WO2019127558A1 - 基于脑电的麻醉深度监测方法和装置 - Google Patents

基于脑电的麻醉深度监测方法和装置 Download PDF

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WO2019127558A1
WO2019127558A1 PCT/CN2017/120350 CN2017120350W WO2019127558A1 WO 2019127558 A1 WO2019127558 A1 WO 2019127558A1 CN 2017120350 W CN2017120350 W CN 2017120350W WO 2019127558 A1 WO2019127558 A1 WO 2019127558A1
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anesthesia
signal
eeg
anesthesia depth
electroencephalogram
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PCT/CN2017/120350
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English (en)
French (fr)
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金星亮
罗汉源
何先梁
张宁玲
叶志刚
李明
姚祖明
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深圳迈瑞生物医疗电子股份有限公司
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Priority to CN201780085018.8A priority Critical patent/CN110267590A/zh
Priority to PCT/CN2017/120350 priority patent/WO2019127558A1/zh
Publication of WO2019127558A1 publication Critical patent/WO2019127558A1/zh

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    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/24Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof

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  • the invention relates to the field of inducing electrophysiological signal processing, in particular to an electrocardiogram based anesthesia depth monitoring method and device.
  • proper depth of anesthesia is a necessary condition for surgery.
  • Different clinical needs have different requirements for the depth control of anesthesia. For example, for a large-surgery operation, a deeper degree of anesthesia is required to ensure that the patient does not have an anesthesia accident such as intraoperative awareness due to surgical stimulation; and for a small stimulation operation, it is necessary to effectively control the amount of the anesthetic to make the patient fit.
  • the depth of anesthesia can quickly wake up after the end of surgery, while saving the amount of anesthetic use.
  • anesthesia Depth Monitoring Instrument the method of feature weighted summation is used to obtain the depth of anesthesia.
  • the anesthesia depth is obtained by weighted summation using a Beta Ratio, a Bispectral Index (BIS), a Burst Suppression (BSR), and a Suppression Index (QUAZI).
  • the depth of anesthesia obtained by this weighted summation method is not accurate enough.
  • the anesthetic depth values that are fed back under different drug types for patients of different age groups are different from the actual anesthesia depth levels.
  • an anesthesia depth monitoring instrument also has a method of calculating the depth of anesthesia. It obtains the current anesthesia status (divided into awake, shallow hemp, medium hemp, deep anesthesia and excessive anesthesia) through the C4.5 decision tree, and then obtains the depth of anesthesia according to different anesthesia states.
  • the C4.5 decision tree has a clear logical relationship and is a classic single tree structure in the decision tree algorithm.
  • different patient types, different drug types, and corresponding anesthesia depths require a large amount of data to establish a correspondence.
  • Using the C4.5 decision tree with a large amount of data is prone to over-fitting problems, even if pruning or the like does not solve the problem well. Therefore, the use of the C4.5 decision tree to determine the state of anesthesia cannot be adapted to the complex clinical anesthesia needs.
  • the present invention has been made in view of the above circumstances, and an object thereof is to provide an electroencephalogram-based anesthesia depth monitoring method and apparatus capable of avoiding over-fitting problems in a large data amount.
  • an aspect of the present invention provides an electroencephalogram-based anesthesia depth monitoring method, including: acquiring an electroencephalogram signal by a sensor; performing denoising processing on the electroencephalogram signal; and extracting the brain after denoising processing A signal characteristic of the electrical signal; and determining an anesthesia depth value based on the signal characteristic and a predetermined anesthesia depth prediction model.
  • the anesthesia depth prediction model is trained by an anesthesia depth database comprising a plurality of anesthesia full-course EEG signals and an anesthesia state corresponding to each segment of the EEG signals in each anesthesia.
  • the anesthesia depth prediction model is trained using the anesthesia depth database, and the anesthesia depth value is confirmed by the collected electroencephalogram signal and the trained anesthesia depth prediction model. In this case, over-fitting problems can be avoided in the case of large data volumes.
  • the method further includes displaying the anesthesia depth value.
  • the anesthesia depth value can be obtained intuitively.
  • the acquired EEG signal is a simulated EEG signal
  • the simulated EEG signal is before the EEG signal is denoised.
  • a pre-amplification process and an analog-to-digital conversion process are performed to obtain a digital EEG signal. Thereby, the storage of the EEG signal is facilitated.
  • the denoising process includes processing a physiological interference signal and a non-physiological interference signal. Thereby, the interference signal is removed, and the anesthesia depth value can be obtained more accurately.
  • the signal characteristics include time domain features, frequency domain features, and nonlinear domain features.
  • the anesthesia depth value can be obtained according to the time domain feature, the frequency domain feature and the nonlinear domain feature, and the preset anesthesia depth prediction model.
  • the time domain characteristic includes an explosion suppression ratio
  • the frequency domain characteristic includes an electroencephalogram related energy ratio
  • the nonlinear domain characteristic includes information entropy .
  • the anesthesia state corresponding to each segment of the EEG signal includes awake, sedation, general anesthesia, deep anesthesia, excessive anesthesia, and no brain electrical activity.
  • the data in the anesthesia depth database is more comprehensive.
  • the anesthesia depth database further includes one or more of population information, drug information, and a type of surgery corresponding to an anesthesia full-course EEG signal.
  • the predetermined anesthesia depth prediction model has a decision tree number of at least 200, and each decision tree has no more than three layers. Thereby, it is possible to avoid an over-fitting problem in the case where the amount of data is large.
  • an electroencephalogram-based anesthesia depth monitoring apparatus comprising: an acquisition module that acquires an electroencephalogram signal through a sensor; a denoising module that performs denoising processing on the EEG signal; and an extraction module And extracting a signal characteristic of the electroencephalogram signal after the denoising process; and a calculation module determining the anesthesia depth value according to the signal characteristic and the preset anesthesia depth prediction model.
  • the anesthesia depth prediction model is obtained from an anesthesia depth database comprising a plurality of anesthesia full-course EEG signals and an anesthetic state corresponding to each segment of the EEG signals in each anesthesia.
  • the calculation module confirms the anesthesia depth value by the acquired EEG signal and the trained anesthesia depth prediction model, and the anesthesia depth prediction model is obtained by training the anesthesia depth database. In this case, over-fitting problems can be avoided in the case of large data volumes.
  • a display module for displaying the anesthesia depth value is further included. Thereby, the anesthesia depth value can be obtained intuitively through the display module.
  • the acquired EEG signal is a simulated EEG signal
  • the EEG signal is denoised Previously
  • the analog EEG signal was subjected to preamplification processing and analog-to-digital conversion processing to obtain a digital EEG signal.
  • the anesthesia depth value can be obtained by the digital EEG signal and the preset anesthesia depth prediction model.
  • the denoising process includes processing a physiological interference signal and a non-physiological interference signal. Thereby, the interference signal is removed, and the anesthesia depth value can be obtained more accurately.
  • the signal characteristics include time domain features, frequency domain features, and nonlinear domain features.
  • the anesthesia depth value can be obtained according to the time domain feature, the frequency domain feature and the nonlinear domain feature, and the preset anesthesia depth prediction model.
  • the time domain characteristic includes an explosion suppression ratio
  • the frequency domain characteristic includes an electroencephalogram related energy ratio
  • the nonlinear domain characteristic includes information entropy.
  • the anesthesia state corresponding to each segment of the EEG signal includes awake, sedation, general anesthesia, deep anesthesia, excessive anesthesia, and no EEG activity.
  • the data in the anesthesia depth database is more comprehensive.
  • the anesthesia depth database further includes one or more of population information, medication information, and a type of surgery corresponding to an anesthesia global electroencephalogram signal.
  • the predetermined anesthesia depth prediction model has a decision tree number of at least 200, and each decision tree has no more than three layers. . Thereby, it is possible to avoid an over-fitting problem in the case where the amount of data is large.
  • an electroencephalogram-based anesthesia depth monitoring apparatus including: a sensor for collecting an electroencephalogram signal; a memory for storing the collected electroencephalogram signal; and a processor The method is configured to: extract a signal characteristic of the EEG signal; determine an anesthesia depth value according to the signal characteristic and a preset anesthesia depth prediction model.
  • the anesthesia depth prediction model is obtained from an anesthesia depth database comprising a plurality of anesthesia full-course EEG signals and an anesthetic state corresponding to each segment of the EEG signals in each anesthesia.
  • anesthesia depth database comprising a plurality of anesthesia full-course EEG signals and an anesthetic state corresponding to each segment of the EEG signals in each anesthesia.
  • the signal characteristic includes one or more of a time domain characteristic, a frequency domain characteristic, and a nonlinear domain characteristic.
  • the anesthesia depth value can be obtained according to the time domain feature, the frequency domain feature and the nonlinear domain feature, and the preset anesthesia depth prediction model.
