WO2018053968A1 - 睡眠状态分析中去除眼电伪迹的设备 - Google Patents

睡眠状态分析中去除眼电伪迹的设备 Download PDF

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WO2018053968A1
WO2018053968A1 PCT/CN2016/113143 CN2016113143W WO2018053968A1 WO 2018053968 A1 WO2018053968 A1 WO 2018053968A1 CN 2016113143 W CN2016113143 W CN 2016113143W WO 2018053968 A1 WO2018053968 A1 WO 2018053968A1
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signal
eigenmode
eigenmode function
eeg
correlation coefficient
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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
    • A61B5/48—Other medical applications
    • A61B5/4806—Sleep evaluation
    • A61B5/4812—Detecting sleep stages or cycles
    • 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
    • A61B5/7221—Determining signal validity, reliability or quality
    • 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
    • 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
    • A61B5/7235—Details of waveform analysis

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  • the present invention relates to the field of assisted sleep technology, and more particularly to an apparatus for removing ocular artifacts in sleep state analysis.
  • Polysomnography also known as sleep electroencephalography
  • PSG Polysomnography
  • EEG electroencephalogram
  • An electroencephalogram is a waveform signal that records and amplifies electrical activity generated from the cerebral cortex on the scalp using sophisticated electronic instruments. Since the EEG signal is very weak (microvolt level), it is easily interfered by bioelectrical signals from other parts.
  • the amplitude of the EO signal When the amplitude of the EO signal is low (ie, there is no strong eyeball/eyelid activity such as blinking, etc.), the interference of the EOG signal on the EEG signal is weak.
  • the amplitude of the EO signal is high, since the frequency of the EO signal is lower than that of the normal EEG signal, a high-amplitude EOG signal superimposed on the EEG signal forms a phenomenon similar to the baseline drift.
  • Indepdent component analysis is a commonly used method. It first assumes that the input signals are linear combinations of statistically independent non-Gaussian signals and then uses linear transformation to separate the signals. Its disadvantages are that (1) the assumptions of the input signal are not fully satisfied in actual use; (2) for the separated signals, it is necessary to further determine which signals are "pure" EEG signals, which signals are The electro-oculogram signal that is separated.
  • EEG Pure EEG original -0.2*EOG
  • the waveform of the EEG signal is a very important indicator of sleep state in the analysis of sleep state. For example, the appearance of a spindle wave and a K complex wave indicates entry into the S2 phase of non-eye fast moving sleep.
  • the waveform of the EEG signal after the traditional method is often changed, which affects the subsequent analysis of the EEG signal.
  • An apparatus for removing an electro-oculant artifact in a sleep state analysis comprising: an electroencephalogram electrode, an electro-oculogram electrode, a reference electrode, an analog-to-digital converter, a filter circuit, and a processor;
  • the electroencephalogram electrode, the electrooculogram 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;
  • the electroencephalogram electrode is configured to detect a raw EEG signal of a user during sleep; and the EEG electrode is configured to collect an OEG signal of the user during sleep;
  • 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 perform empirical mode decomposition on each frame of the original EEG signal, decompose it into a plurality of eigenmode functions, and calculate a correlation coefficient between each eigenmode function and an ocular electrical signal at the same time; Find and delete the eigenmode function with the correlation coefficient greater than the preset threshold and the eigenmode function with the largest correlation coefficient, and reconstruct the EEG signal of each frame by using the remaining eigenmode function.
  • the device for removing the electro-optical artifact in the above sleep state analysis uses the ocular electrical signal collected by the ocular electrode and the original EEG signal collected by the EEG electrode, and after the analog-to-digital conversion and filtering process, the processor pairs each frame
  • the original EEG signal is subjected to empirical mode decomposition, which is decomposed into several eigenmode functions, and the correlation coefficient between each eigenmode function and the EOG signal at the same time is calculated. Finding and deleting the correlation coefficient is greater than the preset threshold.
