WO2019041772A1 - 一种基于脑电信号的麻醉深度的监测方法及系统 - Google Patents
一种基于脑电信号的麻醉深度的监测方法及系统 Download PDFInfo
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- A—HUMAN NECESSITIES
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
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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]
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- the present invention relates to the field of medical technology, and in particular to a method and system for monitoring anesthesia depth based on an electroencephalogram signal.
- Anesthesia refers to the disappearance of systemic or local sensation and the state of memory forgetting caused by means of drugs and the like, which can ensure the smooth operation of the operation, and the anesthesia is too deep or too shallow, which may cause harm to the patient. Therefore, monitoring the depth of anesthesia is particularly important.
- EEG can directly reflect the activities of the central nervous system. Therefore, EEG technology has become one of the best means to determine the depth of anesthesia.
- the anesthesia depth monitoring methods based on EEG signals mainly include frequency domain analysis, bispectral index, anesthesia trend and entropy index analysis.
- the bispectral index scale (BIS) is the most widely used anesthesia depth monitoring index.
- BIS is clearly dependent on the use of anesthetics and is sensitive to patient differences.
- the update process of the BIS parameters is delayed due to the use of an averaging algorithm and a process of removing data segments with large artifacts.
- Narcotrend uses Kugler's multi-parameter statistics to classify anesthesia brain waves into six major stages, A, B, C, D, E, and F, 14 subclasses. Studies have shown that anesthesia trends have similar clinical manifestations as bispectral indices, with differences between anesthetics and individual patients.
- Chinese patent application 2015100855324 uses a nonlinear dynamics method based on complexity to process EEG signals, calculate the lattice complexity, edge frequency and burst suppression ratio of EEG signals, and use the decision tree algorithm to fit the depth of anesthesia. index.
- this method requires the classification of anesthesia status given by experts as a reference, and the accuracy of the monitoring results is affected by the training of decision trees.
- Chinese patent application 2007101248131 proposes an anesthesia depth monitoring method based on sorting entropy, which segments the EEG data, calculates the sorting entropy of each data segment, and estimates the depth of anesthesia according to the size of the sorting entropy.
- Chinese patent application 2014800085154 proposes a method and apparatus for measuring the depth of anesthesia by using a cepstrum technique, but it only considers the frequency domain information of the EEG signal.
- the technical problem to be solved by the present invention is to provide a method and system for monitoring anesthesia depth of an EEG signal based on an improved trend-removing moving average method, which has improved the real-time performance of anesthesia depth monitoring, The purpose of robustness and accuracy.
- a method for monitoring anesthesia depth based on an EEG signal comprising:
- the wave function of the interference-free EEG signal is calculated using an improved trend-free moving average algorithm, and the anesthesia depth index is calculated based on the wave function.
- the method for monitoring an anesthesia depth based on an EEG signal wherein the acquiring an EEG signal of a patient and pre-processing the EEG signal to obtain an interference-free EEG signal is specifically:
- the EEG information of the patient is collected, and the noise interference of the EEG signal is eliminated by using a wavelet entropy threshold method to obtain a non-interfering EEG signal.
- the method for monitoring an anesthesia depth based on an electroencephalogram signal wherein the wave function of calculating the interference-free EEG signal is calculated by using an improved trend-eliminating moving average algorithm, and calculating the anesthesia depth index according to the wave function is specifically :
- E m wavelet entropy
- s is the window length
- L is a positive integer
- anesthesia depth index is:
- F MDMA (s) is the wave function.
- a monitoring system for anesthesia depth based on an EEG signal comprising:
- a processing module configured to collect an EEG signal of the patient, and preprocess the EEG signal to obtain a non-interfering EEG signal
- a calculation module for calculating a fluctuation function of the interference-free EEG signal using an improved trend-free moving average algorithm, and calculating the anesthesia depth index according to the wave function.
- the monitoring system for anesthesia depth based on an EEG signal wherein the processing module is specifically configured to:
- the EEG information of the patient is collected, and the noise interference of the EEG signal is eliminated by using a wavelet entropy threshold method to obtain a non-interfering EEG signal.
- the monitoring system for anesthesia depth based on an EEG signal wherein the calculation module specifically includes:
- An acquiring unit configured to set the interference-free EEG signal to an EEG signal time sequence, and obtain an average value of the EEG signal time series;
- a first calculating unit configured to determine an accumulated sequence of the EEG signal time series according to the mean value, and calculate a trend sequence of a fixed window length by using a backward sliding average method
- a first calculating unit configured to calculate a cancellation trend sequence of the EEG signal time series according to the trend sequence, and calculate a fluctuation value of the window length interference-free EEG signal according to the elimination trend sequence;
- a third calculating unit configured to calculate a wave function of the window length interference-free EEG signal according to the fluctuation value, and calculate the anesthesia depth index according to the wave function.
