Prediction method and prediction system for sleep disorder based on incremental neural network model

Abstract

The invention discloses a prediction method for a sleep disorder based on an incremental neural network model. The method comprises the steps that a database of sleep disorder daily data is established; the neural network model is trained; daily life data is collected and sent to a server; data of the present day is extracted from a user daily data recording list, and a n-dimensional vector is formed, normalized and input into a pathologic neural network model of the sleep disorder, so that the danger degree and probability of the sleep disorder can be predicted; intelligent home-use sleep disorder nursing equipment judges whether the danger degree W of the sleep disorder is larger than or equal to 3; when a user receives a warning from a warning indicator, the user can go to a hospital for examination and transmit examination results to a server, and the server judges whether the examination results are correct; and an incremental algorithm will be executed when the examination results are wrong, and then the neural network model will be modified dynamically. According to the invention, prediction is accurate, and a neural network model can be customized for each user.

Description

technical field [0001] The invention belongs to the field of medical technology, in particular to a method and system for predicting sleep disorders based on an incremental neural network model. Background technique [0002] At present, all health management systems in China have set up sleep disorder prediction and evaluation, and the prediction method used is data matching. The principle is to input personal life data into the system, and the system matches the fixed data to obtain the probability of disease. However, due to the complexity and unpredictability of the human body and diseases, the detection and signal expression of biological signals and information in the form of expression and change rules (self-change and changes after medical intervention), the obtained data and information Analysis, decision-making and many other aspects have very complex nonlinear connections. Therefore, the use of traditional data matching can only be blind data screening, unable to...

AI Technical Summary

Problems solved by technology

However, due to the complexity and unpredictability of the human body and diseases, the detection and signal expression of biological signals and information in the form of expression and change law (self-change and change after medical intervention), the acquired data and information There are very complex nonlinear relationships in analysis, decision-making and many other aspects

Therefore, the use of traditional data matching can only be blind data screening, unable to judge the logical relationship between data and variables, and the obtained value range deviation is large, resulting in very poor specificity of system prediction, so the current domestic health management The system cannot effectively predict an individual's sleep disturbance accurately

[0003] Previously, most sleep disturbance predictions used the BP neural network model, but when new detection data is generated, the neural network model must be trained again, and the calculation efficiency is extremely low.

And when the scale of system users increases, the server will not be able to complete the training tasks in time

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