  • the eigenmode function and the eigenmode function with the largest correlation coefficient reconstruct the EEG signal of each frame by using the remaining eigenmode functions.
  • the device can reduce the influence of the process of removing the electro-optical artifacts on the waveform of the EEG signal, retaining most of the details of the original signal, and ensuring the subsequent analysis of the EEG signal.
  • FIG. 1 is a schematic structural diagram of an apparatus for removing an electro-optical artifact in a sleep state analysis according to an embodiment
  • FIG. 2 is a flow chart of an algorithm for removing an electro-optical artifact from a processor
  • Figure 3 is a schematic diagram showing the results of experimental data for removing ocular artifacts.
  • FIG. 1 is a schematic structural diagram of an apparatus for removing an electro-optical artifact in a sleep state analysis according to the present invention, including: an electroencephalogram electrode, an electro-oculogram electrode, a reference electrode, an analog-to-digital converter, a filter circuit, and a processor. ;
  • the electroencephalogram electrode, the electrooculogram 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;
  • the electroencephalogram electrode is configured to detect a raw EEG signal of a user during sleep; and the EEG electrode is configured to collect an OEG signal of the user during sleep;
  • 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 perform empirical mode decomposition on each frame of the original EEG signal, decompose it into a plurality of eigenmode functions, and calculate a correlation coefficient between each eigenmode function and an ocular electrical signal at the same time; Find and delete the eigenmode function with the correlation coefficient greater than the preset threshold and the eigenmode function with the largest correlation coefficient, and reconstruct the EEG signal of each frame by using the remaining eigenmode function.
  • the device for removing the electro-optical artifacts uses the ocular electrical signal collected by the ocular electrode and the original EEG signal collected by the electroencephalogram electrode, and after the analog-to-digital conversion and filtering process, the processor Perform empirical mode decomposition on each frame of original EEG signals, decompose it into several eigenmode functions, calculate the correlation coefficient between each eigenmode function and the EOG signal at the same time; find and delete the correlation coefficient is greater than the pre- The eigenmode function with the threshold and the eigenmode function with the largest correlation coefficient are used to reconstruct the EEG signal of each frame by using the remaining eigenmode functions.
  • the device can reduce the influence of the process of removing the electro-optical artifacts on the waveform of the EEG signal, retaining most of the details of the original signal, and ensuring the subsequent analysis of the EEG signal.
  • the EEG signal output by the device can be used for sleep state monitoring and analysis, etc., of course, the subsequent processing can also be implemented on the processor.
  • the electroencephalogram electrode is disposed at a forehead position of a user; the reference electrode is disposed at a user's earlobe; and the ocular electrode is disposed at an eye corner position; as shown in FIG. 1, in the figure, an electroencephalogram electrode That is, "M” in the figure, the electro-optical electrode includes two electrodes on the left and the right, that is, “ROC” and “LOC” in the figure, and the reference electrode is disposed on the earlobe of the user, that is, “R” and “L” in the figure, the acceleration sensor That 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 corresponding algorithm module can be configured in the processor.
  • the algorithm flow for removing the electro-optical artifacts by the processor includes (1) to (5), as follows:
  • the processor controls the ocular electrode and the EEG electrode to collect the EOG signal and the original EEG signal according to the set frame length;
  • the processor can collect the EOG signal and the EEG signal generated by the user during the sleep process by setting the frame length and the eye electrode and the EEG electrode worn by the user.
  • the processor can collect in one frame of 30s, and then the analysis and processing of each frame of ocular electrical signals and EEG signals.
  • the processor performs empirical mode decomposition on the EEG signal and decomposes it into a form of a sum of a number of eigenmode functions (IMF) and a residual function (Redisual, Re).
  • IMF eigenmode functions
  • Re residual function
  • the set of eigenmode functions includes the following formula:
  • EEG original represents the original EEG signal
  • imf i represents the ith eigenmode function
  • Re represents the residual function
  • FIG. 2 is a flowchart of an algorithm for removing an electro-optical artifact from a processor.
