Health data detection method based on convolution auto-encoder Gaussian mixture model

Abstract

The invention discloses a health data detection method based on a convolution auto-encoder Gaussian mixture model, and belongs to the technical field of medical care. According to the method, multi-dimensional data is converted into low-dimensional feature representation in a self-adaptive, non-linear and multi-layer coding mode, and the problem that the detection accuracy is reduced due to largecalculation expenditure caused by dimensionality disasters is effectively solved; a convolution and de-convolution neural network layer is added for multi-stage characteristics of human body activitydata, data features are effectively recognized and extracted, and the detection precision is further improved; a dimension reduction process and a density estimation process are organically combined together, and the situation that two models are independent and fall into a locally optimal awkward situation is avoided; meanwhile, in consideration of the singular point problem of the matrix and theproblem that the inverse of the covariance matrix may not be solved, the cholesky decomposition of the covariance matrix is constructed by utilizing the hybrid probability, the mean value and the covariance, so that the sample density is calculated, and the problem that the inverse of the covariance matrix cannot be solved is avoided.

Description

Technical field [0001] The present invention relates to a health data detection method based on convolutionary self-encoder Gaussian mixed model, belonging to a health care technique. Background technique [0002] With the continuous development of the modern economy, people have more attention to their own health, so some health data is more and more attention to their own health data, and more and more people start using some to monitor sleep data, sports steps And the wearable device of the resting heart rate, such as the bracelet. At the same time, if you do medical treatment, the patient can provide its personal daily health information, which will greatly speed up the diagnosis speed and improve the quality of diagnosis. Especially for chronic diseases such as sleep disorders, obstructive sleep apnea syndrome, if it is possible to screen and monitor such chronic diseases in advance according to daily health data will be a major advancement in the medical field. [0003] Due...

AI Technical Summary

Problems solved by technology

[0006] In order to solve the problem of high computational overhead in existing health data detection methods, the present invention provides a health data anomaly detection method based on a convolutional autoencoder Gaussian mixture model, the method comprising:

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