Obstructive Sleep Apnea and Type 2 Diabetes: Dual Interaction

Apnoea detection using ECG signal based on machine learning classifiers and its performances

Sleep apnoea is a common disorder affecting sleep quality by obstructing the respiratory airway. This disorder can also be correlated to certain diseases like stroke, depression, neurocognitive disorder, non-communicable disease, etc. We implemented machine learning techniques for detecting sleep apnoea to make the diagnosis easier, feasible, convenient, and cost-effective. Electrocardiography signals are the main input used here to detect sleep apnoea. The considered ECG signal undergoes pre-processing to remove noise and other artefacts. Next to pre-processing, extraction of time and frequency domain features is carried out after finding out the R-R intervals from the pre-processed signal. The power spectral density is calculated by using the Welch method for extracting the frequency-domain features. The extracted features are fed to different machine learning classifiers like Support Vector Machine, Decision Tree, k-nearest Neighbour, and Random Forest, for detecting sleep apnoea and performances are analysed. The result shows that the K-NN classifier obtains the highest accuracy of 92.85% compared to other classifiers based on 10 extracted features. The result shows that the proposed method of signal processing and machine learning techniques can be reliable and a promising method for detecting sleep apnoea with a reduced number of features.

COPD and Diabetes Mellitus: Down the Rabbit Hole

Abstract:

One of the important comorbidities that has a longstanding research history in COPD is diabetes. Although there are multiple studies on COPD and diabetes, the exact links between these two conditions are still controversial. The exact prevalence of diabetes in COPD varies between 2 and 37 %. The true nature of this relationship is complex and may be partially related to the tradi-tional risk factors for diabetes such as smoking, cardiovascular disease and use of steroids. Howev-er, COPD is a disease that has multiple phenotypes and is no longer regarded as a homogeneous condition. It seems that some COPD patients who overlap with asthma or the obese phenotype are at a particular risk for T2DM. The aim of this review is to analyze the prevalence, risk factors and possible interactions between COPD and diabetes mellitus.

To sleep, or not to sleep - that is the question, for polysomnography

Have we reached the point where respiratory polygraphy can replace polysomnography in the assessment of OSAS? http://ow.ly/UxCU30bNopq.