WO2018053967A1 - 催眠深度检测器 - Google Patents

催眠深度检测器 Download PDF

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Publication number
WO2018053967A1
WO2018053967A1 PCT/CN2016/113139 CN2016113139W WO2018053967A1 WO 2018053967 A1 WO2018053967 A1 WO 2018053967A1 CN 2016113139 W CN2016113139 W CN 2016113139W WO 2018053967 A1 WO2018053967 A1 WO 2018053967A1
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signal
user
depth
area
hypnotic
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PCT/CN2016/113139
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English (en)
French (fr)
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赵巍
胡静
韩志
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广州视源电子科技股份有限公司
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/369—Electroencephalography [EEG]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/1103—Detecting eye twinkling
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/1118—Determining activity level
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/398—Electrooculography [EOG], e.g. detecting nystagmus; Electroretinography [ERG]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4806—Sleep evaluation
    • A61B5/4809—Sleep detection, i.e. determining whether a subject is asleep or not
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7225—Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
    • A61M21/00—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis
    • A61M21/02—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis for inducing sleep or relaxation, e.g. by direct nerve stimulation, hypnosis, analgesia
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
    • A61M21/00—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis
    • A61M2021/0005—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis by the use of a particular sense, or stimulus
    • A61M2021/0027—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis by the use of a particular sense, or stimulus by the hearing sense

Definitions

  • the present invention relates to the field of assisted sleep technology, and more particularly to a hypnosis depth detector.
  • Intelligent assisted sleep is a sleep method that combines modern technology.
  • his suggestiveness is significantly improved, and he can maintain a close sensory relationship with the hypnotist, and will accept his suggestive instructions without criticism.
  • hypnotism is applied to assist sleep, when the hypnotist is hypnotized by the hypnotist, the hypnotist issues a sleep command to put the hypnotized person to sleep.
  • Hypnotic-based assisted sleep has fewer side effects on the body than drug interventions (hypnotics) and is more suitable for everyday use.
  • hypnosis depth In intelligent assisted sleep, how to accurately identify hypnosis depth is an important factor. Different hypnosis strategies are needed at different hypnotic depths to guide users into sleep. At present, for hypnosis depth, it can be generally divided into three hypnotic depths. Stage and six levels: mild hypnosis (1-2), moderate hypnosis (3-4) and deep hypnosis (5-6). For each level of hypnosis depth, there is a corresponding judgment standard, which is judged according to the behavior characteristics of the hypnotized person. For intelligent assisted sleep by hypnosis, correct recognition is the premise of performing the next stage of hypnosis. However, this generally requires the hypnotist to judge based on sufficient experience. It is difficult for the average user to judge. At the same time, these perception methods identify the depth of hypnosis, the accuracy is difficult to guarantee, and the efficiency is low.
  • a hypnosis depth detector comprising: an electroencephalogram electrode, an electrooculogram electrode, a reference electrode, an analog to digital converter, a filter circuit, an acceleration sensor, a speaker, and a processor;
  • the EEG electrode, the EEG electrode, and the reference electrode are respectively connected to an analog-to-digital converter, and are sequentially connected to the processor through the analog-to-digital converter and the filter circuit, and the acceleration sensor is connected to the processor;
  • the electroencephalogram electrode is configured to detect an electroencephalogram signal of a user during sleep; the electro-oculogram electrode is configured to collect an electro-oculogram signal of a user during sleep; and the accelerometer sensor is configured to detect an action signal generated by a reaction of a user arm; The speaker is used to play a sound to the user;
  • the analog-to-digital converter converts an ocular electrical signal and an electroencephalogram signal into a digital signal, and the filtering circuit performs low-frequency filtering on the ocular electrical signal and the electroencephalogram signal, and then inputs the signal to the processor;
  • the processor is configured to determine, according to the EEG signal, an ocular electrical signal, and an action signal, a depth of hypnosis currently in which the user is located; wherein the hypnotic depth includes a first-level hypnotic depth, a second hypnotic depth, and a third hypnosis depth.
  • the above hypnotic depth detector detects the user's eye electrical signal and uses the detection window to identify during the playing of the hypnotic guiding word.
  • the acceleration sensor detects the motion signal of the user arm to identify the second Level hypnosis depth, and then use the auditory stimulation test based on the EEG signal to identify the third-level hypnotic depth, thus achieving the recognition of the three-level hypnotic depth, which can improve the recognition accuracy and improve the recognition efficiency, providing the next stage of hypnosis.
