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The system of Wearable sleep monitoring and nights forewarning for nocturnal cardiovascular events
The project is committed to mainly studying cuffless continuous monitoring of blood pressure. Compared to blood pressure measured by traditional cuff devices, continuous blood pressure and its variability are more important dangerous factors of cardiovascular events. Disturbance-free monitoring by the wearable system bears vital significance for lowering the death rate of cardiovascular events. The continuous blood pressure mechanism model based on pulse wave conduction time had disadvantages of low accuracy, need for calibration and limited duration for tracking blood pressure changes. Aimed at the disadvantages, our research team creatively proposed a continuous blood pressure calculation method that combined data models and the mechanism model, thereby improving the accuracy and universality of the model. To be specific, based on human body hemodynamics model, the team explored factors that significantly influenced blood pressure changes. Using multi-modality data integration theory and large volume of data, the team discovered the impact factor of continuous blood pressure and its internal relations with blood pressure, therefore achieving accurate measurement of blood pressure. Based on the theories above, the team brought forward a 9-point calibration scheme for long-term tracking of blood pressure. The effect of the scheme was expected to meet AAMI standards. Moreover, a high-precision model of continuous blood pressure was established based on data mining and deep learning methods. The model achieved static and dynamic tracking of blood pressure. Representative papers have been published on IEEE JBHI, a SCI journal.
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