  • a set of eigenmode functions is obtained, and the eigenmode function 1-n (imf 1 ⁇ is calculated respectively).
  • correlation coefficient imf n) with the eye of the electrical signal EOG 1-n corrcoef 1 ⁇ corrcoef n).
  • the eigenmode function whose correlation coefficient is larger than the threshold and the eigenmode function having the largest correlation coefficient are deleted, and the remaining m eigenmode functions.
  • the processor may also be used in the eigenmode function whose correlation coefficient is greater than a preset threshold; calculating the Euclidean distance of the eigenmode function and the EO signal; from the Euclidean distance The smallest eigenmode function is removed from the set of eigenmode functions.
  • the processor can also be used in the eigenmode function whose correlation coefficient is greater than a preset threshold; calculating the cosine distance of the eigenmode function and the EOG signal; the eigenvalue from the smallest cosine distance The modulo function is removed from the set of eigenmode functions.
  • the Euclidean distance or the cosine distance judgment is combined, and More residual EO artifacts that cannot be removed in the correlation coefficient judgment are removed.
  • the processor After removing the electro-optical artifacts, the processor reconstructs the EEG signals after the electro-optical artifacts are removed by using the remaining m eigenmode functions. As an embodiment, when reconstructing the EEG signal, the processor selects a plurality of eigenmode functions in the eigenmode function set to reconstruct the EEG signal according to the order of the eigenmode functions.
  • the eigenmode function is arranged in order of frequency from large to small, and the eigenmode function having the highest similarity with the EOG signal is generally arranged in the middle position, when reconstructing the EEG signal, only The EEG signals are reconstructed by using the first few eigenmode functions to delete the lower frequency eigenmode function including the most similar eigenmode function.
  • the processor can reconstruct the EEG signal by the following formula:
  • EEG pure denotes the reconstructed EEG signal
  • corrcoef denotes the correlation coefficient
  • imf denotes the ith eigenmode function
  • EOG denotes the EOG
  • corrcoef max denotes the largest correlation coefficient
  • thre denotes the preset correlation coefficient threshold.
  • the processor may divide the original EEG signal frame into N time windows before performing empirical mode decomposition on the original EEG signal, and perform eigenmode on the EEG signal of each time window. Function decomposition; and after reconstructing the EEG signal, the EEG signals reconstructed in each time window are combined to obtain an EEG signal frame.
  • the signal processing speed can be accelerated, and the efficiency of the sleep state analysis can be improved.
  • taking 30s as a frame 5s or 10s can be used as a time window length.
  • the apparatus for removing ocular artifacts in the sleep state analysis of the present invention removes only artifacts similar to baseline drift caused by high-amplitude EO, and retains most of the details of the original signal; subsequent use of the EEG signal Better results can be obtained when performing sleep state analysis.
  • FIG. 3 is a schematic diagram showing the results of experimental data for removing ocular artifacts.
  • Fig. 3(a) shows the original EEG signals collected
  • Fig. 3(b) shows the collected EOG signals
  • Fig. 3(c) compares the EEG signals before and after the EO artifacts are removed (in the figure, 1 is the original brain).
  • the electrical signal; 2 is the EEG signal after removing the electro-optical artifacts.
  • the figure below is a partial enlarged view of the above figure. It can be found that the amplitude of the data points in the above-mentioned interval is relatively deep. V-shaped fluctuations are eliminated by this scheme, and more original information is retained.
  • the input multi-path signal is regarded as a multi-source signal after linear combination. And trying to separate these signals from each other, the traditional method can get better results on the periodic signal.
  • EEG signals since EEG signals and EEG signals can be regarded as random signals, and EEG signals are susceptible to external interference, it is difficult to completely separate EEG signals from EEG signals. The signal will be mixed with additional noise signals, making it difficult to analyze subsequent signal processing.
