Nan Fu

Continuous blood pressure (BP) estimation using pulse transit time (PTT) is a promising method for unobtrusive BP measurement. However, the accuracy of this approach must be improved for it to be viable for a wide range of applications. This study proposes a novel continuous BP estimation approach that combines data mining techniques with a traditional mechanism-driven model. First, 14 features derived from simultaneous electrocardiogram and photoplethysmogram signals were extracted for beat-to-beat BP estimation. A genetic algorithm-based feature selection method was then used to select BP indicators for each subject. Multivariate linear regression and support vector regression were employed to develop the BP model. The accuracy and robustness of the proposed approach were validated for static, dynamic, and follow-up performance. Experimental results based on 73 subjects showed that the proposed approach exhibited excellent accuracy in static BP estimation, with a correlation coefficient and mean error of 0.852 and -0.001 ± 3.102 mmHg for systolic BP, and 0.790 and -0.004 ± 2.199 mmHg for diastolic BP. Similar performance was observed for dynamic BP estimation. The robustness results indicated that the estimation accuracy was lower by a certain degree one day after model construction but was relatively stable from one day to six months after construction. The proposed approach is superior to the state-of-the-art PTT-based model for an approximately 2-mmHg reduction in the standard derivation at different time intervals, thus providing potentially novel insights for cuffless BP estimation.

Abstract Advances in the study of the rate processes in spray drying have helped improve product quality. Single droplet drying (SDD) is an established method for monitoring the drying kinetics and morphological changes of an isolated droplet under a controlled drying environment, mimicking the droplet convective drying process in spray drying. To enhance particle quality requires understanding of both the particle formation process and knowledge of how different particle properties are affected by the drying conditions used. The latest development in the SDD technique enables evaluation of these aspects by incorporating a dissolution test in the drying experiment. The experiment is realized by attaching a solvent droplet to a dried/semi-dried single particle in situ and then video-recording the resultant morphological changes. Some of the particle (e.g., crystallinity) properties obtained under different drying conditions can be modelled using the measured droplet drying kinetics. This paper reviews the applications of SDD experiments in measuring the drying kinetics and monitoring the droplet morphological changes during drying. Some examples of extending the glass filament SDD technique to examine particle functionalities are discussed. SDD experiments are shown to be a powerful tool for particle engineering due to its ability to study both the external convective transport process of a single droplet and to understand the different particle functionalities of the resultant single dried particle. Keywords: Drying kineticsGlass filament approachParticle engineeringParticle functionalitySingle droplet drying