- the monitoring system for anesthesia depth based on an EEG signal wherein the fluctuation function is:
- E m wavelet entropy
- s is the window length
- L is a positive integer
- the monitoring system for anesthesia depth based on an EEG signal wherein the anesthesia depth index is:
- F MDMA (s) is the wave function.
- the present invention provides a method and system for monitoring anesthesia depth based on an EEG signal, the method comprising: collecting an EEG signal of a patient, and preprocessing the EEG signal A non-interfering EEG signal is obtained; a wave function of the interference-free EEG signal is calculated using an improved trend-eliminating moving average algorithm, and the anesthesia depth index is calculated based on the wave function.
- the present invention calculates the anesthesia depth index by an improved trend-free moving average algorithm to obtain an anesthesia depth index, thereby improving the real-time, robustness (or stability) and accuracy of anesthesia depth monitoring, and the quality of the EEG signal. The reliability of the test can also be maintained in the case of poor conditions.
- FIG. 1 is a flow chart of a preferred implementation of a method for monitoring anesthesia depth based on an EEG signal provided by the present invention.
- step S100 is a schematic flow chart of step S100 in the method for monitoring anesthesia depth based on an electroencephalogram signal provided by the present invention.
- Figure 3 is a comparison of the noise removal results using the wavelet entropy threshold method and the SureShink threshold and the Minimax threshold method.
- (a) is the original EEG signal
- (b) is the result of denoising using the SureShink threshold method
- (c) is the adoption.
- (d) is the result of denoising by the wavelet entropy threshold method.
- FIG. 4 is a schematic flow chart of step S200 in the method for monitoring anesthesia depth based on an electroencephalogram signal provided by the present invention.
- Figure 5 is a graph comparing the trend of the anesthesia depth index F min (s) monitored by the present invention with the BIS trend.
- FIG. 6 is a structural schematic diagram of a monitoring system for anesthesia depth based on an EEG signal provided by the present invention.
- FIG. 7 is a structural schematic diagram of an embodiment of a monitoring system for anesthesia depth based on an electroencephalogram signal provided by the present invention.
- the present invention provides a method and system for monitoring the depth of anesthesia based on an electroencephalogram signal.
- the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It is understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
- the terminal device can be implemented in various forms.
- the terminal described in the present invention may include, for example, a mobile phone, a smart phone, a notebook computer, a digital broadcast receiver, a PDA (Personal Digital Assistant), a PAD (Tablet), a PMP (Portable Multimedia Player), a navigation device, etc.
- Mobile terminals and fixed terminals such as digital TVs, desktop computers, and the like.
- PDA Personal Digital Assistant
- PAD Tablett
- PMP Portable Multimedia Player
- FIG. 1 is a flow chart of a preferred embodiment of a method for monitoring anesthesia depth based on an EEG signal according to the present invention. The method includes:
- S100 Collect a patient's EEG signal, and preprocess the EEG signal to obtain a non-interfering EEG signal;
- the acquired EEG signal EEG is pre-processed to eliminate low-frequency noise and sharp-wave interference to obtain a non-interfering EEG signal, and an improved elimination trend sliding average algorithm is used to calculate the EEG signal.
- Anesthesia depth index This allows the anesthesia depth monitoring to have a smaller time delay and more quickly reflect changes in the depth of anesthesia. Also, the signal fluctuation index range is larger by using the improved trend-free moving average algorithm (MDMA). In the case of poor signal quality, a stable anesthesia depth index can also be accurately monitored.
- MDMA trend-free moving average algorithm
- the EEG signal is brain electrical information.
- the acquisition of the patient's EEG signal refers to the use of the patient's EEG signal.
- the collected EEG signals are EEG signals with interference, such as noise, sharp wave interference, and the like.
- the EEG signal needs to be pre-processed to remove interference.
- the EEG signal is preprocessed by using a wavelet entropy threshold to obtain a non-interfering EEG signal.
- the collecting the EEG signal of the patient and pre-processing the EEG signal to obtain the interference-free EEG signal specifically includes:
- S101 Apply a discrete wavelet transform to the EEG signal to obtain an approximate wavelet coefficient and a detail wavelet coefficient by using a patient's EEG signal;
- the applying the discrete wavelet transform to the EEG signal refers to performing wavelet packet decomposition on the EEG signal by using db16 wavelet to obtain approximate wavelet coefficients and detailed wavelet coefficients, where The approximate wavelet coefficients and the detail wavelet coefficients can be expressed as:
- a j (k) is the approximate wavelet coefficient
- D j (k) is the detail wavelet coefficient
- j is the wavelet decomposition layer number
- the low-pass filter g and the high-pass filter h both adopt the db16 wavelet.