  • the time domain characteristic includes an explosion suppression ratio
  • the frequency domain characteristic includes an electroencephalogram related energy ratio
  • the nonlinear domain characteristic includes information entropy.
  • the processor further performs denoising processing on the acquired electroencephalogram signal before performing the step. Thereby, the interference signal is removed, and the anesthesia depth value can be obtained more accurately.
  • the anesthesia depth database further includes one or more of population information, medication information, and a type of surgery corresponding to an anesthesia global electroencephalogram signal.
  • the predetermined anesthesia depth prediction model has a decision tree number of at least 200, and each decision tree has no more than three layers. . Thereby, it is possible to avoid an over-fitting problem in the case where the amount of data is large.
  • FIG. 1 is a schematic structural view showing an electroencephalogram-based anesthesia depth monitoring apparatus according to the present embodiment.
  • FIG. 2 is a schematic diagram showing the structure of an acquisition module of an electroencephalogram-based anesthesia depth monitoring apparatus according to the present embodiment.
  • FIG. 3 is a schematic structural view showing a denoising module of an electroencephalogram-based anesthesia depth monitoring device according to the present embodiment.
  • FIG. 4 is a schematic view showing the structure of another electroencephalogram-based anesthesia depth monitoring device according to the present embodiment.
  • FIG. 5 is a schematic structural view showing another electroencephalogram-based anesthesia depth monitoring apparatus according to the present embodiment.
  • Fig. 6 is a flow chart showing a method for monitoring anesthesia depth based on electroencephalogram according to the present embodiment.
  • Fig. 7 is a flow chart showing another method of monitoring anesthesia depth based on electroencephalogram according to the present embodiment.
  • FIG. 1 is a schematic structural view showing an electroencephalogram-based anesthesia depth monitoring apparatus according to the present embodiment.
  • FIG. 2 is a schematic diagram showing the structure of an acquisition module of an electroencephalogram-based anesthesia depth monitoring apparatus according to the present embodiment.
  • 3 is a schematic structural view showing a denoising module of an electroencephalogram-based anesthesia depth monitoring device according to the present embodiment.
  • 4 is a schematic diagram showing the configuration of a calculation module of an electroencephalogram-based anesthesia depth monitoring apparatus according to the present embodiment.
  • the present embodiment discloses an electroencephalogram-based anesthesia depth monitoring device 1 that can be used for anesthesia monitoring.
  • the electroencephalogram-based anesthesia depth monitoring apparatus 1 may include an acquisition module 10 , a denoising module 20 , an extraction module 30 , and a calculation module 40 .
  • the acquisition module 10 can acquire an EEG signal through a sensor.
  • the acquisition module 10 can acquire an EEG signal through the electrode pads.
  • the acquisition module 10 can also acquire an EEG signal through an EEG acquisition device or the like.
  • the EEG signal collected by the sensor may be a simulated EEG signal.
  • the acquired EEG signals may include various EEG signals of the patient throughout the anesthesia.
  • the acquisition module 10 may further include an amplification unit 110.
  • the amplification unit 110 may be a preamplifier.
  • the amplifying unit 110 can perform preamplification processing on the analog EEG signal. Thereby, the simulated EEG signal can be amplified to improve the power and signal quality of the simulated brain signal.
  • the acquisition module 10 may further include an analog to digital conversion unit 120.
  • the analog to digital conversion unit 120 may be an analog to digital (A/D) converter.
  • the analog to digital conversion unit 120 can perform analog to digital conversion processing on the analog EEG signal. That is, the analog to digital conversion unit 120 can convert the amplified analog EEG signal into a digital EEG signal. Specifically, the analog-to-digital conversion mainly samples the analog EEG signal and then quantizes it into a digital EEG signal. Thereby, the storage of the EEG signal is facilitated.
  • the electroencephalogram-based anesthesia depth monitoring device 1 may further include a storage module (not shown).
  • the storage module can be used to receive and store the EEG signals output by the acquisition module 10.
  • the storage module can be a memory such as a RAM, a memory stick, or the like.
  • the electroencephalogram-based anesthesia depth monitoring device 1 may further include a denoising module 20 .
  • the denoising module 20 can be a processor such as a central processing unit (CPU), a micro processor (MPU), an application specific integrated circuit (ASIC), or the like.
  • CPU central processing unit
  • MPU micro processor
  • ASIC application specific integrated circuit
  • the embodiment is not limited thereto, and the denoising module 20 may also be a filter.
  • the denoising module 20 can identify and process the interfering signals in the EEG signals. That is, the denoising module 20 can be used to perform denoising processing on the EEG signal.
  • the interference signal may include a physiological interference signal and a non-physiological interference signal.
  • the denoising module 20 performs denoising processing on the EEG signal, and may perform denoising processing on the digital EEG signal.
  • the acquisition module 10 can transmit the digital EEG signal to the denoising module 20, and the denoising module 20 can receive the digital EEG signal from the interface, and then identify and process the physiological and non-physiological factors in the digital EEG signal. The resulting interference signal.
  • the denoising module 20 may include identifying and removing the physiological interference signal unit 210. Removing the physiological interference signal unit 210 can remove the physiological interference signal.
  • the physiological interference signal may include an eye movement interference signal and a myoelectric interference signal in the brain electrical signal.
  • the eye movement interference signal is mainly the low frequency waveform caused by blinking and eyeball rotation
  • the myoelectric interference signal is mainly the high frequency waveform caused by muscle discharge. Thereby, the interference signal is removed, and the anesthesia depth value can be obtained more accurately.
  • the denoising module 20 may further include a non-physiological interference signal unit 220. Removing the non-physiological interference signal unit 220 may remove the non-physiological interference signal.
  • Non-physiological interference signals may include abnormal signal amplitudes in the EEG signals, abnormal signal slopes, and electrosurgical interference.
  • the interference signal caused by the signal amplitude and the abnormal slope is mainly the interference signal caused by the external pressing of the touch sensor.
  • the electric knife interference is mainly caused by the impact of the high-frequency electric knife on the impact of the acquisition system during the operation.
  • the EEG signal processing apparatus 1 may further include an extraction module 30.
  • the extraction module 30 can be a processor such as a central processing unit (CPU), a micro processor (MPU), an application specific integrated circuit (ASIC), or the like.
  • the extraction module 30 can be used to extract signal characteristics of the EEG signal after the denoising process.
  • the signal characteristics can include at least one of a time domain feature, a frequency domain feature, and a non-linear domain feature.
  • the time domain feature can include an outbreak suppression ratio.
  • the burst suppression ratio can be obtained by dividing the length of the suppression signal by the length of the entire segment of the signal.
  • the frequency domain characteristics may include an EEG-related energy ratio.
  • the energy ratio is the proportion of energy in different frequency bands of the EEG signal to the total energy.
  • Nonlinear domain features may include information entropy. The method of calculating information entropy is to calculate the probability that each waveform amplitude in a length of EEG waveform signal appears in the entire segment of the signal.
  • the EEG signal processing apparatus 1 may further include a calculation module 40.
  • the computing module 40 can be a processor such as a central processing unit (CPU), a micro processor (MPU), an application specific integrated circuit (ASIC), or the like.
  • the calculation module 40 can be configured to determine an anesthesia depth value based on the signal characteristics and a predetermined anesthesia depth prediction model.
  • the anesthesia depth prediction model is trained by the anesthesia depth database.
  • the anesthesia depth database contains a plurality of anesthesia full-encephalic EEG signals and anesthesia status corresponding to each segment of EEG signals in each anesthesia.
  • the anesthesia depth database may include different EEG signals, signal characteristics of the EEG signals, and correspondence between each segment of the EEG signals and the anesthetic state (ie, the anesthesia depth database includes various segments of EEG signals). Correspondence between signal characteristics and anesthesia status).
  • the signal characteristic of the EEG signal in the anesthesia depth database may be at least one of a time domain feature, a frequency domain feature, and a nonlinear domain feature. Therefore, the correspondence between the signal characteristics of different EEG signals and the anesthetic state may be a correspondence between at least one of the time domain characteristics, the frequency domain characteristics, and the nonlinear domain characteristics of different EEG signals and the anesthesia state.
  • the anesthesia state in the anesthesia depth database may be that the anesthesiologist indicates the current anesthesia state based on the electroencephalogram waveform and the clinical anesthesia state corresponding to each segment of the EEG signal.
  • the anesthesia status corresponding to each segment of EEG signals includes one or more of awake, sedation, general anesthesia, deep anesthesia, excessive anesthesia, and no EEG activity.
  • the anesthesia depth database further includes one or more of crowd information, medication information, and type of surgery corresponding to the anesthesia full-course EEG signal.