  • the technical solution of the present invention only removes artifacts similar to baseline drift caused by high-amplitude electro-oculography, and retains most of the details of the original signal. Therefore, it is advantageous for the processing of the subsequent time domain-based EEG signal analysis method.

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Abstract

一种睡眠状态分析中去除眼电伪迹的设备，包括：脑电电极（M）、眼电电极（ROC，LOC）、参考电极（R，L）及其连接的模数转换器，以及通过模数转换器和滤波电路连接的处理器；脑电电极（M）用于检测原始脑电信号；眼电电极（ROC，LOC）用于采集眼电信号；模数转换器用于模数转换，滤波电路用于低频滤波后输入至处理器；处理器，用于对每帧滤波后的脑电信号进行经验模态分解，将其分解成若干个本征模函数，计算各个本征模函数与同一时刻的眼电信号之间的相关系数；查找并删除相关系数大于预设阈值的本征模函数和相关系数最大的本征模函数，利用剩余的本征模函数重建每帧脑电信号。睡眠状态分析中去除眼电伪迹的设备可以减少去除眼电伪迹过程对脑电信号的波形的影响，保留了原始信号的大部分细节信息。

Description

睡眠状态分析中去除眼电伪迹的设备 技术领域
本发明涉及辅助睡眠技术领域，特别是涉及一种睡眠状态分析中去除眼电伪迹的设备。
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。
目前市面上已经有一些设备来帮助人们入睡，提高睡眠质量。例如在某一特定睡眠状态下通过声音、光信号等人工干预，避免在熟睡状态下叫醒用户等。对于辅助睡眠的设备而言，为了真正达到提高用户睡眠质量的目的，正确的识别用户的睡眠状态是非常重要的。
多导睡眠图(Polysomnography,PSG)，又称睡眠脑电图，是目前临床上用于睡眠诊断和分析的“金标准”。多导睡眠图利用多种生命体征例如脑电、肌电(颌下)、眼电、呼吸、血氧等对睡眠进行分析。在这些体征信号中，脑电图(electroencephalogram，EOG)处于核心地位。脑电图是利用精密的电子仪器，在头皮上将来自大脑皮层产生的电活动加以记录并放大的波形信号。由于脑电图的信号非常微弱(微伏级)，容易被来自其他部位的生物电信号干扰。当眼电信号幅值较低时(即没有较强烈眼球/眼睑活动如眨眼等)，眼电信号对脑电信号的干扰比较微弱。而眼电信号幅值较高时，由于眼电信号的频率比正常脑电信号低，高幅值的眼电信号叠加在脑电信号上就形成了一个类似于基线漂移的现象。
为了降低眼电信号所带来的影响，目前有很多去除眼电伪迹的方法。独立成分分析(Indepdent component analysis,ICA)是一种常用的方法。它首先假设输入信号都是统计独立的非高斯的信号的线性组合，然后利用线性变换将来自于信号分离。它的缺点是(1)输入信号的假设条件在实际使用中并不能完全满足；(2)对于分离后的多个信号，还需要进一步判断哪些信号是“纯净的”脑电信号，哪些信号是被分离出的眼电信号。此外，还有方法假设了一个眼电信号对脑电信号的影响因子(如0.2)，然后利用脑电信号减去乘以影响因子的眼电信号的方法去除眼电伪迹，如公式：EEGpure＝EEGoriginal-0.2*EOG，由于存在个体差异及眼电电极的位置的不同，一个固定的影响因子并不能很好的适应不同的个体。
此外，由于在睡眠状态分析中，脑电信号的波形是一个很重要的睡眠状态指标。例如纺锤波和K复合波的出现，表示进入了非眼快动睡眠的S2期。经过传统方法处理后的脑电信号的波形往往会发生变化，影响了后续对脑电信号的分析效果。
发明内容
基于此，有必要针对上述问题，提供一种睡眠状态分析中去除眼电伪迹的设备，减少去除眼电伪迹过程对脑电信号的波形的影响，确保后续对脑电信号的分析效果。
一种睡眠状态分析中去除眼电伪迹的设备，包括：脑电电极、眼电电极、参考电极、模数转换器、滤波电路以及处理器；
