Brain wave medical record signal reduction method based on three-layer cross game energy trees

Abstract

The invention discloses a brain wave medical record signal reduction method based on three-layer cross game energy trees, the brain wave medical record signal reduction method includes designing two adjacent three-layer cross game trees Ti and Tj, realizing self game in a same main layer and cross coordinated game among different main layers, and adopting a depth-first search strategy to select global optimization elites Elitisti-c and Elitistj-c; distributing elite energy gain matrix PMENnxn to optimal elite, and enhancing performance, on acquiring self-adaptive Nash energy exploration and development balance, of elite on the main layers; realizing cross game energy reduction of brain wave medical record signals, and enabling the optimal elite driven by elite energy vector Gamma to converge to optimal Nash balance points of each main layer to realize brain wave medical record signal reduction accuracy balance so as to output global optimal brain wave medical record signals. By the brain wave medical record signal reduction method, brain wave medical record signal reduction efficiency can be improved effectively, and an important method is provided for brain wave noise elimination and feature extraction and analysis; the brain wave medical record signal reduction method has high application value in brain disease diagnosis and adjuvant therapy.

Description

Technical field: [0001] The invention relates to the field of biomedicine, in particular to a brainwave medical record signal reduction method based on a three-layer cross-game energy tree. Background technique: [0002] The brain wave signal in the electronic medical record is a waveform with a potential of about millivolts on the cerebral cortex under the scalp detected by medical equipment. delta and theta waves are slow waves, and beta and gamma waves are fast waves. Brain wave signals have certain regularity. When the brain, especially the cortex, has lesions, the regularity of brain waves will be destroyed, which will cause abnormal brain waves, and the waveform will change. Department of disease diagnosis. However, because the brain wave signal waveform detected in the electronic medical record system is a function of time, it is constantly updated and dynamically changed, and has a strong uncertainty of timeliness. The potential of the brain wave will suddenly dro...

AI Technical Summary

Problems solved by technology

[0003] At present, Fourier transform, frequency domain energy analysis, wavelet transform, fast independent component analysis and other methods are generally used to improve the shortcomings of brain wave medical record signals such as uncertainty, timeliness, weakness, diversity and nonlinearity, but the brain wave medical records obtained by these methods The signal prevents a lot of information from the original brainwave signal from being retained, and it is very likely that the useful brainwave signal will be suppressed or processed as noise, which indirectly changes the characteristics of the brainwave signal, making it impossible for us to determine the frequency of the brainwave in the electronic medical record system. , amplitude, quantity, location, appearance, pathological waves, etc. for effective analysis

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