  • the calculation module 40 can obtain an anesthesia depth prediction model through the above-described anesthesia depth database training. Specifically, the calculation module 40 can train the anesthesia depth prediction model through the signal characteristics of the anesthesia full-encephalic EEG signal in the anesthesia depth database and the anesthesia state corresponding to each segment of the EEG signal in each anesthesia. In some examples, computing module 40 may train anesthesia depth prediction by correspondence between at least one of time domain features, frequency domain features, and non-linear domain features of the different EEG signals in the anesthesia depth database and anesthesia status model. Thereby, the correspondence relationship between the EEG signal and the anesthesia depth in the anesthesia depth prediction model can be obtained more accurately.
  • the predetermined number of decision trees of the anesthesia depth prediction model may include at least 200 trees, and the number of layers of each decision tree does not exceed 3 layers. However, the embodiment is not limited thereto, and for example, the number of decision trees may include at least 300 trees.
  • an anesthetic depth value of 0 to 100 is obtained. There is a correspondence between anesthesia status and anesthesia value, as shown in Table 1.
  • the signal characteristics of the extraction module 30 may include one or more of a time domain feature, a frequency domain feature, and a non-linear domain feature.
  • the calculation module 40 is capable of obtaining an anesthesia depth value based on one or more of the time domain features, the frequency domain features, and the non-linear domain features and the predetermined anesthesia depth prediction model.
  • the time domain feature may include an explosion suppression ratio
  • the frequency domain feature may include an EEG related energy ratio
  • the nonlinear domain feature may include information entropy.
  • the calculation module 40 can obtain an anesthesia depth value based on the burst suppression ratio, the EEG-related energy ratio, and the information entropy and the preset anesthesia depth prediction model.
  • FIG. 4 is a schematic view showing the structure of another electroencephalogram-based anesthesia depth monitoring device according to the present embodiment.
  • the EEG signal processing apparatus 1 may further include a display module 50.
  • Display module 50 can be a screen or other device.
  • display module 50 can be a plurality of indicator lights. Different indicators indicate different anesthesia status.
  • the display module 50 can also be a display screen, such as a liquid crystal display device, an LED display device, or an LCD display device.
  • the display module 50 is configured to display an anesthesia depth value. Thereby, the anesthesia depth value can be obtained intuitively through the display module.
  • the present embodiment is not limited to this, and an anesthesia state may be displayed, and both the anesthesia depth value and the anesthesia state may be displayed.
  • the anesthesia depth value is confirmed by the acquired EEG signal and the trained anesthesia depth prediction model, and the anesthesia depth prediction model is obtained by training the anesthesia depth database.
  • over-fitting problems can be avoided in the case of large data volumes.
  • FIG. 5 is a schematic structural view showing another electroencephalogram-based anesthesia depth monitoring apparatus according to the present embodiment.
  • the EEG signal processing apparatus 1 may include a sensor 11, a memory 12, a processor 13, and a display module 14.
  • the senor 11 can collect an EEG signal.
  • sensor 11 can be an electrode sheet.
  • the EEG signal acquired by the sensor 11 may be a simulated EEG signal.
  • the electroencephalogram signal processing device 1 may further include a preamplifier (not shown) and an analog-to-digital (A/D) converter (not shown). Since the quality of the simulated EEG signal collected by the sensor 11 may be poor, the acquired EEG signal can be preamplified by the preamplifier. Thereby, the EEG signal can be amplified to improve the power and signal quality of the EEG signal.
  • An analog-to-digital (A/D) converter can simulate the conversion of EEG signals into digital EEG signals. Thereby, the storage of the EEG signal is facilitated.
  • the memory 12 can store the collected EEG signals.
  • the memory can be RAM, FIFO, memory stick, and the like.
  • the processor 13 can extract the signal characteristics of the EEG signal.
  • the signal characteristics include one or more of a time domain feature, a frequency domain feature, and a nonlinear domain feature.
  • the time domain feature can include an outbreak suppression ratio.
  • the burst suppression ratio can be obtained by dividing the length of the suppression signal by the length of the entire segment of the signal.
  • the frequency domain characteristics may include an EEG-related energy ratio.
  • the energy ratio is the proportion of energy in different frequency bands of the EEG signal to the total energy.
  • Nonlinear domain features may include information entropy. The method of calculating information entropy is to calculate the probability that each waveform amplitude in a length of EEG waveform signal appears in the entire segment of the signal.
  • the processor 13 may also determine an anesthesia depth value based on the signal characteristics and a predetermined anesthesia depth prediction model.
  • the anesthesia depth prediction model is trained by the anesthesia depth database.
  • the anesthesia depth database contains a plurality of anesthesia full-encephalic EEG signals and anesthesia status corresponding to each segment of EEG signals in each anesthesia.
  • the anesthesia status corresponding to each segment of the EEG signal includes one or more of awake, sedation, general anesthesia, deep anesthesia, excessive anesthesia, and no EEG activity.
  • the anesthesia depth database also includes one or more of population information, medication information, and type of surgery corresponding to the anesthesia signal.
  • the predetermined anesthesia depth prediction model has a decision tree number of at least 200 and the number of layers per decision tree does not exceed three. Processor 13 is similar to computing module 40 described above.
  • the processor 13 can also perform denoising processing on the acquired EEG signals.
  • the denoising process of the processor 13 can denoise the acquired EEG signals by an algorithm.
  • the denoising of the EEG signal can be realized by the hardware circuit.
  • the denoising process in the denoising module 20 described above may be referred to.
  • the EEG signal processing apparatus 1 may further include a display module 14.
  • the display module 14 is similar to the display module 50 described above.
  • Fig. 6 is a flow chart showing a method for monitoring anesthesia depth based on electroencephalogram according to the present embodiment.
  • the electroencephalogram-based anesthesia depth monitoring method includes acquiring an electroencephalogram signal by a sensor (step S101).
  • the EEG signal can be acquired by the sensor, and the EEG signal of the patient can also be collected by the EEG device.
  • the EEG device can be any of a variety of devices known to those skilled in the art for collecting EEG signals.
  • the EEG signal collected by the sensor may be a simulated EEG signal. Simulated EEG signals can usually be converted into digital EEG signals.
  • a pre-amplification process and an analog-to-digital conversion process are performed on the simulated EEG signal to obtain a digital EEG signal.
  • the preamplification process can amplify the simulated EEG signal and improve the power and signal quality of the simulated EEG signal.
  • the analog to digital conversion process can convert the amplified analog EEG signal into a digital EEG signal. Thereby, the storage of the EEG signal is facilitated.
  • the electroencephalogram-based anesthesia depth monitoring method may further include performing denoising processing on the electroencephalogram signal (step S102).
  • the denoising process includes identifying and processing the physiological interference signal and the non-physiological interference signal. Denoising the EEG signal can improve the quality of the EEG signal. Thereby, the interference signal is removed, and the anesthesia depth value can be obtained more accurately.
  • the physiological interference signal may include an eye movement interference signal and a myoelectric interference signal.
  • the eye movement interference signal is usually a low frequency waveform caused by blinking and eyeball rotation.
  • the eye movement interference signal may be a frequency band below 10hz in the EEG signal.
  • the eye movement interference signal frequency band it can be identified by analyzing the abnormal low frequency signal in the EEG waveform, and the segment data is deleted after the eye movement interference is recognized.
  • the myoelectric interference signal is usually a high frequency mutation caused by muscle discharge.
  • the myoelectric interference signal can be a frequency band above 50hz in the EEG signal.
  • the frequency band of the myoelectric interference signal it can be identified by analyzing the high frequency abnormal mutation in the EEG waveform, and the data is removed after the high frequency interference of the myoelectric is recognized.
  • the non-physiological interference signal may include abnormal signal amplitude, abnormal signal slope, and electric knife interference.
  • the abnormal signal amplitude and the abnormality of the signal slope are usually caused by the external pressing of the touch sensor.
  • the signal amplitude usually refers to the maximum value of the signal fluctuation over a period of time.
  • the slope of the signal is usually the maximum of the difference between two adjacent sampling points.
  • the segment data can be eliminated after the amplitude and slope abnormal interference are recognized.
  • the electrosurgical interference is usually caused by an impact of the high-frequency electric knife on the acquisition system during the operation.
  • the electric knife interference can be identified by abnormal fluctuations in the high frequency band.
  • the high frequency band generally refers to the frequency band above 500hz.
  • the segment data is rejected after the electric knife interference is recognized.
  • step S102 the denoising process is performed on the EEG signal, and the digital EEG signal may be denoised to obtain an effective digital EEG signal.
  • the electroencephalogram-based anesthesia depth monitoring method may further include extracting a signal characteristic of the electroencephalogram signal after the denoising process (step S103).
  • the signal feature may include at least one of a time domain feature, a frequency domain feature, and a nonlinear domain feature.
  • the time domain feature can calculate the burst suppression ratio (BSR).
  • Burst suppression is a waveform that occurs when the brain is under deep anesthesia. It is characterized by a small amplitude suppression waveform alternating with a large burst waveform.
  • the EEG signal fluctuation range is less than 5 uv and the duration exceeds 0.5 second, it can be considered as a suppression signal.