- the calculation formula of the wavelet entropy threshold may be:
- Em is the wavelet entropy calculated from the correlated wavelet energy
- m represents the length of the EEG signal processed at one time
- the calculation process of the wavelet entropy Em may be:
- calculation formula of the wavelet entropy may be:
- the specific processing method for performing soft threshold nonlinear processing on the detail wavelet coefficients by using the wavelet entropy threshold is:
- the processed detail wavelet coefficients are processed.
- Performing wavelet inverse transform is specifically for the processed detail wavelet coefficient Perform discrete wavelet inverse transform, for example, using db16 wavelet for discrete wavelet inverse transform to estimate the low frequency noise signal e i .
- the EEG signal obtained according to the low-frequency noise signal and the EEG signal is specifically an EEG signal obtained by subtracting the low-frequency noise signal from the EEG signal to obtain noise cancellation, that is, the interference-free EEG signal.
- the calculation formula of the interference-free EEG signal may be:
- x i is a non-interfering EEG signal and y i is an acquired EEG signal.
- the step S200 calculates a wave function of the interference-free EEG signal using an improved trend-free moving average algorithm, and calculates the anesthesia according to the wave function.
- the depth index specifically includes:
- S201 Set the interference-free EEG signal to a time series of an EEG signal, and obtain an average value of the time series of the EEG signals;
- X mean wherein the formula of X mean can be:
- the accumulating sequence for determining the time series of the EEG signals according to the mean value is specifically: each item of the EEG time series x i is subtracted from the mean X mean to obtain a new one. The time series is then summed to the new time series to obtain a summation sequence. After selecting a fixed window length s, a simple backward sliding average method is used to obtain a trend sequence. In practical applications, the calculation formula of the accumulated sequence Y(i) may be:
- the trend sequence The formula can be:
- the canceling trend sequence for calculating the time series of the EEG signal according to the trend sequence is specifically subtracting the trend sequence from the accumulated sequence Y(i)
- the calculation formula of the trend-eliminating sequence C s (i) can be:
- the calculation formula of the fluctuation value may be:
- Em is the wavelet entropy calculated from the relevant wavelet energy.
- anesthesia depth index F min (s) is calculated according to the wave function, and the calculation formula of the anesthesia depth index may be:
- the invention also provides a monitoring system for anesthesia depth based on an EEG signal, as shown in FIG. 6, which specifically includes:
- the processing module 101 is configured to collect an EEG signal of the patient, and perform pre-processing on the EEG signal to obtain a non-interfering EEG signal;
- the calculation module 102 is configured to calculate a wave function of the interference-free EEG signal using an improved trend-free moving average algorithm, and calculate the anesthesia depth index according to the wave function.
- the monitoring system for anesthesia depth based on an EEG signal wherein the processing module is specifically configured to:
- the EEG information of the patient is collected, and the noise interference of the EEG signal is eliminated by using a wavelet entropy threshold method to obtain a non-interfering EEG signal.
- the monitoring system for anesthesia depth based on an EEG signal wherein the calculation module specifically includes:
- An acquiring unit configured to set the interference-free EEG signal to an EEG signal time sequence, and obtain an average value of the EEG signal time series;
- a first calculating unit configured to determine an accumulated sequence of the EEG signal time series according to the mean value, and calculate a trend sequence of a fixed window length by using a backward sliding average method
- a first calculating unit configured to calculate a cancellation trend sequence of the EEG signal time series according to the trend sequence, and calculate a fluctuation value of the window length interference-free EEG signal according to the elimination trend sequence;
- a third calculating unit configured to calculate a wave function of the window length interference-free EEG signal according to the fluctuation value, and calculate the anesthesia depth index according to the wave function.
- the monitoring system for anesthesia depth based on an EEG signal wherein the fluctuation function is:
- E m wavelet entropy
- s is the window length
- L is a positive integer
- the monitoring system for anesthesia depth based on an EEG signal wherein the anesthesia depth index is:
- F MDMA (s) is the wave function.
- the processing of the EEG signal based on the electroencephalogram signal-based anesthesia depth monitoring system and the index of detecting the depth of anesthesia according to the EEG signal may be implemented by a processor, that is, As shown in FIG. 7, it may include:
- the collecting module 201 is configured to collect brain electrical information of the patient
- the processor 202 is configured to remove artifacts in the EEG signal, and apply a trend elimination moving average method to the EEG signal after removing artifacts and noise to obtain an index F min (s) indicating the depth of anesthesia.
- the monitoring system for anesthesia depth based on the EEG signal may further include:
- the A/D converter 203 is configured to convert the analog EEG signal into a digital signal and transmit the digital signal to the processor.
