ABSTRACT
Obstructive sleep apnea is a sleep disorder that is linked with many health complications and severe form of apnea can even be lethal. Overnight polysomnography is the gold standard for diagnosing apnea, which is expensive, time-consuming, and requires manual analysis by a sleep expert. Recently, there have been numerous studies demonstrating the application of artificial intelligence to detect apnea in real time. But the majority of these studies apply data pre-processing and feature extraction techniques resulting in a longer inference time that makes the real-time detection system inefficient. This study proposes a single convolutional neural network architecture that can automatically extract spatial features and detect apnea from both electrocardiogram (ECG) and blood-oxygen saturation (SpO2) signals. Using segments of 10s, the network classified apnea with an accuracy of 94.2% and 96% for ECG and SpO2 respectively. Moreover, the overall performance of both models was consistent with an AUC score of 0.99.
ABSTRACT
Sleep apnea (SA) is a common sleep disorder characterized by respiratory disturbance during sleep. Polysomnography (PSG) is the gold standard for apnea diagnosis, but it is time-consuming, expensive, and requires manual scoring. As an alternative to PSG, we investigated a real-time SA detection system using oxygen saturation level (SpO2) and electrocardiogram (ECG) signals individually as well as a combination of both. A series of R-R intervals were derived from the raw ECG data and a feed-forward deep artificial neural network is employed for the detection of SA. Three different models were built using 1-minute-long sequences of SpO2 and R-R interval signals. The 10-fold cross-validation result showed that the SpO2-based model performed better than the ECG-based model with an accuracy of 90.78 ± 10.12% and 80.04 ± 7.7%, respectively. Once combined, these two signals complemented each other and resulted in a better model with an accuracy of 91.83 ± 1.51%.
ABSTRACT
PURPOSE: The aim of the present study was to improve the bioavailability of itopride (ITO) and sustain its action by formulating as a floating dosage form. MATERIALS AND METHODS: Sustained-release floating tablets of ITO hydrochloride (HCl) were prepared by direct compression using different hydrocolloid polymers such as hydroxypropyl methylcellulose and ethylcellulose and/or methacrylic acid polymers Eudragit RSPM and Carbopol 934P. The floating property was achieved using an effervescent mixture of sodium bicarbonate and anhydrous citric acid (1:1 mol/mol). Hardness, friability, content uniformity, and dissolution rate of the prepared floating tablets were evaluated. The formulation F10 composed of 28.5% Eudragit RSPM, 3% NaHCO3, and 7% citric acid provided sustained drug release. RESULTS: In vitro results showed sustained release of F10 where the drug release percentage was 96.51%±1.75% after 24 hours (P=0.031). The pharmacokinetic results indicated that the area under the curve (AUC0-∞) of the prepared sustained-release floating tablets at infinity achieved 93.69 µg·h/mL compared to 49.89 µg·h/mL for the reference formulation (Ganaton®) and the relative bioavailability of the sustained-release formulation F10 increased to 187.80% (P=0.022). CONCLUSION: The prepared floating tablets of ITO HCl (F10) could be a promising drug delivery system with sustained-release action and enhanced drug bioavailability.
