Pulse signal subhealth detection method and detection system based on PCANet

Abstract

The invention relates to a pulse signal subhealth detection method and a detection system based on PCANet. The method comprises the following steps: selecting research objects and initially dividing and screening health conditions of the research objects during detection, and then collecting pulse signal data of the research objects through a pulse sensor; normalizing the collected pulse signal data, wherein the pulse signal data is mapped to a range of [-1, 1], and removing noises and abnormal waveforms in the pulse signal to obtain pure pulse signals; separately extracting health and subhealth pulse signal characteristics on the pre-processed pulse signals by PCANet to reconstruct errors as a loss function, learning a PCA filter nucleus, calculating a sample set by means of a PCA algorithm, and extracting characteristic vectors corresponding to previous L maximum characteristic values of a covariance matrix of the sample seat to form a characteristic mapping matrix; and recognizing health and subhealth states on the extracted characteristic mapping matrix through an SVM classifier and a KNN classifier. The detection process is simple and efficient.

Description

technical field [0001] The invention belongs to the field of computer intelligent vision, and in particular relates to a PCANet-based pulse signal sub-health detection method and detection system. Background technique [0002] Sub-health is an intermediate state between health and disease, mainly manifested in reduced physical function, decreased function and immunity. According to the global survey results of the World Health Organization (WHO), 75% of people are in a sub-health state, 20% are in a disease state, and only 5% are in a healthy state. Sub-health seriously endangers the health of residents. Studies have shown that physiological signals contain a wealth of information closely related to human physiological activities, mental states, and disease diagnosis, and pulse signals are important physiological signals of the human body, which are extremely valuable for the diagnosis of various human diseases. Therefore, The detection of sub-health status can be realized ...

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Problems solved by technology

Of course, it also has some disadvantages: modal aliasing, poor real-time performance, easy to generate new frequency components, etc.

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