- the disclosed systems and methods may be implemented in other manners.
- the device embodiments described above are merely illustrative.
- the division of the modules is only a logical function division.
- there may be another division manner for example, multiple units or components may be combined or Can be integrated into another system, or some features can be ignored or not executed.
- the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device or unit, and may be in an electrical, mechanical or other form.
- the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of the embodiment.
- each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
- the above integrated unit can be implemented in the form of hardware or in the form of hardware plus software functional units.
- the above-described integrated unit implemented in the form of a software functional unit can be stored in a computer readable storage medium.
- the above software functional unit is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor to perform the methods of the various embodiments of the present invention. Part of the steps.
- the foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, and the like, which can store program codes. .
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Abstract
一种基于脑电信号的麻醉深度的监测方法及系统，方法包括：采集患者的EEG信号，并对EEG信号进行预处理以得到无干扰EEG信号（S100）；采用改进的消除趋势滑动平均算法计算无干扰EEG信号的波动函数，并根据波动函数计算麻醉深度指数（S200）。通过改进的消除趋势滑动平均算法对无干扰脑电信号进行计算以得到麻醉深度指数，从而提高麻醉深度监测的实时性、鲁棒性和准确性，在脑电信号质量较差的情况下也可以保持检测的可靠性。
Description
本发明涉及医疗技术领域，特别涉及一种基于脑电信号的麻醉深度的监测方法及系统。
麻醉是指借助于药物等方法而产生的全身或局部感觉的消失及记忆遗忘状态，它可以确保手术的顺利进行，麻醉过深或过浅都会对患者造成危害。因此，麻醉深度的监测尤为重要。而脑电可以直接反映出中枢神经系统的活动。因此脑电技术成为确定麻醉深度的最佳手段之一。目前基于脑电信号的麻醉深度监测方法主要包括频域分析、双谱指数、麻醉趋势和熵指数分析等。双谱指数(bispectral index scale,BIS)是目前使用最多的麻醉深度监测指标，它采用Beta比率、快慢波的相对同步性、爆发抑制率BSR和QUAZI四个指标，根据麻醉状态调整各个指数的权值，然后通过加权和得到BIS指数。研究发现，BIS明显依赖于麻醉药的使用，且敏感于病人的差异。此外，由于采用平均算法和去除存在较大伪迹干扰的数据段等处理过程，导致BIS参数的更新延迟。麻醉趋势(Narcotrend)利用Kugler多参数统计和，将麻醉脑电波分为A、B、C、D、E、F六大期，14小类。研究表明，麻醉趋势与双谱指数具有相似的临床表现，存在麻醉药和病人个体之间的差异。