  • FIG. 1 is a schematic structural view of a hypnosis depth detector of an embodiment
  • Figure 3 is a schematic diagram of an ocular electrical signal waveform
  • FIG. 4 is a schematic view showing a peak area of an ocular electrical signal waveform in a detection window
  • Figure 5 is a waveform diagram of an EEG signal detected after multiple target stimulation
  • Fig. 6 is a waveform diagram of the superimposed average of the EEG signal waveforms.
  • FIG. 1 is a schematic structural diagram of a hypnosis depth detector according to an embodiment, including: an electroencephalogram electrode, an electro-oculogram electrode, a reference electrode, an analog-to-digital converter, a filter circuit, an acceleration sensor, a speaker, and a processor;
  • the EEG electrode, the EEG electrode, and the reference electrode are respectively connected to an analog-to-digital converter, and are sequentially connected to the processor through the analog-to-digital converter and the filter circuit, and the acceleration sensor is connected to the processor;
  • the electroencephalogram electrode is used for detecting an EEG signal of a user during sleep; the electro-oculogram electrode is used for collecting a user in sleep An EOG signal; the acceleration sensor is configured to detect an action signal generated by a user's arm reaction; and the speaker is used to play a sound to a user;
  • the analog-to-digital converter converts an ocular electrical signal and an electroencephalogram signal into a digital signal, and the filtering circuit performs low-frequency filtering on the ocular electrical signal and the electroencephalogram signal, and then inputs the signal to the processor;
  • the processor is configured to determine, according to the EEG signal, an ocular electrical signal, and an action signal, a depth of hypnosis currently in which the user is located; wherein the hypnotic depth includes a first-level hypnotic depth, a second hypnotic depth, and a third hypnosis depth.
  • the hypnosis depth detector of the above embodiment detects the EOG signal of the user and uses the detection window to identify during the playing of the hypnotic guiding word. After identifying the first-level hypnotic depth, the acceleration sensor detects the motion signal of the user's arm. Identifying the second-level hypnotic depth, and then using the auditory stimulation test based on the EEG signal to identify the third-level hypnotic depth, thereby achieving the recognition of the three-level hypnotic depth, which can improve the recognition accuracy and improve the recognition efficiency for the next execution. Stage hypnosis provides a good reference.
  • FIG. 2 is an algorithm architecture diagram inside the processor, the processor
  • the first level hypnosis depth recognition module, the second level hypnosis depth recognition module and the third level hypnosis depth recognition module are respectively configured with three algorithm modules; respectively, for identifying a level of hypnosis depth.
  • the first stage hypnosis depth identification module is configured to play a hypnotic guide word to the user in the smart assisted sleep, detect the eye electric signal of the user, and obtain a corresponding waveform of the electrooculogram signal; after playing the hypnotic guide word In the first time period, the waveform of the electrooculogram is detected by using a preset detection window, and if the amplitude of the waveform of the electrooculogram does not exceed the height threshold of the detection window, it is determined that the user is currently in the a first stage hypnosis depth state; wherein the detection window includes a set detection window length and a height threshold.
  • the second stage hypnosis depth identification module is configured to play an arm lifting command to the user, and detect an action of the user reaction by using an acceleration sensor fixed on the user arm in a second time period after the arm raising command is played. a signal; if the acceleration sensor does not output an action signal corresponding to the arm lifting command during the second time period, determining that the user is currently in a second-level hypnotic depth state;
  • the third stage hypnosis depth identification module is configured to play a target stimulation signal to the user, and detect an EEG signal of the user in a third time period after the target stimulation; if the EEG signal appears positive within a specified time range To the waveform, it is determined that the user is currently in the third level hypnosis depth state.
  • the present invention provides a hypnotic depth detector, the brain electrical electrode being disposed on a forehead of a user
  • the reference electrode is disposed at a user's earlobe; the electro-oculogram electrode is disposed at an eye corner position; and the acceleration sensor is disposed at a user's arm position.
  • the electroencephalogram electrode is "M” in the figure
  • the electrooculogram electrode includes two electrodes on the left and the right, that is, "ROC” and “LOC” in the figure
  • the reference electrode is disposed on the earlobe of the user, that is, In the figure, "R” and “L”
  • the acceleration sensor is "AT” in the figure.