  • the burst suppression ratio can be obtained by dividing the length of the suppression signal by the length of the entire segment of the signal.
  • the burst suppression ratio is a quantitative indicator for evaluating the burst suppression waveform.
  • the calculation formula for the burst suppression ratio is as follows:
  • the time domain feature may further include a signal amplitude, a maximum and minimum value, a difference between adjacent points of the signal (slope of the waveform), an extreme value of the adjacent point difference of the signal, and a zero crossing of the waveform.
  • the situation that is, the number of times the waveform crosses the zero point per unit time).
  • the frequency domain characteristic may be an EEG-related energy ratio.
  • the energy ratio is the proportion of energy in different frequency bands of the EEG signal to the total energy.
  • different frequency bands may include a delta wave with a frequency range of 0.5 to 3 hz, a theta wave with a frequency range of 4 to 7 hz, an alpha wave with a frequency range of 8 to 13 hz, and a frequency range of 14 to 30 hz.
  • the total energy frequency can range from 0.5 to 47 hz.
  • energy of different frequency bands can be calculated by the following steps:
  • the energy is equal to the sum of the squares of the Fourier transforms, ie the energy is calculated according to equation (3):
  • the energy level of the corresponding frequency band can be obtained, and the energy ratio of each frequency band is obtained by comparing the energy level of the corresponding frequency band with the total energy.
  • the frequency domain feature may further include a frequency characteristic of the signal, a frequency ratio of the different frequency bands, a power spectrum of the specific frequency band, and a ratio thereof, and typically includes a 95% edge frequency (Spectral Edge Frequency: SEF). , 50% Median Frequency (MF), Total Power (TP), Peak Power Frequency (PPF), etc.
  • SEF Standard Edge Frequency
  • MF Median Frequency
  • TP Total Power
  • PPF Peak Power Frequency
  • the nonlinear domain feature may be information entropy.
  • the calculation method of information entropy is to calculate the probability Pi of each waveform amplitude in the EEG waveform signal of length n in the whole segment of the signal, as shown in formula (4).
  • the nonlinear domain feature may further include spectral entropy, complexity, sample entropy, and the like.
  • the electroencephalogram-based anesthesia depth monitoring method may further include determining an anesthesia depth value based on the signal characteristics and the preset anesthesia depth prediction model (step S104).
  • the anesthesia depth prediction model is trained by the anesthesia depth database.
  • the anesthesia depth database contains a plurality of anesthesia full-encephalic EEG signals and anesthesia status corresponding to each segment of the EEG signal.
  • the anesthesia depth database may include different EEG signals, signal characteristics of the EEG signals, and correspondence between each segment of the EEG signals and the anesthetic state (ie, the anesthesia depth database includes various segments of EEG signals). Correspondence between signal characteristics and anesthesia status).
  • the signal characteristic of the EEG signal in the anesthesia depth database may be at least one of the features involved in step S103. That is, the signal characteristics may be at least one of a time domain feature, a frequency domain feature, and a nonlinear domain feature. Therefore, the correspondence between the signal characteristics of different EEG signals and the anesthetic state may be a correspondence between at least one of the time domain characteristics, the frequency domain characteristics, and the nonlinear domain characteristics of different EEG signals and the anesthesia state.
  • the anesthesia state in the anesthesia depth database may be that the anesthesiologist indicates the current anesthesia state based on the electroencephalogram waveform and the clinical anesthesia state corresponding to each segment of the EEG signal.
  • the anesthesia status corresponding to each segment of the EEG signal may include one or more of awake, sedation, general anesthesia, deep anesthesia, excessive anesthesia, and no EEG activity.
  • the data in the anesthesia depth database is more comprehensive.
  • the anesthesia depth database further includes one or more of population information, medication information, and type of surgery corresponding to the anesthesia full-course EEG signal.
  • the population coverage of the database refers to the coverage of the whole age group, which is divided into children (0 to 13 years old), adults (13 to 60 years old), and the elderly (over 60 years old).
  • the drug coverage is mainly Refers to anesthesia data collection for sedative, analgesic, and muscle relaxants used in mainstream venous and gas anesthesia, including but not limited to the following types: propofol, etomidate, midazolam, dextromethorphan, and Fluoroether, sevoflurane, desflurane, fentanyl, remifentanil, alfentanil, sufentanil, rocuronium bromide, vecuronium bromide, atracurium, etc. General surgery.
  • the signal characteristics of the different EEG signals and the corresponding anesthesia state can be stored in the anesthesia depth database.
  • the anesthesia depth prediction model was trained using signal characteristics and anesthesia status in the anesthesia depth database.
  • the predetermined number of decision trees for the anesthesia depth prediction model is at least 200, and the number of layers per decision tree does not exceed three.
  • the embodiment is not limited thereto, and for example, the number of decision trees may include at least 300 trees.
  • anesthesia depth value of 0 to 100 is obtained for each decision tree in the random forest.
  • anesthesia status is obtained for each decision tree in the random forest.
  • Anesthesia Range of anesthesia depth values wide awake 100-80 Calm, sleep state 79-60 General anes 59-40 Deep anesthesia 39-20 Over-anesthesia 19-1 No brain activity 0
  • the output anesthesia depth values of all decision trees in the anesthesia depth prediction model are averaged, and the final output anesthesia depth value can be obtained.
  • each random forest decision tree can be regarded as a weak classifier.
  • the anesthesia depth prediction model is a combination of a large number of weak classifiers.
  • the anesthesia depth prediction model has 300 decision trees. That is, the anesthesia depth prediction model is a combination of 300 weak classifiers.
  • the results of the 300 decision trees are averaged, which is equivalent to a plurality of weak classifiers forming a strong classifier. This makes the classification results more accurate, and it is more accurate to reflect the depth of anesthesia.
  • such a structure is very robust and scalable, and is well suited for a variety of drugs and anesthesia depths covered by different populations.
  • a single decision tree is trained to generate by:
  • each sample has M attributes. When the decision tree node needs to be split, m attributes are randomly selected from the M attributes, and then information gain is used from the m attributes to select an attribute as the classification attribute of the node. .
  • each node in the decision tree formation process is split according to the second step. There are two stopping conditions for the split. First, the split feature selected in this split is the parent node feature of the node, and the split ends. Second, the number of layers of the decision tree after the split reaches the specified number of layers, and the split stops.
  • the signal characteristics may include one or more of a time domain feature, a frequency domain feature, and a non-linear domain feature.
  • the anesthesia depth value can be obtained according to one or more of the time domain feature, the frequency domain feature, and the nonlinear domain feature and the preset anesthesia depth prediction model.
  • the time domain feature can include an explosion suppression ratio
  • the frequency domain feature can include an EEG related energy ratio
  • the nonlinear domain feature can include information entropy.
  • the anesthesia depth value can be obtained based on the burst suppression ratio, the EEG-related energy ratio, and the information entropy and the preset anesthesia depth prediction model.
  • Fig. 7 is a flow chart showing another method of monitoring anesthesia depth based on electroencephalogram according to the present embodiment.
  • the electroencephalogram-based anesthesia depth monitoring method may further include displaying an anesthesia depth value (step S105).
  • step S105 may directly display the anesthesia depth value, or may display an anesthesia state, or may display both the anesthesia depth value and the anesthesia state. Thereby, the depth of anesthesia or the state of anesthesia can be obtained intuitively.
  • the anesthesia depth prediction model is trained using the anesthesia depth database, and the anesthesia depth value is confirmed by the collected electroencephalogram signal and the trained anesthesia depth prediction model. In this case, over-fitting problems can be avoided in the case of large data volumes.