近几年，非线性动力学方法开始被广泛的应用于脑电信号分析和麻醉深度监测的研究中。利用熵监测麻醉深度的方法就是其中的一种。近似熵是一种度量序列的复杂性和统计量化的规则，对脑电图的时域特征进行分析，其特点是具有较好的抗干扰和抗噪的能力。但是已有的近似熵等复杂性算法由于计算所需序列长度长或计算所需时间长的缺点无法实现实时监测。例如，中国专利申请2015100855324使用基于复杂度的非线性动力学方法对脑电信号进行处理，分别计算脑电信号的格子复杂度、边缘频率和爆发抑制比，并利用决策树算法拟合得到麻醉深度指数。但是这种方法需要专家给出的麻醉状态分类作为参考，其监测结果的准确性受到决策树训练好坏的影响。中国专利申请2007101248131提出了一种基于排序熵的麻醉深度监测方法，对脑电数据进行分段处理，并计算各数据 段的排序熵，根据排序熵值的大小估计麻醉深度。中国专利申请2014800085154提出了一种通过使用倒谱技术测量麻醉深度的方法和设备，但它只考虑了脑电信号的频域信息。
因而现有技术还有待改进和提高。
发明内容
本发明要解决的技术问题在于，针对现有技术的不足，提供一种基于改进的消除趋势滑动平均方法的脑电信号的麻醉深度的监测方法及系统，已达到提高麻醉深度监测的实时性、鲁棒性和准确性的目的。
为了解决上述技术问题，本发明所采用的技术方案如下：
一种基于脑电信号的麻醉深度的监测方法，其包括：
采集患者的EEG信号，并对所述EEG信号进行预处理以得到无干扰EEG信号；
采用改进的消除趋势滑动平均算法计算所述无干扰EEG信号的波动函数，并根据所述波动函数计算所述麻醉深度指数。
所述基于脑电信号的麻醉深度的监测方法，其中，所述采集患者的EEG信号，并对所述EEG信号进行预处理以得到无干扰EEG信号具体为：
采集患者的EEG信息，并采用小波熵阈值法消除所述EEG信号的噪声干扰以得到无干扰EEG信号。
所述基于脑电信号的麻醉深度的监测方法，其中，所述采用改进的消除趋势滑动平均算法计算所述无干扰EEG信号的波动函数，并根据所述波动函数计算所述麻醉深度指数具体为：
设所述无干扰EEG信号为脑电信号时间序列，并获取所述脑电信号时间序列的均值；
根据所述均值确定所述脑电信号时间序列的累加序列，并采用后向滑动平均值法计算一固定窗口长度的趋势序列；
根据所述趋势序列计算所述脑电信号时间序列的消除趋势序列，并根据所述消除趋势序列计算所述窗口长度无干扰EEG信号的波动值；
根据所述波动值计算所述窗口长度无干扰EEG信号的波动函数，并根据所述波动函数计算所述麻醉深度指数。
所述基于脑电信号的麻醉深度的监测方法，其中，所述波动函数为：
其中，E
m为小波熵，s为窗口长度，L为正整数；
表示波动值。
所述基于脑电信号的麻醉深度的监测方法，其中，所述麻醉深度指数为：
其中，所述
为增益指数，F
MDMA(s)为波动函数。
一种基于脑电信号的麻醉深度的监测系统，其包括：
处理模块，用于采集患者的EEG信号，并对所述EEG信号进行预处理以得到无干扰EEG信号；
计算模块，用于采用改进的消除趋势滑动平均算法计算所述无干扰EEG信号的波动函数，并根据所述波动函数计算所述麻醉深度指数。
所述基于脑电信号的麻醉深度的监测系统，其中，所述处理模块具体用于：
采集患者的EEG信息，并采用小波熵阈值法消除所述EEG信号的噪声干扰以得到无干扰EEG信号。
所述基于脑电信号的麻醉深度的监测系统，其中，所述计算模块具体包括：
获取单元，用于设所述无干扰EEG信号为脑电信号时间序列，并获取所述脑电信号时间序列的均值；
第一计算单元，用于根据所述均值确定所述脑电信号时间序列的累加序列，并采用后向滑动平均值法计算一固定窗口长度的趋势序列；
第一计算单元，用于根据所述趋势序列计算所述脑电信号时间序列的消除趋势序列，并根据所述消除趋势序列计算所述窗口长度无干扰EEG信号的波动值；
第三计算单元，用于根据所述波动值计算所述窗口长度无干扰EEG信号的波动函数，并根据所述波动函数计算所述麻醉深度指数。
所述基于脑电信号的麻醉深度的监测系统，其中，所述波动函数为：
其中，E
m为小波熵，s为窗口长度，L为正整数；
表示波动值。
所述基于脑电信号的麻醉深度的监测系统，其中，所述麻醉深度指数为：
其中，所述
为增益指数，F
MDMA(s)为波动函数。
有益效果：与现有技术相比，本发明提供了一种基于脑电信号的麻醉深度的监测方法及系统，所述方法包括：采集患者的EEG信号，并对所述EEG信号进行预处理以得到无干扰EEG信号；采用改进的消除趋势滑动平均算法计算所述无干扰EEG信号的波动函数，并根据所述波动函数计算所述麻醉深度指数。本发明通过改进的消除趋势滑动平均算法对无干扰脑电信号进行计算以得到麻醉深度指数，从而提高麻醉深度监测的实时性、鲁棒性(或稳定性)和准确性，在脑电信号质量较差的情况下也可以保持检测的可靠性。
图1为本发明提供的基于脑电信号的麻醉深度的监测方法较佳实施的流程图。
图2为本发明提供的基于脑电信号的麻醉深度的监测方法中步骤S100的流程示意图。