  • the filter circuit mainly performs low-pass filtering and filtering power frequency interference. In order to adapt to the processing of the EEG signal and the EOG signal, the filter circuit filters the signal of the 0-256 Hz band to the processor.
  • the main function analysis can be as follows:
  • the user when the user is not asleep, the user performs intelligent assisted sleep, plays a hypnotic guide word to the user, performs sleep intervention on the user, uses the electro-optical electrode, detects the user's EO signal, and sends it to the processor to draw the corresponding eye.
  • Electrical signal waveform diagram when the user is not asleep, the user performs intelligent assisted sleep, plays a hypnotic guide word to the user, performs sleep intervention on the user, uses the electro-optical electrode, detects the user's EO signal, and sends it to the processor to draw the corresponding eye.
  • Electrical signal waveform diagram when the user is not asleep, the user performs intelligent assisted sleep, plays a hypnotic guide word to the user, performs sleep intervention on the user, uses the electro-optical electrode, detects the user's EO signal, and sends it to the processor to draw the corresponding eye. Electrical signal waveform diagram.
  • the processor may draw a corresponding EOG waveform diagram on the time-magnification coordinate system; the coordinate system may take the time as the horizontal axis and the EOG signal amplitude. The value is the vertical axis.
  • the processor may also detect the sleep state of the user before playing the hypnotic guide word to the user, and then start the hypnosis intervention and recognition process when detecting that the user is not asleep.
  • the detection window After playing the hypnosis guide word to intervene, use the detection window to move in the time axis direction within the set time (generally 30s), and move the detection of the EE signal waveform to detect the eyelid activity, when the EOG waveform is If the value does not exceed the height threshold, that is, the amplitude of the amplitude of the EOG waveform in the window is not detected to exceed the height threshold, it is determined that the user is currently in the first level hypnosis depth state.
  • a detection window length and a height threshold that is, to establish a detection window moving along the time axis direction on the time-amplitude coordinate system, and according to the blink speed and the EO signal Amplitude Set the detection window length and height threshold.
  • the specific setting process can include the following:
  • the blink speed is [Ta, Tb], the detection window length is ⁇ 2Ta; the EO signal amplitude is M, and the height threshold is ⁇ 0.7M.
  • FIG. 3 is a schematic diagram of an EE signal waveform.
  • the detection window length can be set to 0.6 s
  • the height threshold is Can be set to 140uV.
  • the processor can further judge the sharpness of the waveform to improve the recognition accuracy.
  • the sharpness parameter of the peak of the EE signal waveform in the detection window can be calculated by the following formula:
  • p i is the ocular electrical signal in the detection window
  • p max is the maximum value of the ocular electrical signal in the detection window
  • p min is the minimum value of the ocular electrical signal in the detection window
  • area up indicates the area of the upper region
  • area down indicates Area of the lower area
  • the blink area indicates the area of the peak, and if indicates that the condition is satisfied
  • FIG. 4 is a schematic diagram showing the peak area of the EE signal waveform in the detection window.
  • the area of the upper and lower areas of the peaks in the two directions is as shown in the figure, the area 1 indicates the area of the upper area, and the area 2 indicates the area of the lower area;
  • the peak direction of the graph is upward, and the peak of the right graph is downward.
  • Blink ratio blink area /in-blink area
  • the blink ratio represents the sharpness parameter
  • the in-blink area represents the area of the non-spike portion
  • the main function analysis can be as follows:
  • the user After detecting the first-level hypnotic depth, the user raises the arm lifting command to perform the first-level hypnotic depth detection, detects that the user's large muscle group is impliedly manipulated, and uses the acceleration sensor to detect the arm's motion signal.
  • the acceleration sensor does not output an action signal corresponding to the arm lift command during the second time period (generally 10 s), for example, or an action signal generated by the corresponding motion sense rhythm or other arm motion, the user may be considered to reach the second level hypnosis depth.
  • the main function analysis can be as follows:
  • Detection can be performed in oddball mode using the P300 signal based on auditory stimulation. For example, in the hypnotic content, the user is prompted by the digital block, the user pays attention to the target stimulus in the music to be played, then starts playing the music, and inserts the target stimulus multiple times (for example, 15 times) while playing the music, and finally more
  • the EEG signals in the third time period (generally 600 ms) after the occurrence of the sub-target stimulation are superimposed and the average value is calculated.