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Abstract

一种基于脑电的麻醉深度监测方法,包括:通过传感器获取脑电信号(S101);对脑电信号进行去噪处理(S102);提取去噪处理后的脑电信号的信号特征(S103);并且根据信号特征和预设的麻醉深度预测模型确定麻醉深度值(S104),麻醉深度预测模型由麻醉深度数据库训练得到,麻醉深度数据库包含多个麻醉全程脑电信号及与各麻醉全程脑电信号中各段脑电信号对应的麻醉状态。利用麻醉深度数据库对麻醉深度预测模型进行训练,通过麻醉深度预测模型确认麻醉深度值,能够在大数据量情况下,避免过拟合问题。

Description

基于脑电的麻醉深度监测方法和装置 技术领域
本发明涉及诱发电生理信号处理领域,尤其涉及一种基于脑电的麻醉深度监测方法和装置。
背景技术
在外科手术中,适当的麻醉深度是手术进行的必要条件。不同的临床需求对麻醉的深浅控制提出了不同的需求。例如,对于刺激大的手术,需要较深的麻醉程度来确保患者不会因为手术刺激而出现术中知晓等麻醉事故;而对于刺激小的手术,则需要有效地控制***量使得患者处于合适的麻醉深度,在手术结束后能够快速苏醒,同时节省***物的使用量。
在现有技术中,临床中对于麻醉深度的可控、可视化的需求主要通过麻醉深度监测仪器满足。
麻醉深度监测仪器现有技术中,使用特征加权求和的方法来获得麻醉深度。例如,使用β比率(Beta Ratio)、双谱指数(Bispectral Index,BIS)、爆发抑制比(Burst Suppression,BSR)和抑制指数(QUAZI)四个特征进行加权求和得到麻醉深度。但是这种加权求和方法得到的麻醉深度不够准确。具体而言,针对不同年龄段的患者在不同药物类型下反馈的麻醉深度值与真实麻醉深度水平有差异。
另外,麻醉深度监测仪器还存在一种计算麻醉深度的方法。其通过C4.5决策树获得当前麻醉状态(分为清醒、浅麻、中麻、深麻和过深麻醉),然后根据不同的麻醉状态再进行加权求和方法获得麻醉深度。C4.5决策树逻辑关系清晰,是决策树算法中单树结构的经典。但是,在麻醉深度领域,不同的病人类型、不同的药物类型与对应的麻醉深度需要大量的数据来建立对应关系。在大数据量下使用C4.5决策树很容易出现过拟合(Overfiting)的问题,即使进行剪枝等操作亦不能很好地解决该问题。所以使用C4.5决策树来判别麻醉状态的方法不能适 应当前复杂的临床麻醉需求。
发明内容
本发明是鉴于上述情况而作出的,其目的在于提供一种能够在大数据量情况下,避免过拟合问题的基于脑电的麻醉深度监测方法和装置。
为此,本发明的一方面提供一种基于脑电的麻醉深度监测方法,其包括:通过传感器获取脑电信号;对所述脑电信号进行去噪处理;提取去噪处理后的所述脑电信号的信号特征;并且根据所述信号特征和预设的麻醉深度预测模型确定麻醉深度值。
在本发明中,麻醉深度预测模型由麻醉深度数据库训练得到,所述麻醉深度数据库包含多个麻醉全程脑电信号及与各麻醉全程脑电信号中各段脑电信号对应的麻醉状态。
在本发明中,利用麻醉深度数据库对麻醉深度预测模型进行训练,通过采集的脑电信号和训练的麻醉深度预测模型确认麻醉深度值。在这种情况下,能够在大数据量情况下,避免过拟合问题。
在本发明的一方面所涉及的基于脑电的麻醉深度监测方法中,还包括显示所述麻醉深度值。由此,可以直观的获得麻醉深度值。
在本发明的一方面所涉及的基于脑电的麻醉深度监测方法中,所获取的脑电信号是模拟脑电信号,在对所述脑电信号进行去噪之前,对所述模拟脑电信号进行前置放大处理和模数转换处理,获得数字脑电信号。由此,便于脑电信号的存储。
在本发明的一方面所涉及的基于脑电的麻醉深度监测方法中,所述去噪处理包括对生理干扰信号和非生理干扰信号进行处理。由此,去除干扰信号,能够更加准确的获得麻醉深度值。
在本发明的一方面所涉及的基于脑电的麻醉深度监测方法中,所述信号特征包括时域特征、频域特征和非线性域特征。由此,能够根据时域特征、频域特征和非线性域特征和预设的麻醉深度预测模型得到麻醉深度值。
在本发明的一方面所涉及的基于脑电的麻醉深度监测方法中,所述时域特征包括爆发抑制比,所述频域特征包括脑电相关能量比,所 述非线性域特征包括信息熵。由此,能够根据爆发抑制比、脑电相关能量比以及信息熵和预设的麻醉深度预测模型得到麻醉深度值。
在本发明的一方面所涉及的基于脑电的麻醉深度监测方法中,所述各段脑电信号对应的麻醉状态包括清醒、镇静、一般麻醉、深度麻醉、过深麻醉、无脑电活动中的一个或多个。由此,麻醉深度数据库的数据更加全面。
在本发明的一方面所涉及的基于脑电的麻醉深度监测方法中,所述麻醉深度数据库还包括与麻醉全程脑电信号对应的人群信息、药物信息和手术类型中的一个或多个。由此,能够使得麻醉深度预测模型中脑电信号与麻醉深度的对应关系覆盖类型更广。
在本发明的一方面所涉及的基于脑电的麻醉深度监测方法中,所述预设的麻醉深度预测模型的决策树数量为至少200棵,并且每棵决策树的层数不超过3层。由此,能够避免在数据量大的情况下出现过拟合问题。
本发明的另一方面提供一种基于脑电的麻醉深度监测装置,其包括:采集模块,其通过传感器获取脑电信号;去噪模块,其对所述脑电信号进行去噪处理;提取模块,其提取去噪处理后的所述脑电信号的信号特征;以及计算模块,其根据所述信号特征和预设的麻醉深度预测模型,确定麻醉深度值。
在本发明中,麻醉深度预测模型由麻醉深度数据库得到,所述麻醉深度数据库包含多个麻醉全程脑电信号及与各麻醉全程脑电信号中各段脑电信号对应的麻醉状态。
在本发明中,计算模块通过采集的脑电信号和训练的麻醉深度预测模型确认麻醉深度值,麻醉深度预测模型是利用麻醉深度数据库训练得到的。在这种情况下,能够在大数据量情况下,避免过拟合问题。
在本发明的另一方面所涉及的基于脑电的麻醉深度监测装置中,还包括显示模块,其用于显示所述麻醉深度值。由此,可以通过显示模块直观的获得麻醉深度值。
在本发明的另一方面所涉及的基于脑电的麻醉深度监测装置中,在所述采集模块中,所获取的脑电信号是模拟脑电信号,并且在对所述脑电信号进行去噪之前,对所述模拟脑电信号进行前置放大处理和 模数转换处理,获得数字脑电信号。由此,能够通过数字脑电信号和预设的麻醉深度预测模型得到麻醉深度值。
在本发明的另一方面所涉及的基于脑电的麻醉深度监测装置中,在所述去噪模块中,所述去噪处理包括对生理干扰信号和非生理干扰信号进行处理。由此,去除干扰信号,能够更加准确的获得麻醉深度值。
在本发明的另一方面所涉及的基于脑电的麻醉深度监测装置中,所述信号特征包括时域特征、频域特征和非线性域特征。由此,能够根据时域特征、频域特征和非线性域特征和预设的麻醉深度预测模型得到麻醉深度值。
在本发明的另一方面所涉及的基于脑电的麻醉深度监测装置中,所述时域特征包括爆发抑制比,所述频域特征包括脑电相关能量比,所述非线性域特征包括信息熵。由此,能够根据爆发抑制比、脑电相关能量比以及信息熵和预设的麻醉深度预测模型得到麻醉深度值。
在本发明的一方面所涉及的基于脑电的麻醉深度监测装置中,所述各段脑电信号对应的麻醉状态包括清醒、镇静、一般麻醉、深度麻醉、过深麻醉、无脑电活动中的一个或多个。由此,麻醉深度数据库的数据更加全面。
在本发明的另一方面所涉及的基于脑电的麻醉深度监测装置中,所述麻醉深度数据库还包括与麻醉全程脑电信号对应的人群信息、药物信息和手术类型中的一个或多个。由此,能够使得麻醉深度预测模型中脑电信号与麻醉深度的对应关系覆盖类型更广。
在本发明的另一方面所涉及的基于脑电的麻醉深度监测装置中,所述预设的麻醉深度预测模型的决策树数量为至少200棵,并且每棵决策树的层数不超过3层。由此,能够避免在数据量大的情况下出现过拟合问题。
本发明的另一方面提供一种基于脑电的麻醉深度监测装置,其包括:传感器,其用于采集脑电信号;存储器,其用于存储采集到的所述脑电信号;处理器,其用于执行下述步骤:提取所述脑电信号的信号特征;根据所述信号特征和预设的麻醉深度预测模型确定麻醉深度值。
在本发明中,麻醉深度预测模型由麻醉深度数据库得到,所述麻醉深度数据库包含多个麻醉全程脑电信号及与各麻醉全程脑电信号中各段脑电信号对应的麻醉状态。在这种情况下,能够在大数据量情况下,避免过拟合问题。