图3为采用小波熵阈值法与SureShink阈值、Minimax阈值法去除噪声结果的比较图，其中，(a)为原始EEG信号，(b)为采用SureShink阈值法去噪的结果，(c)为采用Minimax阈值法去噪的结果，(d)为采用小波熵阈值法去噪的结果。
图4为本发明提供的基于脑电信号的麻醉深度的监测方法中步骤S200的流程示意图。
图5为本发明监测的麻醉深度指数F
min(s)趋势与BIS趋势的比较图。
图6为本发明提供的基于脑电信号的麻醉深度的监测系统的结构原理图。
图7为本发明提供的基于脑电信号的麻醉深度的监测系统的一个实施例的结构原理图。
本发明提供一种基于脑电信号的麻醉深度的监测方法及系统，为使本发明的目的、技术方案及效果更加清楚、明确，以下参照附图并举实施例对本发明进 一步详细说明。应当理解，此处所描述的具体实施例仅用以解释本发明，并不用于限定本发明。
本发明中，使用用于表示元件的诸如“模块”、“部件”或“单元”的后缀仅为了有利于本发明的说明，其本身并没有特定的意义。因此，“模块”、“部件”或“单元”可以混合地使用。
终端设备可以以各种形式来实施。例如，本发明中描述的终端可以包括诸如移动电话、智能电话、笔记本电脑、数字广播接收器、PDA(个人数字助理)、PAD(平板电脑)、PMP(便携式多媒体播放器)、导航装置等等的移动终端以及诸如数字TV、台式计算机等等的固定终端。然而，本领域技术人员将理解的是，除了特别用于移动目的的元件之外，根据本发明的实施方式的构造也能够应用于固定类型的终端。
下面结合附图，通过对实施例的描述，对发明内容作进一步说明。
请参照图1，图1为本发明提供的基于脑电信号的麻醉深度的监测方法的较佳实施例的流程图。所述方法包括：
S100、采集患者的EEG信号，并对所述EEG信号进行预处理以得到无干扰EEG信号；
S200、采用改进的消除趋势滑动平均算法计算所述无干扰EEG信号的波动函数，并根据所述波动函数计算所述麻醉深度指数。
本实施例中，首先对采集到的患者的脑电信号EEG进行预处理以消除低频噪声和尖波干扰得到无干扰EEG信号，在对所述EEG信号采用改进的消除趋势滑动平均算法进行计算得到麻醉深度指数。这样使得麻醉深度监测具有更小的时间延迟，能够更快速的反映麻醉深度的变化情况。并且，通过使用改进的消除趋势滑动平均算法(MDMA)，信号波动指数范围更大。在信号质量较差的情况下，也可以准确监测到稳定的麻醉深度指数。
具体的来说，在步骤S100中，所述EEG信号为脑电信息。所述采集患者的EEG信号指的是采用患者的脑电信号。所述采集得到的脑电信号为具有干扰的脑电信号，如，噪声、尖波干扰等。从而，在采集到患者EEG信号后，需要对所述EEG信号进行预处理以去除干扰。在本实施例中，采用小波熵阈值的方法对所述EEG信号进行预处理得到无干扰EEG信号。
示例性的，如图2、3和4所示，所述采集患者的EEG信号，并对所述EEG信号进行预处理以得到无干扰EEG信号具体包括：
S101、采用患者的EEG信号，对所述EEG信号应用离散小波变换以得到近似小波系数和细节小波系数；
S102、计算所述一固定窗口长度的EEG信号的小波熵阈值；
S103、采用多的小波熵阈值对所述细节小波系数进行软阈值非线性处理；
S104、对处理后的细节小波系数进行小波逆变换以估算低频噪声信号；
S105、根据所述低频噪声信号以及EEG信号得到无干扰的EEG信号。
具体的来说，在所述步骤S101中，所述对所述EEG信号应用离散小波变换指的是采用db16小波对所述EEG信号进行小波包分解得到近似小波系数和细节小波系数，其中，所述近似小波系数和细节小波系数可以表示为：
其中，A
j(k)为近似小波系数，D
j(k)为和细节小波系数，j为小波分解层数，低通滤波器g和高通滤波器h均采用db16小波。
在所述步骤S102中，所述小波熵阈值的计算公式可以为：
其中，Em是根据相关小波能量计算的小波熵，m表示一次处理EEG信号的长度，G和Voffset为离线分析中得到的经验值，优选为，G＝10和Voffset＝8。
进一步，所述小波熵Em的计算过程可以为：
首先选取一次处理EEG信号的长度(窗口长度)m，将时刻k的能量定义为：
那么，整个窗口内的总能量为：
于是，相关小波能量为；
进而，所述小波熵的计算公式可以为：
在所述步骤S103中，采用小波熵阈值对细节小波系数进行软阈值非线性处理的具体处理方式为：
在所述步骤S104中，所述对处理后的细节小波系数
进行小波逆变换具体为对所述处理后的细节小波系数
进行离散小波逆变换，例如，采用db16小波进行离散小波逆变换来估算低频噪声信号e
i。
在所述步骤S105中，所述根据所述低频噪声信号以及EEG信号得到无干扰的EEG信号具体为采用所述EEG信号减去低频噪声信号得到消除噪声的EEG信号，即无干扰EEG信号。所述无干扰EEG信号的计算公式可以为：
x
i＝y
i-e
i
