Physiological parameter measuring method based on multi-scale fusion network

Abstract

The invention provides a physiological parameter measuring method based on a multi-scale fusion network. The method comprises the following steps of sampling physiological signals and generating a one-dimensional physiological signal data sequence; performing noise filtering on data fragments meeting the signal quality requirement through data fragment division and signal quality evaluation; performing mathematical transformation on the data fragments to generate a multi-dimensional input data tensor; extracting potential features from input data by utilizing the multi-scale fusion network to obtain estimated values of physiological parameters; by identifying a measurement mode identifier, taking a mean value of all the estimated values as a physiological parameter measurement value in a static mode; and taking a one-dimensional continuous data sequence formed by all the estimated values as a physiological parameter continuous measurement value in a dynamic mode. According to the method, complementary information of different scales in the signals can be fully extracted, accurate measurement of the physiological parameters is achieved, the application range of the method covers the measurement of all the physiological parameters, and the method has certain application value in the fields of cardiovascular disease research and signal processing research.

Description

technical field [0001] The invention relates to a method for measuring physiological parameters based on a multi-scale fusion network. Background technique [0002] The heart is the power center of human blood circulation. It supplies blood to the whole body through regular beats to meet the metabolism of the human body, thereby maintaining the normal life activities of the human body. It is extremely important to effectively measure physiological parameters related to the cardiovascular system to monitor human health and prevent cardiovascular diseases. With the aging of the population and the increase of work pressure, the prevalence of cardiovascular diseases in China is increasing year by year. According to the World Health Organization report in 2020, cardiovascular disease has become the leading cause of human death globally. Therefore, dynamic detection of physiological parameters related to the cardiovascular system and accurate assessment of health status have gre...

AI Technical Summary

Problems solved by technology

At present, most studies use a single-scale convolutional neural network to achieve automatic feature extraction, but this approach ignores potentially useful information on other scales, resulting in the inability to further improve the measurement accuracy

At the same time, for the same physiological signal, the inter-individual variability and the variability of the waveform also make it impossible for a single-scale convolutional neural network to effectively extract features.

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