5% with a sensitivity of 81.9% and a specificity of 82.8%. The results were comparable to other published research with the same input data.
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PMID:Sleep apnoea episodes recognition by a committee of ELM classifiers from ECG signal. 2673 70

In this paper, we present an approximation method for principal component analysis (PCA) and apply it to estimating the respiration from the overnight ECG signal. The approximation method is computationally fast with low memory requirements. We compare it to a full PCA method which is applied to segments of the ECG. Features were calculated from the two ECG derived respiration signals (EDR) and classifiers trained to detect obstructive sleep apnoea (OSA). The Extreme Learning Machine and Linear Discriminant classifier were used to classify the recordings. The data from 35 overnight ECG recordings from MIT PhysioNet Apnea-ECG training database was utilized in the paper. Apnoea detection was evaluated with leave-one-record-out cross validation. The approximated PCA method obtained the highest accuracy of 76.4% by ELM classifier at fan-out 10 and accuracy of 78.4% by LDA. While, the segmented PCA achieved lower accuracies for both classifiers, 75.9% by ELM classifier and 76.6% by LDA. We conclude that the approximation method for PCA is well suited to deriving the respiration signal from overnight ECGs.
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PMID:A fast approximation method for principal component analysis applied to ECG derived respiration for OSA detection. 2826 67

A measure of the respiratory effort during a sleep study is an important contributor to the diagnosis of sleep apnoea. A common way of measuring respiratory effort is with bands with stretch sensors placed around the chest and/or abdomen. An alternative, and more convenient method from the patient's perspective, is via the ECG derived respiration (EDR) signal which provides an estimate of the respiratory effort at each heartbeat. In this study we performed a side-by-side comparison of the discrimination information for identifying epochs of sleep apnoea contained in the chest respiratory effort signal and three methods of calculating the EDR signal. Using simultaneously recorded chest band and ECG signals extracted from overnight polysomnogram (PSG) data from 8 subjects (4 controls, 4 apnoeas. MIT PhysioNet Apnea-ECG database), we extracted identical features from the two sensors and used the features to train a linear discriminant classifier to classify one-minute epochs as being apneic or normal. Ground truth labelling of each epoch was achieved with an expert using the full PSG as a reference. Our cross validation results revealed that the full respiratory effort signal resulted in an accuracy of 87% in correctly identifying the epoch label. When the respiratory signal was resampled at each heartbeat (as occurs with the EDR signal) the accuracy was 86%, suggesting that the sampling process inherent to the EDR signal does not materially affect its discrimination ability. The best EDR method was based on the calculating the QRS area for every heart and achieved an accuracy of 81%. Our results suggest that, while there is some information loss in the EDR estimation process, the EDR signal is a convenient and useful signal for sleep apnoea diagnosis.
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PMID:Sleep apnoea diagnosis using respiratory effort-based signals - a comparative study. 2906 Jan 76

In this paper, we present a principal component analysis (PCA) method for estimating the respiration from overnight ECG recording. In comparison to other published methods, our method is very fast to compute and has low memory requirements, which makes it suitable for processing long duration ECG recordings. We used our method to derive respiratory features for the ECG which were then used to identify epochs of sleep apnoea from the ECG. Three classifiers including the extreme learning machine (ELM), linear discriminant analysis, and support vector machine were used to detect sleep apnoea. The method was evaluated on the MIT PhysioNet Apnea-ECG database. Apnoea detection was evaluated with leave-one-record-out cross-validation. Our PCA method obtained the highest accuracy of 74% by ELM classifier. We conclude that the fast PCA method is useful to apply PCA to long ECG recordings.
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PMID:A Fast Principal Component Analysis Method For Calculating The ECG Derived Respiration. 3044 32

In this paper, we used ECG signals and repiratory inductance plethysmography (RIP) or respiratory bands. We evaluated the performance of the signals individually as well as different combinations of features and signals for sleep apnoea detection. We implemented two methods (QRS area, and fast principal component analysis (PCA) methods) for estimating the ECG derived respiratory (EDR) signal and the cardiopulmonary coupling (CPC) spectrum. We then extracted features from the time and frequency representations of the ECG and RIP signals. Finally, we applied different features sets to a linear discriminant analysis (LDA) for classification. The results were examined on the MIT PhysioNet Apnea-ECG database. Apnoea classification was carried out using leave-one-record-out crossvalidation approach. The highest performance of our algorithm was achieved using the RIP and RR-interval features as well as using the RIP and PCA CPC features with an accuracy of 90% and AUC of 0.97. The highest performance results of using only RIP or ECG features achieved an accuracy of 87% and AUC of 0.95. We conclude that although ECG sensors are more convenient for patients in sleep studies, using both RIP and ECG sensors enhances the performance results for automated diagnosis of sleep apnoea.
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PMID:Non-invasive Diagnosis of Sleep Apnoea Using ECG and Respiratory Bands. 3194 4

Sleep Apnea-Hypopnea Syndrome (SAHS) is a sleep-related breathing disorder which involves the reduction in breathing airway when patiens sleep. However, a large proportion of patients are usually undiagnosed and untreated which may lead to the risk of life. In this paper, we propose an automatic SAHS event detection method based on Long Short-Term Memory (LSTM) network via nasal airway pressure and temperature signals from clinical polysomnography (PSG) dataset. Focusing on time location of the events, we firstly segment the two channels of signals into a series of sequences by feature extraction. Secondly, a LSTM network is established and these sequences are subsequently fed into this LSTM network for SAHS event classification. The experimental results on both our clinical PSG dataset and public MIT-BIH PSG database show that our method is promising in terms of recall.
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PMID:Sleep Apnea and Hypopnea Events Detection Based on Airflow Signals Using LSTM Network. 3194 23


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