其中，x
i为无干扰EEG信号，y
i为采集的EEG信号。
进一步，在所述步骤S200中，所述采用改进的消除趋势滑动平均算法计算所述无干扰EEG信号的波动函数指的是将所述无干扰EEG信号看做是脑电信号时间序列x
i，i＝1,2,…,L，对所述脑电信号时间序列采用改进的消除趋势滑动平均DMA算法来计算麻醉深度指数。
在本实施例中，如图5、6和7所示，所述步骤S200、采用改进的消除趋势滑动平均算法计算所述无干扰EEG信号的波动函数，并根据所述波动函数计算所述麻醉深度指数具体包括：
S201、设所述无干扰EEG信号为脑电信号时间序列，并获取所述脑电信号时间序列的均值；
S202、根据所述均值确定所述脑电信号时间序列的累加序列，并采用后向滑动平均值法计算一固定窗口长度的趋势序列；
S203、根据所述趋势序列计算所述脑电信号时间序列的消除趋势序列，并根据所述消除趋势序列计算所述窗口长度无干扰EEG信号的波动值；
S204、根据所述波动值计算所述窗口长度无干扰EEG信号的波动函数，并根据所述波动函数计算所述麻醉深度指数。
具体的来说，在所述步骤S201中，假设所述无干扰EEG信号为脑电信号时间序列x
i，i＝1,2,…,L，计算脑电信号时间序列x(i)的均值X
mean，其中，所述X
mean的计算公式可以为：
其中，K表示时刻。
在所述步骤S202中，所述根据所述均值确定所述脑电信号时间序列的累加序列具体为：脑电信号时间序列x
i的每一项均减去所述均值X
mean得到一个新的时间序列，再对新的时间序列进行累加求和得到求和序列。在选取一个固定的窗口长度s，采用简单后向滑动平均法求取趋势序列。在实际应用中，所述累加序列Y(i)的计算公式可以为：
所述趋势序列
的公式可以为：
在所述步骤S203中，所述根据所述趋势序列计算所述脑电信号时间序列的消除趋势序列具体是从累加序列Y(i)中减去趋势序列
得到消除趋势的序列C
s(i)，所述消除趋势的序列C
s(i)的计算公式可以为：
再计算得到消除趋势的序列后，根据所述消除趋势的序列计算窗口长度为s时所述EEG信号对应的波动值
所述波动值的计算公式可以为：
其中，L
s＝[L/s]，v＝1，...，2L
s。
在所述步骤S204中，由波动值
计算波动函数F
MDMA(s)，所述波动函数计算公式如下：
其中，Em是根据相关小波能量计算的小波熵。
获取所述波动函数之后，根据所述波动函数计算麻醉深度指数F
min(s)，所述麻醉深度指数的计算公式可以为：
其中，
为增益指数，优选为
本发明还提供了一种基于脑电信号的麻醉深度的监测系统，如图6所示，其具体包括：
处理模块101，用于采集患者的EEG信号，并对所述EEG信号进行预处理以得到无干扰EEG信号；
计算模块102，用于采用改进的消除趋势滑动平均算法计算所述无干扰EEG信号的波动函数，并根据所述波动函数计算所述麻醉深度指数。
所述基于脑电信号的麻醉深度的监测系统，其中，所述处理模块具体用于：
采集患者的EEG信息，并采用小波熵阈值法消除所述EEG信号的噪声干扰以得到无干扰EEG信号。
所述基于脑电信号的麻醉深度的监测系统，其中，所述计算模块具体包括：
获取单元，用于设所述无干扰EEG信号为脑电信号时间序列，并获取所述脑电信号时间序列的均值；
第一计算单元，用于根据所述均值确定所述脑电信号时间序列的累加序列，并采用后向滑动平均值法计算一固定窗口长度的趋势序列；
第一计算单元，用于根据所述趋势序列计算所述脑电信号时间序列的消除趋势序列，并根据所述消除趋势序列计算所述窗口长度无干扰EEG信号的波动值；
第三计算单元，用于根据所述波动值计算所述窗口长度无干扰EEG信号的波动函数，并根据所述波动函数计算所述麻醉深度指数。
所述基于脑电信号的麻醉深度的监测系统，其中，所述波动函数为：
其中，E
m为小波熵，s为窗口长度，L为正整数；
表示波动值。
所述基于脑电信号的麻醉深度的监测系统，其中，所述麻醉深度指数为：
其中，所述
为增益指数，F
MDMA(s)为波动函数。
在本发明的一个实施例中，所述基于脑电信号的麻醉深度的监测系统的所述对脑电信号的处理以及根据脑电信号检测麻醉深度的指数可以通过处理器实现，也就是说，如图7所示，其可以包括:
采集模块201，用于采集患者的脑电信息；
处理器202，用于去除脑电信号中的伪迹，并对去除伪迹和噪声后的脑电信号应用消除趋势滑动平均方法，得到指示麻醉深度的指数F
min(s)。
在本实施例中，所述基于脑电信号的麻醉深度的监测系统还可以包括：
A/D转换器203，用于模拟脑电信号转换成数字信号，并将所述数字信号传输至处理器。
值得说明的，所述处理器计算麻醉深度的指数F
min(s)的过程在上述方法中已经说明，这里不在赘述。
上述基于脑电信号的麻醉深度的监测系统的各个模块在上述方法中已经详细说明，在这里就不再一一陈述。