  • FIG. 5 is a waveform diagram of an EEG signal detected after multiple target stimulations; a portion of the superimposed image intercepted in the dotted line frame is superimposed and averaged, as shown in FIG. 6, and FIG. 6 is an EEG signal. The waveform is superimposed on the average waveform.
  • the processor detects the user's eye electric signal and uses the detecting window to identify, and after identifying the first level hypnotic depth, the acceleration sensor is used to detect the user's arm motion signal. Identifying the second-level hypnotic depth, and then using the auditory stimulation test to identify the EEG signal to determine the third-level hypnotic depth, thereby achieving the recognition of the third-level hypnotic depth, which can improve the recognition accuracy and improve the recognition efficiency for the next execution. Stage hypnosis provides a good reference.

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Abstract

一种催眠深度检测器，包括：脑电电极、眼电电极、参考电极以及依次连接的模数转换器、滤波电路和处理器，加速度传感器连接处理器；脑电电极用于检测用户在睡眠中的脑电信号；眼电电极用于采集用户在睡眠中的眼电信号；加速度传感器用于检测用户手臂反应产生的动作信号；扬声器用于向用户播放声音；模数转换器将眼电信号和脑电信号转换为数字信号，滤波电路对眼电信号和脑电信号进行低频滤波后输入至处理器；处理器用于根据脑电信号、眼电信号以及动作信号判断用户当前所处的催眠深度；其中，催眠深度包括第一级催眠深度、第二催眠深度和第三催眠深度。从而能够提高识别准确性和识别效率。

Description

催眠深度检测器 技术领域
本发明涉及辅助睡眠技术领域，特别是涉及一种催眠深度检测器。
背景技术
在睡眠中，人体进行了自我放松及恢复的过程。因此良好的睡眠是保持身体健康的一项基本条件。但是由于工作压力大、生活作息不规律等原因，导致了部分人群的睡眠质量欠佳，表现为失眠、半夜惊醒等。
智能辅助睡眠是一种结合现代科技的睡眠方法，当被试者进入催眠状态后，其受暗示性明显提高，能与催眠师保持密切的感应关系，会不加批判地接受其暗示指示。将催眠术应用于辅助睡眠时，当催眠者被催眠师所催眠后，催眠师发出睡眠指令即可使被催眠者进入睡眠状态。与药物干预(安眠药)相比，基于催眠术的辅助睡眠对身体的副作用较小，比较适合日常应用。
在智能辅助睡眠中，如何准确地识别催眠深度是重要因素，在不同的催眠深度需要进行不同的催眠策略，引导用户进入睡眠，目前对于催眠深度，一般可以分为，催眠深度一般可分成三个阶段和六个等级：轻度催眠(1-2级)，中度催眠(3-4级)和深度催眠(5-6)级。对于每个等级的催眠深度而言，有相应的判断标准，根据被催眠者的表现出来的动作特征进行评判，对于通过催眠来智能辅助睡眠而言，正确的识别是执行下一阶段催眠的前提，但这一般需要催眠师依据足够经验才能进行判断，对于一般用户而言则难以判断，同时这些觉察方式识别催眠深度，准确性难以保证，效率低。