在本发明的另一方面所涉及的基于脑电的麻醉深度监测装置中,所述信号特征包括时域特征、频域特征和非线性域特征中的一种或多种。由此,能够根据时域特征、频域特征和非线性域特征和预设的麻醉深度预测模型得到麻醉深度值。
在本发明的另一方面所涉及的基于脑电的麻醉深度监测装置中,所述时域特征包括爆发抑制比,所述频域特征包括脑电相关能量比,所述非线性域特征包括信息熵。由此,能够根据爆发抑制比、脑电相关能量比以及信息熵和预设的麻醉深度预测模型得到麻醉深度值。
在本发明的另一方面所涉及的基于脑电的麻醉深度监测装置中,在执行所述的步骤前,所述处理器还对获取的所述脑电信号进行去噪处理。由此,去除干扰信号,能够更加准确的获得麻醉深度值。
在本发明的另一方面所涉及的基于脑电的麻醉深度监测装置中,所述麻醉深度数据库还包括与麻醉全程脑电信号对应的人群信息、药物信息和手术类型中的一个或多个。由此,能够使得麻醉深度预测模型中脑电信号与麻醉深度的对应关系覆盖类型更广。
在本发明的另一方面所涉及的基于脑电的麻醉深度监测装置中,所述预设的麻醉深度预测模型的决策树数量为至少200棵,并且每棵决策树的层数不超过3层。由此,能够避免在数据量大的情况下出现过拟合问题。
附图说明
图1是示出了本实施方式所涉及的一种基于脑电的麻醉深度监测装置结构示意图。
图2是示出了本实施方式所涉及的基于脑电的麻醉深度监测装置的采集模块结构示意图。
图3是示出了本实施方式所涉及的基于脑电的麻醉深度监测装置的去噪模块结构示意图。
图4是示出了本实施方式所涉及的另一种基于脑电的麻醉深度监测装置结构示意图。
图5是示出了本实施方式所涉及的另一种基于脑电的麻醉深度监测装置结构示意图。
图6是示出了本实施方式所涉及的一种基于脑电的麻醉深度监测方法流程示意图。
图7是示出了本实施方式所涉及的另一种基于脑电的麻醉深度监测方法流程示意图。
具体实施方式
以下,参考附图,详细地说明本发明的优选实施方式。在下面的说明中,对于相同的部件赋予相同的符号,省略重复的说明。另外,附图只是示意性的图,部件相互之间的尺寸的比例或者部件的形状等可以与实际的不同。
图1是示出了本实施方式所涉及的一种基于脑电的麻醉深度监测装置结构示意图。图2是示出了本实施方式所涉及的基于脑电的麻醉深度监测装置的采集模块结构示意图。图3是示出了本实施方式所涉及的基于脑电的麻醉深度监测装置的去噪模块结构示意图。图4是示出了本实施方式所涉及的基于脑电的麻醉深度监测装置的计算模块结构示意图。本实施方式公开一种基于脑电的麻醉深度监测装置1,其可以用于麻醉监护。
在本实施方式中,如图1所示,基于脑电的麻醉深度监测装置1可以包括采集模块10、去噪模块20、提取模块30和计算模块40。
在本实施方式中,采集模块10可以通过传感器获取脑电信号。例如,采集模块10可以通过电极片获取脑电信号。采集模块10还可以通过EEG采集设备等获取脑电信号。另外,通过传感器采集到的脑电信号可以是模拟脑电信号。采集到的脑电信号可以包括患者在麻醉全程中各段脑电信号。
在本实施方式中,采集模块10还可以包括放大单元110。放大单元110可以是前置放大器。放大单元110可以对模拟脑电信号进行前置放大处理。由此,能够将模拟脑电信号进行放大,提高模拟脑电信 号的功率和信号质量。
在本实施方式中,如图2所示,采集模块10还可以包括模数转换单元120。模数转换单元120可以是模数(A/D)转换器。模数转换单元120可以对模拟脑电信号进行模数转换处理。也即模数转换单元120可以将放大后的模拟脑电信号转换成数字脑电信号。具体而言,模数转换主要对模拟脑电信号进行采样,然后量化编码为数字脑电信号。由此,便于脑电信号的存储。
在本实施方式中,基于脑电的麻醉深度监测装置1还可以包括存储模块(未图示)。存储模块可以用来接收并存储采集模块10输出的脑电信号。存储模块可以是存储器,例如RAM,内存条等。
在本实施方式中,如图1所示,基于脑电的麻醉深度监测装置1还可以包括去噪模块20。去噪模块20可以是处理器例如中央处理器(CPU)、微型处理器(MPU)、专用集成电路(ASIC)等。但本实施方式不限于此,去噪模块20还可以是滤波器。
在本实施方式中,去噪模块20可以识别并处理脑电信号中的干扰信号。也即去噪模块20可以用于对脑电信号进行去噪处理。干扰信号可以包括生理干扰信号和非生理干扰信号。
在本实施方式中,去噪模块20对脑电信号进行去噪处理,可以是对数字脑电信号进行去噪处理。具体而言,采集模块10可以将数字脑电信号传输至去噪模块20中,去噪模块20可以从接口处接收数字脑电信号,然后识别并处理数字脑电信号中的生理和非生理因素导致的干扰信号。
在本实施方式中,如图3所示,去噪模块20可以包括识别并去除生理干扰信号单元210。去除生理干扰信号单元210可以去除生理干扰信号。生理干扰信号可以包括脑电信号中的眼动干扰信号和肌电干扰信号。其中,眼动干扰信号主要是眨眼和眼球转动引起的低频波形,肌电干扰信号主要是肌肉放电导致的高频波形。由此,去除干扰信号,能够更加准确的获得麻醉深度值。
在本实施方式中,如图3所示,去噪模块20还可以包括去除非生理干扰信号单元220。去除非生理干扰信号单元220可以去除非生理干扰信号。非生理干扰信号可以包括脑电信号中的信号幅度异常、信号 斜率异常以及电刀干扰。信号幅度以及斜率异常引起的干扰信号主要是外界按压触碰传感器导致的干扰信号。电刀干扰主要是在手术进行中高频电刀对采集***产生的冲击带来的干扰。
在本实施方式中,如图1所示,脑电信号处理装置1还可以包括提取模块30。提取模块30可以是处理器例如中央处理器(CPU)、微型处理器(MPU)、专用集成电路(ASIC)等。
在本实施方式中,提取模块30可以用于提取去噪处理后的脑电信号的信号特征。信号特征可以包括时域特征、频域特征和非线性域特征中的至少一种。时域特征可以包括爆发抑制比。爆发抑制比可以通过抑制信号的长度除以整段信号的长度获得。频域特征可以包括脑电相关能量比。能量比是脑电信号中不同频段能量占总能量的占比。非线性域特征可以包括信息熵。信息熵的计算方法是统计一段长度的脑电波形信号中每个波形幅度在整段信号中出现的概率。
在本实施方式中,如图1所示,脑电信号处理装置1还可以包括计算模块40。计算模块40可以是处理器例如中央处理器(CPU)、微型处理器(MPU)、专用集成电路(ASIC)等。
在本实施方式中,计算模块40可以用于根据信号特征和预设的麻醉深度预测模型,确定麻醉深度值。麻醉深度预测模型由麻醉深度数据库训练得到,麻醉深度数据库包含多个麻醉全程脑电信号及与各麻醉全程脑电信号中各段脑电信号对应的麻醉状态。
在本实施方式中,麻醉深度数据库可以包括不同的脑电信号、脑电信号的信号特征以及各段脑电信号与麻醉状态之间的对应关系(也即麻醉深度数据库包含各段脑电信号的信号特征与麻醉状态之间的对应关系)。
在本实施方式中,麻醉深度数据库中脑电信号的信号特征可以是时域特征、频域特征和非线性域特征中的至少一个。故不同脑电信号的信号特征与麻醉状态之间的对应关系可以是不同脑电信号的时域特征、频域特征和非线性域特征中的至少一个特征与麻醉状态之间的对应关系。在本实施方式中,麻醉深度数据库中的麻醉状态可以是麻醉专家根据脑电波形和各段脑电信号对应的临床麻醉状态标注出当前的麻醉状态。各段脑电信号对应的麻醉状态包括清醒、镇静、一般麻醉、 深度麻醉、过深麻醉、无脑电活动中的一个或多个。
另外,在本实施方式中,麻醉深度数据库还包括与麻醉全程脑电信号对应的人群信息、药物信息和手术类型中的一个或多个。
在本实施方式中,计算模块40可以通过上述的麻醉深度数据库训练得到麻醉深度预测模型。具体而言,计算模块40可以通过麻醉深度数据库中的麻醉全程脑电信号的信号特征和各麻醉全程脑电信号中各段脑电信号对应的麻醉状态来训练麻醉深度预测模型。在一些示例中,计算模块40可以通过麻醉深度数据库中的不同脑电信号的时域特征、频域特征和非线性域特征中的至少一个特征与麻醉状态之间的对应关系来训练麻醉深度预测模型。由此,能够更加准确的得到麻醉深度预测模型中脑电信号与麻醉深度的对应关系。