在本发明所提供的实施例中，应该理解到，所揭露的系统和方法，可以通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如，所述模块的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，装置或单元的间接耦合或通信连接，可以是电性，机械或其它的形式。
所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
另外，在本发明各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用硬件加软件功能单元的形式实现。
上述以软件功能单元的形式实现的集成的单元，可以存储在一个计算机可读取存储介质中。上述软件功能单元存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)或处理器(processor)执行本发明各个实施例所述方法的部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(Read-Only Memory，ROM)、随机存取存储器(Random Access Memory，RAM)、磁碟或者光盘等各种可以存储程序代码的介质。
最后应说明的是：以上实施例仅用以说明本发明的技术方案，而非对其限制；尽管参照前述实施例对本发明进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本发明各实施例技术方案的精神和范围。
Claims (10)
- 一种基于脑电信号的麻醉深度的监测方法，其特征在于，其包括：采集患者的EEG信号，并对所述EEG信号进行预处理以得到无干扰EEG信号；采用改进的消除趋势滑动平均算法计算所述无干扰EEG信号的波动函数，并根据所述波动函数计算所述麻醉深度指数。
- 根据权利要求1所述基于脑电信号的麻醉深度的监测方法，其特征在于，所述采集患者的EEG信号，并对所述EEG信号进行预处理以得到无干扰EEG信号具体为：采集患者的EEG信息，并采用小波熵阈值法消除所述EEG信号的噪声干扰以得到无干扰EEG信号。
- 根据权利要求1所述基于脑电信号的麻醉深度的监测方法，其特征在于，所述采用改进的消除趋势滑动平均算法计算所述无干扰EEG信号的波动函数，并根据所述波动函数计算所述麻醉深度指数具体为：设所述无干扰EEG信号为脑电信号时间序列，并获取所述脑电信号时间序列的均值；根据所述均值确定所述脑电信号时间序列的累加序列，并采用后向滑动平均值法计算一固定窗口长度的趋势序列；根据所述趋势序列计算所述脑电信号时间序列的消除趋势序列，并根据所述消除趋势序列计算所述窗口长度无干扰EEG信号的波动值；根据所述波动值计算所述窗口长度无干扰EEG信号的波动函数，并根据所述波动函数计算所述麻醉深度指数。
- 根据权利要求1或3所述基于脑电信号的麻醉深度的监测方法，其特征在于，所述波动函数为：
其中，E m为小波熵，s为窗口长度，L为正整数；表示波动值。
- 根据权利要求4所述基于脑电信号的麻醉深度的监测方法，其特征在于，所述麻醉深度指数为：
其中，所述为增益指数，F MDMA(s)为波动函数。
- 一种基于脑电信号的麻醉深度的监测系统，其特征在于，其包括：处理模块，用于采集患者的EEG信号，并对所述EEG信号进行预处理以得到无干扰EEG信号；计算模块，用于采用改进的消除趋势滑动平均算法计算所述无干扰EEG信号的波动函数，并根据所述波动函数计算所述麻醉深度指数。
- 根据权利要求6所述基于脑电信号的麻醉深度的监测系统，其特征在于，所述处理模块具体用于：采集患者的EEG信息，并采用小波熵阈值法消除所述EEG信号的噪声干扰以得到无干扰EEG信号。
- 根据权利要求6所述基于脑电信号的麻醉深度的监测系统，其特征在于，所述计算模块具体包括：获取单元，用于设所述无干扰EEG信号为脑电信号时间序列，并获取所述脑电信号时间序列的均值；第一计算单元，用于根据所述均值确定所述脑电信号时间序列的累加序列，并采用后向滑动平均值法计算一固定窗口长度的趋势序列；第一计算单元，用于根据所述趋势序列计算所述脑电信号时间序列的消除趋势序列，并根据所述消除趋势序列计算所述窗口长度无干扰EEG信号的波动值；第三计算单元，用于根据所述波动值计算所述窗口长度无干扰EEG信号的波动函数，并根据所述波动函数计算所述麻醉深度指数。
- 根据权利要求6或8所述基于脑电信号的麻醉深度的监测系统，其特征在于，所述波动函数为：
其中，E m为小波熵，s为窗口长度，L为正整数；表示波动值。
- 根据权利要求9所述基于脑电信号的麻醉深度的监测系统，其特征在于，所述麻醉深度指数为：
其中，所述为增益指数，F MDMA(s)为波动函数。
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