发明内容
基于此，有必要针对上述问题，提供一种催眠深度检测器，能够较为准确地识别用户的催眠深度，有效地提高辅助睡眠效果。
一种催眠深度检测器，包括：脑电电极、眼电电极、参考电极、模数转换器、滤波电路、加速度传感器、扬声器以及处理器；
所述脑电电极、眼电电极、参考电极分别连接模数转换器，并依次通过所述模数转换器和滤波电路连接至处理器，所述加速度传感器连接处理器；
所述脑电电极用于检测用户在睡眠中的脑电信号；所述眼电电极用于采集用户在睡眠中的眼电信号；所述加速度传感器用于检测用户手臂反应产生的动作信号；所述扬声器用于向用户播放声音；
所述模数转换器将眼电信号和脑电信号转换为数字信号，所述滤波电路对眼电信号和脑电信号进行低频滤波后输入至处理器；
所述处理器，用于根据所述脑电信号、眼电信号以及动作信号判断用户当前所处的催眠深度；其中，所述催眠深度包括第一级催眠深度、第二催眠深度和第三催眠深度。
上述催眠深度检测器，在播放催眠引导词过程中，通过检测用户的眼电信号并利用检测窗口进行识别，在识别第一级催眠深度后，利用加速度传感器检测用户手臂的动作信号，识别第二级催眠深度，然后利用基于听觉刺激测试，利用脑电信号识别第三级催眠深度，从而实现三级催眠深度的识别，能够提高识别准确性，而且提高了识别效率，为执行下一阶段催眠提供了良好的参考。
附图说明
图1为一个实施例的催眠深度检测器的结构示意图；
图2是处理器内部的算法架构图；
图3为一个眼电信号波形示意图；
图4为检测窗口内眼电信号波形尖峰面积示意图；
图5为多次靶刺激后检测的脑电信号波形图；
图6为脑电信号波形图叠加平均后的波形图。
具体实施方式
下面结合附图阐述本发明的催眠深度检测器的实施例。
参考图1所示，图1为一个实施例的催眠深度检测器的结构示意图，包括：脑电电极、眼电电极、参考电极、模数转换器、滤波电路、加速度传感器、扬声器以及处理器；
所述脑电电极、眼电电极、参考电极分别连接模数转换器，并依次通过所述模数转换器和滤波电路连接至处理器，所述加速度传感器连接处理器；
所述脑电电极用于检测用户在睡眠中的脑电信号；所述眼电电极用于采集用户在睡眠 中的眼电信号；所述加速度传感器用于检测用户手臂反应产生的动作信号；所述扬声器用于向用户播放声音；
所述模数转换器将眼电信号和脑电信号转换为数字信号，所述滤波电路对眼电信号和脑电信号进行低频滤波后输入至处理器；
所述处理器，用于根据所述脑电信号、眼电信号以及动作信号判断用户当前所处的催眠深度；其中，所述催眠深度包括第一级催眠深度、第二催眠深度和第三催眠深度。
上述实施例的催眠深度检测器，在播放催眠引导词过程中，通过检测用户的眼电信号并利用检测窗口进行识别，在识别第一级催眠深度后，利用加速度传感器检测用户手臂的动作信号，识别第二级催眠深度，然后利用基于听觉刺激测试，利用脑电信号识别第三级催眠深度，从而实现三级催眠深度的识别，能够提高识别准确性，而且提高了识别效率，为执行下一阶段催眠提供了良好的参考。
对于催眠深度检测，主要通过处理器来进行识别，基于处理器实现的功能，可以在处理器中配置相应的算法模块，参考图2所示，图2是处理器内部的算法架构图，处理器中配置有第一级催眠深度识别模块、第二级催眠深度识别模块和第三级催眠深度识别模块三个算法模块；分别用于识别一个等级的催眠深度。
所述第一级催眠深度识别模块，用于在智能辅助睡眠中，向用户播放催眠引导词，检测所述用户的眼电信号并获取对应的眼电信号波形图；在播放催眠引导词后的第一时间段内，利用预设的检测窗口移动检测所述眼电信号波形图，若所述眼电信号波形图的幅值没有超过所述检测窗口的高度阈值，则判定所述用户当前处于第一级催眠深度状态；其中，所述检测窗口包括设定的检测窗口长度和高度阈值。
所述第二级催眠深度识别模块，用于向用户播放手臂抬起命令，在播放手臂抬起命令后的第二时间段内，利用固定在所述用户手臂上的加速度传感器检测用户反应的动作信号；若在所述第二时间段内，所述加速度传感器没有输出与所述手臂抬起命令对应的动作信号，则判定所述用户当前处于第二级催眠深度状态；
所述第三级催眠深度识别模块，用于向用户播放靶刺激信号，检测靶刺激后的第三时间段内所述用户的脑电信号；若所述脑电信号在指定时间范围内出现正向波形，则判定所述用户当前处于第三级催眠深度状态。