在本实施方式中,预设的麻醉深度预测模型的决策树数量可以包括至少200棵,并且每棵决策树的层数不超过3层。但本实施方式不限于此,例如决策树数量可以包括至少300棵。另外,随机森林中的每棵决策树,获得一个0~100的麻醉深度值。麻醉状态和麻醉值之间存在对应关系,如表1所示。
在这种情况下,由于提取模块30的信号特征可以包括时域特征、频域特征和非线性域特征中的一个或多个。由此,计算模块40能够根据时域特征、频域特征和非线性域特征中的一个或多个和预设的麻醉深度预测模型得到麻醉深度值。时域特征可以包括爆发抑制比,频域特征可以包括脑电相关能量比,非线性域特征可以包括信息熵。由此,计算模块40能够根据爆发抑制比、脑电相关能量比以及信息熵和预设的麻醉深度预测模型得到麻醉深度值。
图4是示出了本实施方式所涉及的另一种基于脑电的麻醉深度监测装置结构示意图。
在本实施方式中,如图4所示,脑电信号处理装置1还可以包括显示模块50。显示模块50可以是屏幕或者其它设备。例如,显示模块50可以是多个指示灯。不同指示灯代表不同麻醉状态。显示模块50还可以是显示屏,例如可以是液晶显示设备、可以是LED显示设备,也可以是LCD显示设备。
在本实施方式中,显示模块50用于显示麻醉深度值。由此,可以 通过显示模块直观的获得麻醉深度值。本实施方式不限于此,还可以显示麻醉状态,也可以将麻醉深度值和麻醉状态都显示出来。
在本实施方式中,通过采集的脑电信号和训练的麻醉深度预测模型确认麻醉深度值,麻醉深度预测模型是利用麻醉深度数据库训练得到的。在这种情况下,能够在大数据量情况下,避免过拟合问题。
下面是在一些示例中的脑电信号处理装置。图5是示出了本实施方式所涉及的另一种基于脑电的麻醉深度监测装置结构示意图。
在一些示例中,如图5所示,脑电信号处理装置1可以包括传感器11、存储器12、处理器13和显示模块14。
在本实施方式中,传感器11可以采集脑电信号。在一些示例中,传感器11可以是电极片。传感器11获取的脑电信号可以是模拟脑电信号。
在本实施方式中,脑电信号处理装置1还可以包括前置放大器(未图示)和模数(A/D)转换器(未图示)。由于传感器11采集到的模拟脑电信号信号质量可能较差,因而可以通过前置放大器对采集到的脑电信号进行前置放大。由此,能够将脑电信号进行放大,提高脑电信号的功率和信号质量。模数(A/D)转换器可以模拟脑电信号转换成数字脑电信号。由此,便于脑电信号的存储。
在本实施方式中,如图5所示,存储器12可以存储采集到的脑电信号。存储器可以是RAM、FIFO、内存条等。
在本实施方式中,如图5所示,处理器13可以提取脑电信号的信号特征。信号特征包括时域特征、频域特征和非线性域特征中的一种或多种。时域特征可以包括爆发抑制比。爆发抑制比可以通过抑制信号的长度除以整段信号的长度获得。频域特征可以包括脑电相关能量比。能量比是脑电信号中不同频段能量占总能量的占比。非线性域特征可以包括信息熵。信息熵的计算方法是统计一段长度的脑电波形信号中每个波形幅度在整段信号中出现的概率。
在本实施方式中,处理器13还可以根据信号特征和预设的麻醉深度预测模型确定麻醉深度值。麻醉深度预测模型由麻醉深度数据库训练得到,麻醉深度数据库包含多个麻醉全程脑电信号及与各麻醉全程脑电信号中各段脑电信号对应的麻醉状态。其中各段脑电信号对应的 麻醉状态包括清醒、镇静、一般麻醉、深度麻醉、过深麻醉、无脑电活动中的一个或多个。另外,麻醉深度数据库还包括与麻醉全程脑电信号对应的人群信息、药物信息和手术类型中的一个或多个。在一些示例中,预设的麻醉深度预测模型的决策树数量为至少200棵,并且每棵决策树的层数不超过3层。处理器13与上述的计算模块40类似。
在本实施方式中,处理器13还可以对获取的脑电信号进行去噪处理。处理器13的去噪处理可以通过算法对获取的脑电信号进行去噪。当然,除了处理器13,还可以通过硬件电路实现脑电信号的去噪。具体去噪处理可以参照上述的去噪模块20中的去噪处理。
在本实施方式中,如图5所示,脑电信号处理装置1还可以包括显示模块14。显示模块14与上述显示模块50类似。
以下,结合图6和图7详细地描述本实施方式所涉及的基于脑电的麻醉深度监测方法。
图6是示出了本实施方式所涉及的一种基于脑电的麻醉深度监测方法流程示意图。
在本实施方式中,如图6所示,基于脑电的麻醉深度监测方法包括通过传感器获取脑电信号(步骤S101)。
在步骤S101中,可以通过传感器获取脑电信号,还可以通过EEG设备采集患者的脑电信号。EEG设备可以是本领域技术人员所公知的各种采集脑电信号的设备。
在本实施方式中,通过传感器采集到的脑电信号可以是模拟脑电信号。模拟脑电信号通常可以转换成数字脑电信号。
在本实施方式中,对模拟脑电信号进行前置放大处理和模数转换处理,获得数字脑电信号。前置放大处理可以将模拟脑电信号进行放大,提高模拟脑电信号的功率和信号质量。模数转换处理可以将放大后的模拟脑电信号转换成数字脑电信号。由此,便于脑电信号的存储。
在本实施方式中,如图6所示,基于脑电的麻醉深度监测方法还可以包括对脑电信号进行去噪处理(步骤S102)。
在步骤S102中,去噪处理包括对生理干扰信号和非生理干扰信号进行识别和处理。对脑电信号进行去噪处理,可以提高脑电信号的质量。由此,去除干扰信号,能够更加准确的获得麻醉深度值。
在本实施方式中,生理干扰信号可以包括眼动干扰信号和肌电干扰信号。
在本实施方式中,眼动干扰信号通常是眨眼和眼球转动引起的低频波形。比如眼动干扰信号可以是脑电信号中10hz以下的频段。针对眼动干扰信号频段,可以通过分析脑电波形中的异常低频信号进行识别,识别到眼动干扰后对该段数据进行剔除。
在本实施方式中,肌电干扰信号通常是肌肉放电导致的高频突变。比如肌电干扰信号可以是脑电信号中50hz以上的频段。针对肌电干扰信号频段,可以通过分析脑电波形中的高频异常突变进行识别,识别到肌电高频干扰后对该段数据进行剔除。
在本实施方式中,非生理干扰信号可以包括信号幅度异常、信号斜率异常以及电刀干扰。
在本实施方式中,信号幅度异常、信号斜率异常通常是由外界按压触碰传感器所导致的。信号幅度通常是指在一段时间内的信号波动最大值。信号斜率通常是相邻两个采样点之间的差值的最大值。针对信号幅度异常、信号斜率异常,可以在识别到幅度和斜率异常干扰后对该段数据进行剔除。
在本实施方式中,电刀干扰通常是由在手术进行中高频电刀对采集***产生的冲击而产生的。可以通过高频段的异常波动来识别电刀干扰。高频段一般是指500hz以上的频段。识别到电刀干扰后对该段数据进行剔除。
在步骤S102中,对脑电信号进行去噪处理,可以是对数字脑电信号进行去噪处理,获取有效的数字脑电信号。
在本实施方式中,如图6所示,基于脑电的麻醉深度监测方法还可以包括提取去噪处理后的脑电信号的信号特征(步骤S103)。
在本实施方式中,信号特征可以包括时域特征、频域特征和非线性域特征中的至少一种。
在本实施方式中,时域特征可以计算爆发抑制比(BSR)。爆发抑制是在大脑处于深度麻醉时出现的波形。其特点是小幅度的抑制波形与大幅度的爆发波形交替出现。当脑电信号波动幅度5uv以内,且持续时间超过0.5秒,可以被认为是一段抑制信号。
在本实施方式中,爆发抑制比可以通过抑制信号的长度除以整段信号的长度获得。爆发抑制比是评估爆发抑制波形的量化指标。爆发抑制比的计算公式如下:
Figure PCTCN2017120350-appb-000001
但本实施方式不限于此,例如,时域特征还可以包括信号幅度、最大最小值、信号相邻点之间的差值(波形的斜率)、信号相邻点差的极值和波形的过零情况(即单位时间内波形穿越零点的次数)等。
在本实施方式中,频域特征可以为脑电相关能量比。能量比是脑电信号中不同频段能量占总能量的占比。
在本实施方式中,不同频段可以包括频率范围为0.5~3hz的δ波、频率范围为4~7hz的θ波、频率范围可以为8~13hz的α波、以及频率范围可以为14~30hz的β波。总能量频率范围可以为0.5~47hz。
在本实施方式中,不同频段能量可以通过以下步骤计算:
首先,对一段脑电信号按照公式(2)进行傅里叶变换:
Figure PCTCN2017120350-appb-000002
然后,根据帕塞瓦尔(Parseval)定理,能量等于傅里叶转换式平方之和,即按照公式(3)计算能量:
Figure PCTCN2017120350-appb-000003
