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Detailed classifying system for sleep incubation period

A technology for fine-grained classification and light sleep, applied in the sleep field, can solve the problems that ordinary consumers cannot use conveniently, multi-leads are sensitive to paste, and sensors are easy to fall off, so as to ensure the stability of hardware operation, facilitate wear and use, and improve data quality. The effect of accuracy

Inactive Publication Date: 2019-12-20
XILINMEN FURNITURE
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, since there are many monitoring parameters when using a polysomnography monitor, more sensors need to be used, and most of these sensors are attached to the user's head and torso, so in the monitoring process there are monitoring environment discomfort, many Leads are sensitive to sticking, sensors are easy to fall off, etc. Although portable sleep monitors overcome the shortcomings of polysomnography with few leads and easy to carry, due to their high power supply requirements, high prices, precise requirements for electrode points and Many operating steps and other factors make it difficult for ordinary consumers to use it conveniently, which in turn affects the promotion and application in the civilian market

Method used

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  • Detailed classifying system for sleep incubation period
  • Detailed classifying system for sleep incubation period
  • Detailed classifying system for sleep incubation period

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Experimental program
Comparison scheme
Effect test

Embodiment 2

[0097] Compared with the first embodiment, this embodiment provides another decision tree structure.

[0098] In actual operation, at the first node, the threshold Num-LCZ-Threshold is set. When Num-LCZ > Num-LCZ-Threshold, the single-frame data packet is determined as an artificial artifact, and is sent to the processor by the processor. The external output is the sleep state obtained when processing the last single frame of data packet, otherwise, it transfers to the second node. When artificial artifacts appear, the number of times that the waveform crosses the x-axis in each waveform graph will increase significantly. By comparing the value of the parameter Num-LCZ with the corresponding threshold Num-LCZ-Threshold to determine whether artificial artifacts are generated, specifically , when Num-LCZ>Num-LCZ-Threshold, the single-frame data packet is determined to be an artificial artifact, the single-frame data packet is invalid, and the sleep state obtained when the previo...

Embodiment 3

[0101] Compared with the first embodiment, this embodiment provides another decision tree structure.

[0102] At the second node, the threshold value BVS-Threshold is set. When BVS>BVS-Threshold, the processor determines that the user is in the sleep-onset latency or rapid eye movement period, and transfers to the fifth node. Otherwise, the processor determines that the user is in light sleep Period, light sleep period or deep sleep period, transfer to the third node. When BVS>BVS-Threshold, it means that the amplitude ratio of the alpha wave signal or beta wave signal in the single-frame data packet is stronger, which means that the user's brain is active and in the REM state or the awake state. On the contrary, it means that the amplitude ratio of the alpha wave signal and the beta wave signal in the single-frame data packet is weaker, which means that the user's brain is inactive and is in a light sleep state, a light sleep state, or a deeper sleep state.

[0103] Other fe...

Embodiment 4

[0105] Compared with the first embodiment, this embodiment provides another decision tree structure.

[0106] Set the threshold TVB-Threshold and Num-EOG-Threshold. When TVB>TVA-Threshold and Num-EOG>Num-EOG-Threshold, the processor determines that the user is in the rapid eye movement period, the system stops working, and the single The frame data packet is recorded as a sleep frame data packet, otherwise, the processor determines that the user is in the sleep latency period, and transfers to the sixth node. When TVB>TVB-Threshold, and Num-EOG>Num-EOG-Threshold, the processor determines that the user's brain thinking is in the REM state with theta wave components and irregular eye movements, ends the sleep detection, and records the single The frame data packet is a sleep frame data packet. On the contrary, it means that the user's brain is in a awake state, and it is transferred to the T6 node for further analysis and processing.

[0107] Other features of the sleep-onset ...

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Abstract

The invention relates to a detailed classifying system for sleep incubation period. Existing detailed classifying systems for sleep incubation period are complex in calculation process and low in classification precision. The system comprises a processor and a basic data collector, the processor collects the basic data of a user through the basic data collector, and acquires the detailed sleep state of the user in the sleep incubation period through calculation. The system can provide an operation basis for subsequent sleep aiding equipment, simplify the system structure, improve the calculation efficiency and operation energy consumption, improve the wearing comfort, and improve the use experience.

Description

technical field [0001] The invention relates to the field of sleep, in particular to a sleep-onset latency refinement classification system. Background technique [0002] The quality of sleep directly affects people's health and even life expectancy, and more and more people have sleep disorders due to the accelerated pace of life. Medical research has shown that occasional insomnia can cause fatigue and uncoordinated movements the next day, while long-term insomnia can lead to inability to concentrate, memory impairment, and inability to work. Specifically, the symptom of insomnia is difficulty falling asleep. In order to improve the quality and efficiency of sleep, EEG bioelectric signals are collected through professional sleep aid detection equipment, and then various characteristic parameters of EEG bioelectric signals are extracted through different analysis methods. The classification algorithm is used to identify the sleep state of each stage of sleep, and then to i...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/0496A61B5/0476A61B5/00
CPCA61B5/4806A61B5/4812A61B5/6803A61B5/7203A61B5/725A61B5/369A61B5/398
Inventor 李建军段韩路
Owner XILINMEN FURNITURE
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