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；
所述脑电电极用于检测用户在睡眠中的原始脑电信号；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，用于对每帧原始脑电信号进行经验模态分解，将其分解成若干个本征模函数，计算各个本征模函数与同一时刻的眼电信号之间的相关系数；查找并删除相关系数大于预设阈值的本征模函数和相关系数最大的本征模函数，利用剩余的本征模函数重建每帧脑电信号。
上述睡眠状态分析中去除眼电伪迹的设备，利用眼电电极采集的用户的眼电信号和脑电电极采集的原始脑电信号，经过模数转换和滤波处理后，由处理器对每帧原始脑电信号进行经验模态分解，将其分解成若干个本征模函数，计算各个本征模函数与同一时刻的眼电信号之间的相关系数；查找并删除相关系数大于预设阈值的本征模函数和相关系数最大的本征模函数，利用剩余的本征模函数重建每帧脑电信号。该设备可以减少去除眼电伪迹过程对脑电信号的波形的影响，保留了原始信号的大部分细节信息，确保后续对脑电信号的分析效果。
附图说明
图1为一个实施例的睡眠状态分析中去除眼电伪迹的设备的结构示意图；
图2是处理器去除眼电伪迹的算法流程图；
图3是去除眼电伪迹的实验数据结果示意图。
具体实施方式
下面结合附图阐述本发明的睡眠状态分析中去除眼电伪迹的设备的实施例。
参考图1所示，图1为本发明的睡眠状态分析中去除眼电伪迹的设备的结构示意图，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；
所述脑电电极用于检测用户在睡眠中的原始脑电信号；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，用于对每帧原始脑电信号进行经验模态分解，将其分解成若干个本征模函数，计算各个本征模函数与同一时刻的眼电信号之间的相关系数；查找并删除相关系数大于预设阈值的本征模函数和相关系数最大的本征模函数，利用剩余的本征模函数重建每帧脑电信号。
上述实施例的睡眠状态分析中去除眼电伪迹的设备，利用眼电电极采集的用户的眼电信号和脑电电极采集的原始脑电信号，经过模数转换和滤波处理后，由处理器对每帧原始脑电信号进行经验模态分解，将其分解成若干个本征模函数，计算各个本征模函数与同一时刻的眼电信号之间的相关系数；查找并删除相关系数大于预设阈值的本征模函数和相关系数最大的本征模函数，利用剩余的本征模函数重建每帧脑电信号。该设备可以减少去除眼电伪迹过程对脑电信号的波形的影响，保留了原始信号的大部分细节信息，确保后续对脑电信号的分析效果。
后续可以利用该设备输出的脑电信号进行睡眠状态监测和分析等，当然，该后续的处理也可以在所述处理器上去实现。
在一个实施例中，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；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。
对于去除眼电伪迹功能，主要通过处理器来进行，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。
处理器去除眼电伪迹的算法流程包括(1)～(5)，具体如下：
(1)处理器控制眼电电极和脑电电极根据设定帧长度采集用户的眼电信号和原始脑电信号；
如在对用户进行辅助睡眠等睡眠状态分析中，处理器可以以设定帧长度，通过用户佩戴的眼电电极和脑电电极，采集用户在睡眠过程中产生的眼电信号和脑电信号。在采集信号时，可以以30s为一帧进行采集，后续对每帧眼电信号和脑电信号进行分析处理。
(2)对该帧原始脑电信号进行经验模态分解，将其分解成若干个本征模函数，得到本征模函数集合；
在此，处理器对脑电信号进行经验模态分解，将其分解成若干个本征模函数(Intrinsic Mode Function，IMF)和残差函数(Redisual，Re)之和的形式。
本征模函数集合包括如下公式：
Figure PCTCN2016113143-appb-000001
式中，EEGoriginal表示原始脑电信号，imfi表示第i个本征模函数，Re表示残差函数。
(3)分别计算所述本征模函数集合的各个本征模函数与同一时刻的眼电信号之间的相关系数；
