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Fulltext Road traffic accidents in patients with obstructive sleep apnoea

Liam CK, How LG, Tan CT

Med J Malaysia, 1996 Mar;51(1):143-5.
PMID: 10967996

Three patients involved in road traffic accidents were suspected to have obstructive sleep apnoea (OSA). Two of them fell asleep while riding motorcycles and one patient fell asleep behind the wheel of a truck causing it to overturn. The diagnosis of OSA in each case was suspected based on a history of loud snoring, restless sleep, and excessive daytime somnolence and was confirmed by sleep studies.

Matched MeSH terms: Sleep Apnea Syndromes/physiopathology*
A novel approach to detect respiratory phases from pulmonary acoustic signals using normalised power spectral density and fuzzy inference system

Palaniappan R, Sundaraj K, Sundaraj S, Huliraj N, Revadi SS

Clin Respir J, 2016 Jul;10(4):486-94.
PMID: 25515741 DOI: 10.1111/crj.12250

BACKGROUND: Monitoring respiration is important in several medical applications. One such application is respiratory rate monitoring in patients with sleep apnoea. The respiratory rate in patients with sleep apnoea disorder is irregular compared with the controls. Respiratory phase detection is required for a proper monitoring of respiration in patients with sleep apnoea.
AIMS: To develop a model to detect the respiratory phases present in the pulmonary acoustic signals and to evaluate the performance of the model in detecting the respiratory phases.
METHODS: Normalised averaged power spectral density for each frame and change in normalised averaged power spectral density between the adjacent frames were fuzzified and fuzzy rules were formulated. The fuzzy inference system (FIS) was developed with both Mamdani and Sugeno methods. To evaluate the performance of both Mamdani and Sugeno methods, correlation coefficient and root mean square error (RMSE) were calculated.
RESULTS: In the correlation coefficient analysis in evaluating the fuzzy model using Mamdani and Sugeno method, the strength of the correlation was found to be r = 0.9892 and r = 0.9964, respectively. The RMSE for Mamdani and Sugeno methods are RMSE = 0.0853 and RMSE = 0.0817, respectively.
CONCLUSION: The correlation coefficient and the RMSE of the proposed fuzzy models in detecting the respiratory phases reveals that Sugeno method performs better compared with the Mamdani method.

Matched MeSH terms: Sleep Apnea Syndromes/physiopathology*
Fulltext Obstructed breathing during sleep and obstructive sleep apnoea syndrome--assessment and treatment

Liam CK, Pang YK, Shyamala P, Chua KT

Med J Malaysia, 2007 Aug;62(3):268-73; quiz 274.
PMID: 18246927

During normal sleep the tone of the pharyngeal airway dilator muscles is decreased resulting in upper airway narrowing and increased resistance to airflow. Nasal obstruction may result from a variety of anatomical abnormalities such as septal deviation, nasal polyps, adenoid hypertrophy and rhinitis such as allergic rhinitis, acute viral rhinitis, vasomotor rhinitis and non-allergic rhinitis with nasal eosinophilia syndrome. Disordered breathing during sleep can both result from and be worsened by nasal obstruction. In children, nasal obstruction due to enlarged tonsils and adenoids results in a switch to oral breathing which may lead to the adenoid faces because of changes in the craniofacial structures during growth that predispose to disordered breathing during sleep.

Matched MeSH terms: Sleep Apnea Syndromes/physiopathology
Automated detection of sleep apnea using sparse residual entropy features with various dictionaries extracted from heart rate and EDR signals

Viswabhargav CSS, Tripathy RK, Acharya UR

Comput Biol Med, 2019 05;108:20-30.
PMID: 31003176 DOI: 10.1016/j.compbiomed.2019.03.016

Sleep is a prominent physiological activity in our daily life. Sleep apnea is the category of sleep disorder during which the breathing of the person diminishes causing the alternation in the upper airway resistance. The electrocardiogram derived respiration (EDR) and heart rate (RR-time-series) signals are normally used for the detection of sleep apnea as these two signals capture cardio-pulmonary activity information. Hence, the analysis of these two signals provides vital information about sleep apnea. In this paper, we propose the novel sparse residual entropy (SRE) features for the automated detection of sleep apnea using EDR and heart rate signals. The features required for the automated detection of sleep apnea are extracted in three steps: (i) atomic decomposition based residual estimation from both EDR and heart rate signals using orthogonal matching pursuit (OMP) with different dictionaries, (ii) estimation of probabilities from each sparse residual, and (iii) calculation of the entropy features. The proposed SRE features are fed to the combination of fuzzy K-means clustering and support vector machine (SVM) to pick the best performing classifier. The experimental results demonstrate that the proposed SRE features with radial basis function (RBF) kernel-based SVM classifier yielded higher performance with accuracy, sensitivity and specificity values of 78.07%, 78.01%, and 78.13%, respectively with Fourier dictionary and 10-fold cross-validation. For subject-specific or leave-one-out validation case, the SVM classifier has sensitivity and specificity of 85.43% and 92.60%, respectively using SRE features with Fourier dictionary (FD).

Matched MeSH terms: Sleep Apnea Syndromes/physiopathology*
Fulltext Integrating the Divided Nasal Cannula Into Routine Polysomnography to Assess Nasal Cycle: Feasibility and Effect on Outcomes

Scapuccin M, Schneider L, Rashid N, Zaghi S, Rosa T, Tsou YA, et al.

J Clin Sleep Med, 2018 04 15;14(4):641-650.
PMID: 29609709 DOI: 10.5664/jcsm.7062

STUDY OBJECTIVES: Patients suspected to have sleep-disordered breathing underwent an overnight polysomnography using a divided nasal cannula to gain additional information about the nasal cycle during sleep.
METHODS: This was a prospective, observational cohort study replacing the undivided nasal cannula with a divided nasal cannula during routine polysomnography (n = 28).
RESULTS: Integration of the divided nasal cannula pressure transducer system into routine polysomnography was easy and affordable. Most patients (89%) demonstrated nasal cycle changes during the test. Nasal cycle changes tended to occur during body position changes (62%) and transitions from non-rapid eye movement sleep to rapid eye movement sleep (41%). The mean nasal cycle duration was 2.5 ± 2.1 hours. Other sleep study metrics did not reveal statistically significant findings in relation to the nasal cycle.
CONCLUSIONS: Replacing an undivided nasal cannula with a divided nasal cannula is easy to implement, adding another physiologic measure to polysomnography. Although the divided nasal cannula did not significantly affect traditional polysomnographic metrics such as the apnea-hypopnea index or periodic limb movement index based on this small pilot study, we were able to replicate past nasal cycle findings that may be of interest to sleep clinicians and researchers. Given the ease with which the divided nasal cannula can be integrated, we encourage other sleep researchers to investigate the utility of using a divided nasal cannula during polysomnography.

Matched MeSH terms: Sleep Apnea Syndromes/physiopathology