在一个实施例中，本发明提供的催眠深度检测器，所述脑电电极设置在用户的额头位 置；所述参考电极设置在用户的耳垂；所述眼电电极设置在眼角位置；所述加速度传感器设置在用户的手臂位置。如图1所示，图中，脑电电极即图中的“M”，眼电电极包括左右两个电极，即图中的“ROC”和“LOC”，参考电极设置在用户的耳垂，即图中“R”和“L”，加速度传感器即图中“AT”。滤波电路主要是进行低通滤波和滤除工频干扰，为了适应于脑电信号和眼电信号的处理，滤波电路滤波后，输出0-256Hz频段的信号至处理器。
对于处理器的第一级催眠深度识别模块，其主要功能解析可以如下：
(1)在智能辅助睡眠中，向用户播放催眠引导词，检测所述用户的眼电信号并获取对应的眼电信号波形图；
一般是用户在未睡着的情况下，进行智能辅助睡眠，向用户播放催眠引导词，以对用户进行睡眠干预，利用眼电电极，检测用户的眼电信号，发送至处理器绘制对应的眼电信号波形图。
作为一个实施例，可以在眼电电极检测到眼电信号后，处理器在时间-幅值坐标系上绘制对应的眼电信号波形图；坐标系可以以时间为横轴，以眼电信号幅值为纵轴。
另外，处理器还可以在向用户播放催眠引导词前，对用户的睡眠状态进行检测，在检测到用户是未睡着状态时，再启动催眠干预和识别流程。
即在检测所述用户的眼电信号前，采集用户在智能辅助睡眠过程中产生的脑电信号；根据所述脑电信号对用户的睡眠状态进行检测，当所述用户处于未睡着状态，执行所述向用户播放催眠引导词的步骤。
(2)在播放催眠引导词后的第一时间段内，利用预设的检测窗口移动检测所述眼电信号波形图，若所述眼电信号波形图的幅值没有超过所述检测窗口的高度阈值，则判定所述用户当前处于第一级催眠深度状态；其中，所述检测窗口包括设定的检测窗口长度和高度阈值；
在播放催眠引导词进行干预后，在设定时间(一般取30s)内利用检测窗口按时间轴方向移动，移动检测眼电信号波形图，以检测眼皮活动情况，当眼电信号波形图的幅值没有超过所述高度阈值，即没有检测到窗口内的眼电信号波形幅值波动幅度超过高度阈值，则判定所述用户当前处于第一级催眠深度状态。
在一个实施例中，对于检测窗口的选取，需要设置检测窗口长度和高度阈值，即在所述时间-幅值坐标系上建立沿时间轴方向移动的检测窗口，并根据眨眼速度和眼电信号幅值 设置所述检测窗口长度和高度阈值。
具体设置过程可以包括如下：
(a)在正常情况下，统计所述用户多次眨眼中快速的闭眼动作(眨眼，即瞬目反射)的时间；根据所述统计的时间获取所述用户的眨眼速度；
(b)统计所述用户多次眨眼中的眼电信号幅值的最大值或最小值；根据所述眼电信号幅值的最大值或最小值获取所述用户的眼电信号幅值；
(c)根据所述眨眼速度设置所述检测窗口长度，以及根据所述眼电信号幅值设置所述高度阈值。
其中，所述眨眼速度为[Ta，Tb]，所述检测窗口长度取值≥2Ta；所述眼电信号幅值为M，所述高度阈值取值≤0.7M。
例如，参考图3所示，图3为一个眼电信号波形示意图，当计算的眨眼速度为0.3s-0.4s，眼电信号幅值为200uV，则检测窗口长度可以设为0.6s，高度阈值可以设为140uV。
另外，考虑到眼电信号波形是判断眨眼的重要特征，在波形判断时，容易收到外界的干扰。因此，如果仅依赖于时间和幅值判断，容易导致误判，因此，处理器可以进一步对波形尖锐程度进行判断，以提高识别准确性。
即当检测到眼电信号波形图的幅值超过所述高度阈值时，计算检测窗口内眼电信号波形尖峰的尖锐程度参数，若所述尖锐程度参数小于预设的尖锐程度参数阈值，判定所述用户当前处于第一级催眠深度状态。
可以采用如下公式计算检测窗口内眼电信号波形尖峰的尖锐程度参数：
(a)分别计算眼电信号波形在检测窗口内的上部区域面积和下部区域面积，计算公式如下：
Figure PCTCN2016113139-appb-000001
Figure PCTCN2016113139-appb-000002
式中，pi为检测窗口内的眼电信号，pmax为检测窗口内眼电信号的最大值，pmin为检测窗口内眼电信号的最小值，areaup表示上部区域面积，areadown表示下部区域面积；
(b)根据所述上部区域面积和下部区域面积计算所述眼电信号波形尖峰的面积，计 算公式如下：