参考图2，图2是处理器去除眼电伪迹的算法流程图，原始脑电信号进行经验模态分解后，得到本征模函数集合，分别计算本征模函数1-n(imf1～imfn)与眼电信号EOG的相关系数1-n(corrcoef1～corrcoefn)。
(4)查找出相关系数大于预设阈值的本征模函数和相关系数最大的本征模函数，并将其从本征模函数集合中删除；
如图2所示，通过设定阈值，在计算完相关系数后，将相关系数大于阈值的本征模函数和相关系数最大的本征模函数删除，剩下的m个本征模函数。
作为一个实施例，在计算完相关系数后，处理器还可以用于在相关系数大于预设阈值的本征模函数中；计算本征模函数与眼电信号的欧氏距离；从欧氏距离最小的本征模函数从本征模函数集合中剔除。
类似的，在计算完相关系数后，处理器还可以用于在相关系数大于预设阈值的本征模函数中；计算本征模函数与眼电信号的余弦距离；从余弦距离最小的本征模函数从本征模函数集合中剔除。
通过上述实施例，在相关系数判断基础上结合了欧氏距离或余弦距离判断，可以将与 相关系数判断中无法去除的更多遗留的眼电伪迹去除。
(5)利用本征模函数集合中剩余的本征模函数重建该帧脑电信号；
处理器在去除了眼电伪迹后，利用剩下的m个本征模函数重建去除了眼电伪迹后的脑电信号。作为一个实施例，处理器在重建脑电信号时，根据本征模函数的排列顺序，选择本征模函数集合中位置靠前的若干个本征模函数进行重建脑电信号。
该实施例中，由于本征模函数的排列顺序是按频率由大到小，并且与眼电信号相似度最高的本征模函数一般排列在中间位置，因此在重建脑电信号时，可以仅利用前若干个本征模函数，删除包括相似度最高的本征模函数在内的频率较低的本征模函数后，再重建脑电信号。
其中，处理器可以采用如下公式重建脑电信号：
Figure PCTCN2016113143-appb-000002
式中，EEGpure表示重建的脑电信号，corrcoef表示相关系数，imf表示第i个本征模函数，EOG表示眼电信号，corrcoefmax表示最大的相关系数，thre表示预设的相关系数阈值。
在一个实施例中，处理器可以在对原始脑电信号进行经验模态分解前，先将原始脑电信号帧划分为N个时间窗口，并对每个时间窗口的脑电信号进行本征模函数分解；以及在重建脑电信号后，将各个时间窗口重建的脑电信号进行合并，得到脑电信号帧。
上述实施例，通过将采集的原始脑电信号帧划分多个时间窗口并行处理，能够加快信号处理速度，提高睡眠状态分析的效率。
例如，以30s一帧为例，可以以5s或10s为一个时间窗口长度。
本发明的睡眠状态分析中去除眼电伪迹的设备，只去除高幅度眼电所造成的类似于基线漂移的伪迹，并且保留了原始信号的大部分细节信息；后续使用该脑电信号去进行睡眠状态分析时，可以得到更好的效果。
参考图3所示，图3是去除眼电伪迹的实验数据结果示意图。图3(a)为采集的原始脑电信号，图3(b)为采集的眼电信号，图3(c)比较了去除眼电伪迹前后的脑电信号(图中，①为原始脑电信号；②为去除眼电伪迹后的脑电信号)，下图是上图截取部分放大图，可以发现，上述区间内的数据点之间，由眼电带来的幅度较大的深V形波动被本方案给消除的同时，并且保留了较多的原始信息。
相对于传统方法(如ICA等)将输入的多路信号视为经过线性组合后的多路源信号， 并试图将这些信号彼此分离，能在周期信号上传统方法能获得比较好的效果。而且对于脑电信号而言，由于脑电信号和眼电信号都可以视为随机信号，并且脑电信号容易受到外部干扰，很难将脑电信号和眼电信号彻底分离开，此时脑电信号就会混入额外的噪声信号，加大了后续信号处理分析的难度。而本发明的技术方案，只去除高幅度眼电带来的类似于基线漂移的伪迹，保留了原始信号的大部分细节信息。因此有利于后续的基于时域的脑电信号分析方法的处理。
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. 一种睡眠状态分析中去除眼电伪迹的设备，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；
    所述脑电电极用于检测用户在睡眠中的原始脑电信号；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，用于对每帧原始脑电信号进行经验模态分解，将其分解成若干个本征模函数，计算各个本征模函数与同一时刻的眼电信号之间的相关系数；查找并删除相关系数大于预设阈值的本征模函数和相关系数最大的本征模函数，利用剩余的本征模函数重建每帧脑电信号。
