Monitoring of beta-Blockers Ozone Degradation via Electrospray Ionization Mass Spectrometry

Santos, LS (reprint author), Talca Univ, Lab Asymmetr Synth, POB 747, Talca, Chile.The structures of intermediate products of ozone degradation of different pharmaceutical compounds have been studied. Under the conditions employed, complete ozone degradation of nadolol was achieved after 100 min. The degradation products obtained in aqueous solution were characterized by electrospray ionization mass (and tandem mass) spectrometry (ESI-MS and ESI-MS/MS). The proposed mechanism for degradation, ozone attacks at the aniline amino group giving rise to nitro compounds and further degradation occurs via a series of oxidative processes. Continuous online monitoring by ESI-MS(/MS) with high accuracy mass measurements showed that ozone degradation of atenolol (ATE) and acebutolol (ACE) occurs via mechanisms similar to that of nadolo

Analyzing respiratory effort amplitude for automated sleep stage classification

Respiratory effort has been widely used for objective analysis of human sleep during bedtime. Several features extracted from respiratory effort signal have succeeded in automated sleep stage classification throughout the night such as variability of respiratory frequency, spectral powers in different frequency bands, respiratory regularity and self-similarity. In regard to the respiratory amplitude, it has been found that the respiratory depth is more irregular and the tidal volume is smaller during rapid-eye-movement (REM) sleep than during non-REM (NREM) sleep. However, these physiological properties have not been explicitly elaborated for sleep stage classification. By analyzing the respiratory effort amplitude, we propose a set of 12 novel features that should reflect respiratory depth and volume, respectively. They are expected to help classify sleep stages. Experiments were conducted with a data set of 48 sleepers using a linear discriminant (LD) classifier and classification performance was evaluated by overall accuracy and Cohen's Kappa coefficient of agreement. Cross validations (10-fold) show that adding the new features into the existing feature set achieved significantly improved results in classifying wake, REM sleep, light sleep and deep sleep (Kappa of 0.38 and accuracy of 63.8%) and in classifying wake, REM sleep and NREM sleep (Kappa of 0.45 and accuracy of 76.2%). In particular, the incorporation of these new features can help improve deep sleep detection to more extent (with a Kappa coefficient increasing from 0.33 to 0.43). We also revealed that calibrating the respiratory effort signals by means of body movements and performing subject-specific feature normalization can ultimately yield enhanced classification performance. Keywords Respiratory effort amplitude; Signal calibration; Feature extraction; Sleep stage classificatio

Using dynamic time warping for sleep and wake discrimination

In previous work, a Linear Discriminant (LD) classifier was used to classify sleep and wake states during single-night polysomnography recordings (PSG) of actigraphy, respiratory effort and electrocardiogram (ECG). In order to improve the sleep-wake discrimination performance and to reduce the number of modalities needed for class discrimination, this study incorporated Dynamic Time Warping (DTW) to help discriminate between sleep and wake states based on actigraphy and respiratory effort signal. DTW quantifies signal similarities manifested in the features extracted from the respiratory effort signal. Experiments were conducted on a dataset acquired from nine healthy subjects, using an LD-based classifier. Leave-one- out cross-validation shows that adding this DTW-based feature to the original actigraphy- and respiratory-based feature set results in an epoch-by-epoch Cohen’s Kappa agreement coefficient of ¿ = 0.69 (at an overall accuracy of 95.4%), which represents a significant improvement when compared with the performance obtained without using this feature. Furthermore it is comparable to the result obtained in the previous work which used additional ECG features (¿ = 0.70)

System and method for determining spectral boundaries for sleep stage classification

The present disclosure pertains to a system (10) configured to determine spectral boundaries (216, 218) for sleep stage classification in a subject (12). The spectral boundaries may be customized and used for sleep stage classification in an individual subject. Spectral boundaries determined by the system that are customized for the subject may facilitate sleep stage classification with higher accuracy relative to classifications made based on static, fixed spectral boundaries that are not unique to the subject. In some implementations, the system comprises one or more of a sensor (16), a processor (20), electronic storage (22), a user interface (24), and/or other components