Figure PCTCN2016113139-appb-000003
式中，blinkarea表示尖峰的面积，if表示满足条件；
参考图4所示，图4为检测窗口内眼电信号波形尖峰面积示意图，两种方向的尖峰上、下部区域面积如图所示，①区表示上部区域面积，②区表示下部区域面积；左图尖峰方向向上，右图的尖峰方向向下。
(c)根据尖峰面积计算尖锐程度参数，计算公式如下：
blinkratio＝blinkarea/in-blinkarea
式中，blinkratio表示尖锐程度参数，in-blinkarea表示非尖峰部分的面积，这里尖锐程度参数也可以转化为是上部区域面积和下部区域面积之间的比值。
对于处理器的第二级催眠深度识别模块，其主要功能解析可以如下：
(1)向用户播放手臂抬起命令，在播放手臂抬起命令后的第二时间段内，利用固定在所述用户手臂上的加速度传感器检测用户反应的动作信号；
在检测到第一级催眠深度后，向用户播放手臂抬起命令，进行第一级催眠深度检测，检测用户的大肌肉群受到暗示被操控的情况，利用加速度传感器，检测手臂的动作信号。
(2)若在所述第二时间段内，所述加速度传感器没有输出与所述手臂抬起命令对应的动作信号，则判定所述用户当前处于第二级催眠深度状态；
如果加速度传感器在第二时间段(一般取10s)内没有输出与手臂抬起命令对应的动作信号，例如，或相应运动感觉节律或其他手臂动作产生的动作信号，可以认为用户达到第二级催眠深度。
对于处理器的第三级催眠深度识别模块，其主要功能解析可以如下：
(1)向用户播放多次插入靶刺激的音乐，检测每次靶刺激后的第三时间段内所述用户的脑电信号；
可以利用基于听觉刺激的P300信号，在oddball模式下进行检测。例如，在催眠内容中对用户进行数字阻滞的暗示，用户在即将播放的音乐中关注靶刺激，然后开始播放音乐，并在播放音乐时多次(例如15次)插入靶刺激，最后将多次靶刺激出现后的第三时间段(一般取600ms)内的脑电信号进行叠加并计算平均值。
参考图5所示，图5为多次靶刺激后检测的脑电信号波形图；虚线框内为截取的叠加图像部分，叠加求平均后，得到如图6所示，图6为脑电信号波形图叠加平均后的波形图。
(2)若所述脑电信号在指定时间范围内出现正向波形，则判定所述用户当前处于第三级催眠深度状态；
可以检测在300ms～500ms的时间范围内是否出现一个明显的正向波形，如图6所示，出现明显正向波形，则判定用户当前处于第三级的催眠深度，反之则没有达到。
综合上述实施例的方案，在播放催眠引导词过程中，处理器通过检测用户的眼电信号并利用检测窗口进行识别，在识别第一级催眠深度后，利用加速度传感器检测用户的手臂动作信号，识别第二级催眠深度，然后利用基于听觉刺激测试识别脑电信号来确定第三级催眠深度，从而实现三级催眠深度的识别，能够提高识别准确性，而且提高了识别效率，为执行下一阶段催眠提供了良好的参考。
以上所述实施例的各技术特征可以进行任意的组合，为使描述简洁，未对上述实施例中的各个技术特征所有可能的组合都进行描述，然而，只要这些技术特征的组合不存在矛盾，都应当认为是本说明书记载的范围。
以上所述实施例仅表达了本发明的几种实施方式，其描述较为具体和详细，但并不能因此而理解为对发明专利范围的限制。应当指出的是，对于本领域的普通技术人员来说，在不脱离本发明构思的前提下，还可以做出若干变形和改进，这些都属于本发明的保护范围。因此，本发明专利的保护范围应以所附权利要求为准。

Claims (10)

  1. 一种催眠深度检测器，其特征在于，包括：脑电电极、眼电电极、参考电极、模数转换器、滤波电路、加速度传感器、扬声器以及处理器；
    所述脑电电极、眼电电极、参考电极分别连接模数转换器，并依次通过所述模数转换器和滤波电路连接至处理器，所述加速度传感器连接处理器；
    所述脑电电极用于检测用户在睡眠中的脑电信号；所述眼电电极用于采集用户在睡眠中的眼电信号；所述加速度传感器用于检测用户手臂反应产生的动作信号；所述扬声器用于向用户播放声音；
    所述模数转换器将眼电信号和脑电信号转换为数字信号，所述滤波电路对眼电信号和脑电信号进行低频滤波后输入至处理器；
    所述处理器，用于根据所述脑电信号、眼电信号以及动作信号判断用户当前所处的催眠深度；其中，所述催眠深度包括第一级催眠深度、第二催眠深度和第三催眠深度。