  2. 根据权利要求1所述的睡眠状态分析中去除眼电伪迹的设备，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；所述滤波电路输出0-256Hz频段的信号。
  3. 根据权利要求1所述的睡眠状态分析中去除眼电伪迹的设备，其特征在于，所述处理器，用于根据设定帧长度采集用户的眼电信号和原始脑电信号；对该帧原始脑电信号进行经验模态分解，将其分解成若干个本征模函数，得到本征模函数集合；分别计算所述本征模函数集合的各个本征模函数与同一时刻的眼电信号之间的相关系数；查找出相关系数大于预设阈值的本征模函数和相关系数最大的本征模函数，并将其从本征模函数集合中删除；利用本征模函数集合中剩余的本征模函数重建该帧脑电信号。
  4. 根据权利要求3所述的睡眠状态分析中去除眼电伪迹的设备，其特征在于，所述本征模函数集合包括如下公式：
    Figure PCTCN2016113143-appb-100001
    式中，EEGoriginal表示原始脑电信号，imfi表示第i个本征模函数，Re表示残差函数。
  5. 根据权利要求1所述的睡眠状态分析中去除眼电伪迹的设备，其特征在于，所述处理器采用如下公式重建脑电信号：
    Figure PCTCN2016113143-appb-100002
    式中，EEGpure表示重建的脑电信号，corrcoef表示相关系数，imf表示第i个本征模函数，EOG表示眼电信号，corrcoefmax表示最大的相关系数，thre表示预设的相关系数阈值。
  6. 根据权利要求3所述的睡眠状态分析中去除眼电伪迹的设备，其特征在于，所述处理器，还用于在相关系数大于预设阈值的本征模函数中；计算本征模函数与眼电信号的欧氏距离；将欧氏距离最小的本征模函数从本征模函数集合中剔除。
  7. 根据权利要求3所述的睡眠状态分析中去除眼电伪迹的设备，其特征在于，所述处理器，还用于在相关系数大于预设阈值的本征模函数中；计算本征模函数与眼电信号的余弦距离；从余弦距离最小的本征模函数从本征模函数集合中剔除。
  8. 根据权利要求3所述的睡眠状态分析中去除眼电伪迹的设备，其特征在于，所述处理器，还用于在对原始脑电信号进行经验模态分解前，将原始脑电信号帧划分为若干个时间窗口，并对每个时间窗口的脑电信号进行本征模函数分解；以及在重建脑电信号后将各个时间窗口重建的脑电信号进行合并，得到脑电信号帧。
  9. 根据权利要求3所述的睡眠状态分析中去除眼电伪迹的设备，其特征在于，所述处理器，还用于在重建脑电信号时，根据本征模函数的排列顺序，选择本征模函数集合中位置靠前的若干个本征模函数进行重建脑电信号。
  10. 根据权利要求5所述的睡眠状态分析中去除眼电伪迹的设备，其特征在于，所述预设阈值为0.5，所述帧长度为30s，所述时间窗口长度为5s或10s。
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113081002A (zh) * 2021-03-31 2021-07-09 北京智源人工智能研究院 脑电信号的伪差去除方法、装置及电子设备
CN113208614A (zh) * 2021-04-30 2021-08-06 南方科技大学 脑电降噪的方法及装置、可读存储介质
CN114403896A (zh) * 2022-01-14 2022-04-29 南开大学 单通道脑电信号中眼电伪迹去除方法

Families Citing this family (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106236083B (zh) * 2016-09-21 2018-02-16 广州视源电子科技股份有限公司 睡眠状态分析中去除眼电伪迹的设备
CN107510453B (zh) * 2017-10-12 2019-12-24 北京翼石科技有限公司 一种前额区脑电分析方法
CN110558977A (zh) * 2019-09-09 2019-12-13 西北大学 基于机器学习模糊特征选择的癫痫发作脑电信号分类方法
CN112971778A (zh) * 2021-02-09 2021-06-18 北京师范大学 脑功能成像信号获得方法、装置及电子设备
