Intelligent sleep phase detection and sleep quality evaluation system and method

Abstract

The invention belongs to the technical field of sleep state monitoring, and discloses an intelligent sleep phase detection and sleep quality evaluation system and method. The intelligent sleep phase detection and sleep quality evaluation system comprises an eeg signal acquisition module, a background server and an on-line service platform, wherein the eeg signal acquisition module is used for acquiring sleep eeg data, and is used for completing acquisition of the sleep eeg data through eeg signal detecting components; the background server is used for receiving acquired original sleep eeg datato complete the treatment of sleep eeg signals, so that sleep eeg automatic phase separating is realized, and sleep quality is evaluated; and the on-line service platform is used for tracking the sleep trend of a user and providing services including sleep psychological consultation, expert suggestion and the like. The intelligent sleep phase detection and sleep quality evaluation method comprises the following steps of obtaining an eeg signal, performing pretreatment, performing feature extraction, performing mode recognition and classification and outputting the sleep phase. The evaluationresult can be pushed to a mobile terminal so that professional sleep psychological consultation and expert advice are provided for the user, are completely independent of the working experience of anoperator, and can be used as beneficial supplement and explanation for clinical diagnosis.

Description

Technical field [0001] The invention belongs to the technical field of sleep state monitoring, and in particular relates to an intelligent sleep phase detection and sleep quality assessment system and method. Background technique [0002] Currently, the closest existing technology: [0003] In the field of sleep medicine, polysomnography (PSG) is a commonly used sleep phase detection and sleep quality assessment standard. It can simultaneously collect multi-conductive physiological signals such as brain electricity (EEG), eye movement (EOG), heart rhythm (ECG), muscle activity (EMG), and integrate the above signals to achieve sleep quality assessment. PSG can exclude or diagnose sleep disorders such as narcolepsy, pathological narcolepsy, periodic limb movement disorders, insomnia and sleep apnea. Although the advantages of PSG are clear, it also has application limitations. Patients must be monitored in dedicated clinics. For patients whose sleep quality is not high, adaptabili...

AI Technical Summary

Problems solved by technology

[0062] (1) Polysomnography analysis has clear requirements for monitoring locations, requires professional collection equipment and a fixed experimental environment, which is not conducive to the collection of patient data

Wearing more biosensors makes the subjects feel uncomfortable and affects their normal sleep activities

There are certain requirements for the technical level of the operator, and the equipment debugging is time-consuming. The medical staff is mainly based on stages, and the work experience of the operator is overly dependent, which is easy to increase the workload

[0063] (2) Invasive commercial monitoring equipment requires polysomnography as the analysis background, and is not conducive to the health of the subjects

Some devices, while effective for sleep monitoring, are not suitable for determining wake-up time in patients with mild sleep quality

Other devices that rely on environmental data for sleep quality assessment do not fully consider the patient's own physiological signals, and the accuracy is not high

[0064] (3) The sleep quality monitoring program developed by using mobile phone sensors not only has restrictions on the mobile phone brand and hardware configuration, but also has regulations on the monitoring duration, and the time is too short to meet the data collection requirements

Although some commercial monitoring equipment can replace PSG for data collection, they do not fully consider the patient's own physiological signals, and the monitoring accuracy is not enough to meet medical standards

The sleep monitoring program developed using mobile phone sensors not only has requirements for mobile phone brands and hardware configurations, but also refers too much to the psychological scale method when analyzing sleep quality, so a large number of subjective factors are introduced, which affects the objectivity of the analysis.

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