  2. 根据权利要求1所述的催眠深度检测器，其特征在于，所述处理器配置有第一级催眠深度识别模块、第二级催眠深度识别模块和第三级催眠深度识别模块三个算法模块；
    所述第一级催眠深度识别模块，用于在智能辅助睡眠中，向用户播放催眠引导词，检测所述用户的眼电信号并获取对应的眼电信号波形图；在播放催眠引导词后的第一时间段内，利用预设的检测窗口移动检测所述眼电信号波形图，若所述眼电信号波形图的幅值没有超过所述检测窗口的高度阈值，则判定所述用户当前处于第一级催眠深度状态；其中，所述检测窗口包括设定的检测窗口长度和高度阈值。
    所述第二级催眠深度识别模块，用于向用户播放手臂抬起命令，在播放手臂抬起命令后的第二时间段内，利用固定在所述用户手臂上的加速度传感器检测用户反应的动作信号；若在所述第二时间段内，所述加速度传感器没有输出与所述手臂抬起命令对应的动作信号，则判定所述用户当前处于第二级催眠深度状态；
    所述第三级催眠深度识别模块，用于向用户播放靶刺激信号，检测靶刺激后的第三时间段内所述用户的脑电信号；若所述脑电信号在指定时间范围内出现正向波形，则判定所述用户当前处于第三级催眠深度状态。
  3. 根据权利要求1所述的催眠深度检测器，其特征在于，所述脑电电极设置在用户的额头位置；所述参考电极设置在用户的耳垂；所述眼电电极设置在眼角位置；所述加速 度传感器设置在用户的手臂位置。
  4. 根据权利要求1所述的催眠深度检测器，其特征在于，所述滤波电路输出0-256Hz频段的信号。
  5. 根据权利要求2所述的催眠深度检测器，其特征在于，执行所述第一级催眠深度识别模块的算法流程时，所述处理器用于在时间-幅值坐标系上绘制所述眼电信号波形图；在所述时间-幅值坐标系上建立沿时间轴方向移动的检测窗口，并根据眨眼速度和眼电信号幅值设置所述检测窗口长度和高度阈值。
  6. 根据权利要求1所述的催眠深度检测器，其特征在于，所述处理器还用于采集用户在智能辅助睡眠过程中产生的脑电信号；根据所述脑电信号对用户的睡眠状态进行检测，当所述用户处于未睡着状态，向用户播放催眠引导词。
  7. 根据权利要求2所述的催眠深度检测器，其特征在于，执行所述第一级催眠深度识别模块的算法流程时，所述处理器用于计算检测窗口内眼电信号波形尖峰的尖锐程度参数，若所述尖锐程度参数小于预设的尖锐程度参数阈值，判定所述用户当前处于第一级催眠深度状态。
  8. 根据权利要求7所述的催眠深度检测器，其特征在于，所述处理器用于分别计算眼电信号波形在检测窗口内的上部区域面积和下部区域面积；根据所述上部区域面积和下部区域面积计算所述眼电信号波形尖峰的面积；根据尖峰面积计算尖锐程度参数。
  9. 根据权利要求8所述的催眠深度检测器，其特征在于，所述处理器采用如下计算公式计算上部区域面积和下部区域面积：
    Figure PCTCN2016113139-appb-100001
    Figure PCTCN2016113139-appb-100002
    式中，pi为检测窗口内的眼电信号，pmax为检测窗口内眼电信号的最大值，pmin为检测窗口内眼电信号的最小值，areaup表示上部区域面积，areadown表示下部区域面积；
    所述所述眼电信号波形尖峰的面积的计算公式如下：
    Figure PCTCN2016113139-appb-100003
    式中，blinkarea表示尖峰的面积，if表示满足条件；
    所述尖锐程度参数的计算公式如下：
    blinkratio＝blinkarea/in-blinkarea
    式中，blinkratio表示尖锐程度参数，in-blinkarea表示非尖峰部分的面积，这里尖锐程度参数也可以转化为是上部区域面积和下部区域面积之间的比值。
  10. 根据权利要求4所述的催眠深度检测器，其特征在于，所述眨眼速度为[Ta，Tb]，所述检测窗口长度取值≥2Ta；
    所述眼电信号幅值为M，所述高度阈值取值≤0.7M；
    所述播放催眠引导词后的设定时间为30s，所述第二时间段为10s。
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