CN114886388B (zh) * 2022-07-12 2022-11-22 浙江普可医疗科技有限公司 一种麻醉深度监测过程中脑电信号质量的评估方法及装置

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101869477A (zh) * 2010-05-14 2010-10-27 北京工业大学 一种自适应脑电信号中眼电伪迹的自动去除方法
US20110257556A1 (en) * 2010-04-16 2011-10-20 Dan Guo Systems and methods for quantitatively characterizing slow wave activities and states in sleep
CN103720471A (zh) * 2013-12-24 2014-04-16 电子科技大学 一种基于因子分析的眼电伪迹去除方法
US20150038804A1 (en) * 2013-07-30 2015-02-05 Magdy Younes Method, non-transitory computer readable medium and apparatus for arousal intensity scoring
CN104688220A (zh) * 2015-01-28 2015-06-10 西安交通大学 一种去除脑电信号中眼电伪迹的方法
CN105342605A (zh) * 2015-12-09 2016-02-24 西安交通大学 一种去除脑电信号中肌电伪迹的方法
CN106236083A (zh) * 2016-09-21 2016-12-21 广州视源电子科技股份有限公司 睡眠状态分析中去除眼电伪迹的设备

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110257556A1 (en) * 2010-04-16 2011-10-20 Dan Guo Systems and methods for quantitatively characterizing slow wave activities and states in sleep
CN101869477A (zh) * 2010-05-14 2010-10-27 北京工业大学 一种自适应脑电信号中眼电伪迹的自动去除方法
US20150038804A1 (en) * 2013-07-30 2015-02-05 Magdy Younes Method, non-transitory computer readable medium and apparatus for arousal intensity scoring
CN103720471A (zh) * 2013-12-24 2014-04-16 电子科技大学 一种基于因子分析的眼电伪迹去除方法
CN104688220A (zh) * 2015-01-28 2015-06-10 西安交通大学 一种去除脑电信号中眼电伪迹的方法
CN105342605A (zh) * 2015-12-09 2016-02-24 西安交通大学 一种去除脑电信号中肌电伪迹的方法
CN106236083A (zh) * 2016-09-21 2016-12-21 广州视源电子科技股份有限公司 睡眠状态分析中去除眼电伪迹的设备

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113081002A (zh) * 2021-03-31 2021-07-09 北京智源人工智能研究院 脑电信号的伪差去除方法、装置及电子设备
CN113081002B (zh) * 2021-03-31 2023-03-31 灵犀医学科技(北京)有限公司 脑电信号的伪差去除方法、装置及电子设备
CN113208614A (zh) * 2021-04-30 2021-08-06 南方科技大学 脑电降噪的方法及装置、可读存储介质
CN114403896A (zh) * 2022-01-14 2022-04-29 南开大学 单通道脑电信号中眼电伪迹去除方法
CN114403896B (zh) * 2022-01-14 2023-08-25 南开大学 单通道脑电信号中眼电伪迹去除方法

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