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Brain wave analysis method and system

An analysis method and analysis system technology, applied in electrical digital data processing, medical automatic diagnosis, sensors, etc., can solve the problem of inability to accurately divide the sleep stages of the human body, and achieve the effect of preserving volatility

Active Publication Date: 2017-05-31
TSINGHUA UNIV
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] Based on this, it is necessary to provide a brain wave analysis method and system for the problem that the traditional analysis method of brain wave cannot accurately divide the sleep stages of the human body, wherein the method includes:

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  • Brain wave analysis method and system

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Embodiment Construction

[0052] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0053] figure 1 It is a schematic flow diagram of the brain wave analysis method in an embodiment, such as figure 1 The brainwave analysis methods shown include:

[0054] Step S100, acquiring the original brain wave sequence.

[0055] Specifically, the original brain wave sequence, for example image 3 as shown, image 3 It is the original trend chart of brain waves in the REM period, with large data fluctuations and various frequency components, from which it is impossible to directly analyze the required dynamic fluctuation information of brain waves, which is used to describe the dynamic cha...

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Abstract

The invention relates to a brain wave analysis method and system. The method comprises the steps of obtaining an original brain wave sequence; determining scales according to preset scale interval and scale step length of the original brain wave sequence; calculating coarse-grained high-order moment brain wave sequences at various scales according to the scales, the original brain wave sequence and a preset high-order moment order, wherein the preset high-order moment order is greater than 2; calculating a high-order moment brain wave information entropies at various scales according to the coarse-grained high-order moment brain wave sequences under various scales and a high-order moment information entropy algorithm and obtaining a multi-scale high-order moment information entropy set of the original brain wave; and determining dynamic change information of the original brain wave sequence according to the multi-scale high-order moment information entropy set. According to the brain wave analysis method and device, the problem of overlarge volatility loss in the sequence coarse graining process caused by a mean value calculation method in a traditional brain wave analysis method is solved.

Description

technical field [0001] The invention relates to the technical field of sleep analysis, in particular to a brain wave analysis method and system. Background technique [0002] Good quality sleep is essential for the good functioning of everyday life, such as mental health, creativity, and work performance. Insufficient or ineffective sleep can lead to daytime sleepiness, irritability, emotional distress, depression or anxiety, and even increase accident rates . The EEG represents a wealth of information about brain activity, and sleep / wake is divided into three stages: wakefulness, non-REM sleep, and REM sleep. Understanding the structural changes in sleep during the sleep cycle is crucial. [0003] However, due to the highly complex nature of the EEG signal, both the amplitude and the frequency of the signal exhibit nonlinear and non-stationary patterns, and the determination of each sleep stage in the field of sleep research is a challenge. In the traditional EEG algorith...

Claims

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Application Information

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IPC IPC(8): G06F19/00A61B5/00
CPCA61B5/4812G16H50/20
Inventor 史文彬叶建宏洪阳朱仪芳
Owner TSINGHUA UNIV
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