通过上述两步分别计算δ、θ、α、β中各频段的能量,就可以得到对应频段的能量水平,对应频段能量水平与总能量相比,得到各个频段的能量比。
但本实施方式不限于此,例如,频域特征还可以包括信号的频率特征、不同频段的频率比值、特定频段的功率谱及其比值,典型的包括95%边缘频率(Spectral Edge Frequency:SEF),50%中位频率(Median Frequency:MF),总功率(Total Power:TP),峰值功率点(Peak Power Frequency:PPF)等。
在本实施方式中,非线性域特征可以信息熵。信息熵的计算方法是统计一段长度为n的脑电波形信号中每个波形幅度在整段信号中出现的概率Pi,如公式(4)所示。
Figure PCTCN2017120350-appb-000004
但本实施方式不限于此,例如,非线性域特征还可以包括谱熵、复杂度、样本熵等。
在本实施方式中,如图6所示,基于脑电的麻醉深度监测方法还可以包括根据信号特征和预设的麻醉深度预测模型,确定麻醉深度值(步骤S104)。麻醉深度预测模型由麻醉深度数据库训练得到,麻醉深度数据库包含多个麻醉全程脑电信号及与各麻醉全程脑电信号中各段脑电信号对应的麻醉状态。
在本实施方式中,麻醉深度数据库可以包括不同的脑电信号、脑电信号的信号特征以及各段脑电信号与麻醉状态之间的对应关系(也即麻醉深度数据库包含各段脑电信号的信号特征与麻醉状态之间的对应关系)。
在本实施方式中,麻醉深度数据库中的脑电信号的信号特征可以是步骤S103中所涉及的特征中的至少一个。也即信号特征可以是时域特征、频域特征和非线性域特征中的至少一个。故不同脑电信号的信号特征与麻醉状态之间的对应关系可以是不同脑电信号的时域特征、频域特征和非线性域特征中的至少一个特征与麻醉状态之间的对应关系。
在本实施方式中,麻醉深度数据库中的麻醉状态可以是麻醉专家根据脑电波形和各段脑电信号对应的临床麻醉状态标注出当前的麻醉状态。例如,在麻醉全程脑电信号中,各段脑电信号对应的麻醉状态可以包括清醒、镇静、一般麻醉、深度麻醉、过深麻醉、以及无脑电活动中的一个或多个。由此,麻醉深度数据库的数据更加全面。
在本实施方式中,麻醉深度数据库还包括与麻醉全程脑电信号对应的人群信息、药物信息和手术类型中的一个或多个。由此,可以解决麻醉深度病人、药物/手术类型覆盖的问题。
在本实施方式中,数据库的人群覆盖是指对于全年龄段的覆盖,分为小儿(0~13岁]、成年人(13~60岁],老年人(60岁以上)。药物覆 盖主要是指对主流的静脉和气体麻醉用到的镇静、镇痛和肌松药物的麻醉数据采集,包括但不限于以下类型:丙泊酚、依托咪酯、咪达***、右美托嘧啶、异氟醚、七氟醚、地氟醚、芬太尼、瑞芬太尼、阿芬太尼、舒芬太尼、罗库溴铵、维库溴铵、阿曲库铵等。手术类型覆盖指全科手术。
在本实施方式中,可以将不同脑电信号的信号特征与对应的麻醉状态储存在上述麻醉深度数据库中。利用麻醉深度数据库中的信号特征和麻醉状态训练麻醉深度预测模型。
在本实施方式中,预设的麻醉深度预测模型的决策树数量为至少200棵,并且每棵决策树的层数不超过3层。但本实施方式不限于此,例如决策树数量可以包括至少300棵。由此,能够避免在数据量大的情况下出现过拟合问题和模型扩展性不足的问题。
另外,在本实施方式中,随机森林中的每棵决策树,获得一个0~100的麻醉深度值。麻醉状态和麻醉值之间存在对应关系,如表1所示。
表1麻醉深度值与麻醉深度状态对应表
麻醉状态 麻醉深度值范围
清醒 100-80
镇静、睡眠状态 79-60
一般麻醉 59-40
深度麻醉 39-20
过度麻醉 19-1
无脑电活动 0
在本实施方式中,将麻醉深度预测模型中所有决策树的输出麻醉深度值进行平均,能够得到最终输出的麻醉深度值。
在本实施方式中,每一棵随机森林决策树都可以看成一个弱分类器。麻醉深度预测模型是大量弱分类器的组合。例如,麻醉深度预测模型有300棵决策树。即麻醉深度预测模型是300个弱分类器的组合。对这300棵决策树的结果进行平均,相当于多个弱分类器组成了一个强分类器。这样使得分类的结果更加准确,反映到麻醉深度值上也就更加精准。而且这样的结构具有很好的鲁棒性和可扩展性,十分适合 于多种药物和不同人群覆盖的麻醉深度领域。
在本实施方式中,单棵决策树通过以下方法训练生成:
第一步对于N个样本,则有放回的随机选择N个样本(注意每次随机选择一个样本后,样本放回可供下次随机选择),选择好的N个样本用于训练决策树。样本可以是已经按照“特征-标注”的顺序存储好的麻醉深度数据库序列集合。特征可以是上述的信号特征中的至少一个。标注可以是相应特征下的上述各段脑电信号对应的麻醉状态中的一个。第二步每个样本有M个属性,在决策树节点需要***时,随机从这M个属性中选取m个属性,然后从m个属性中采用信息增益来选择一个属性作为该节点的分类属性。属性可以为麻醉深度数据库序列集合中信号的特征。第三步决策树形成过程中每个节点都按照第二步来进行***。***的停止条件有两个,第一,本次***中选出的***特征为该节点的父节点特征,***结束;第二,***后决策树的层数达到规定层数,***停止。
在本实施方式中,按照上述三步建立大量决策树,构成麻醉深度预测模型。
在本实施方式中,由于信号特征可以包括时域特征、频域特征和非线性域特征中的一个或多个。由此,能够根据时域特征、频域特征和非线性域特征中的一个或多个和预设的麻醉深度预测模型得到麻醉深度值。另外,时域特征可以包括爆发抑制比,频域特征可以包括脑电相关能量比,非线性域特征可以包括信息熵。由此,能够根据爆发抑制比、脑电相关能量比以及信息熵和预设的麻醉深度预测模型得到麻醉深度值。
图7是示出了本实施方式所涉及的另一种基于脑电的麻醉深度监测方法流程示意图。
在本实施方式中,如图7所示,基于脑电的麻醉深度监测方法还可以包括显示麻醉深度值(步骤S105)。
在本实施方式中,步骤S105可以直接显示麻醉深度值,也可以显示麻醉状态,也可以将麻醉深度值和麻醉状态都显示出来。由此,可以直观的获得麻醉深度值或麻醉状态。
在本实施方式中,利用麻醉深度数据库对麻醉深度预测模型进行 训练,通过采集的脑电信号和训练的麻醉深度预测模型确认麻醉深度值。在这种情况下,能够在大数据量情况下,避免过拟合问题。
虽然以上结合附图和实施例对本发明进行了具体说明,但是可以理解,上述说明不以任何形式限制本发明。本领域技术人员在不偏离本发明的实质精神和范围的情况下可以根据需要对本发明进行变形和变化,这些变形和变化均落入本发明的范围内。

Claims (25)

  1. 一种基于脑电的麻醉深度监测方法,其特征在于,
    包括:
    通过传感器获取脑电信号;
    对所述脑电信号进行去噪处理;
    提取去噪处理后的所述脑电信号的信号特征;并且
    根据所述信号特征和预设的麻醉深度预测模型确定麻醉深度值。
  2. 根据权利要求1所述的方法,其特征在于,
    所述麻醉深度预测模型由麻醉深度数据库得到,所述麻醉深度数据库包含多个麻醉全程脑电信号及与各麻醉全程脑电信号中各段脑电信号对应的麻醉状态。
  3. 根据权利要求1或2所述的方法,其特征在于,
    所获取的脑电信号是模拟脑电信号,
    在对所述脑电信号进行去噪之前,对所述模拟脑电信号进行前置放大处理和模数转换处理,获得数字脑电信号。
  4. 根据权利要求1或2所述的方法,其特征在于,
    所述去噪处理包括对生理干扰信号和非生理干扰信号进行处理。
  5. 根据权利要求1或2所述的方法,其特征在于,
    所述信号特征包括时域特征、频域特征和非线性域特征。
  6. 根据权利要求5所述的方法,其特征在于,
    所述时域特征包括爆发抑制比,所述频域特征包括脑电相关能量比,所述非线性域特征包括信息熵。
  7. 根据权利要求1或2所述的方法,其特征在于,
    所述各段脑电信号对应的麻醉状态包括清醒、镇静、一般麻醉、深度麻醉、过深麻醉、无脑电活动中的一个或多个。
  8. 根据权利要求1或2所述的方法,其特征在于,
    所述麻醉深度数据库还包括与麻醉全程脑电信号对应的人群信息、药物信息和手术类型中的一个或多个。
  9. 根据权利要求1或2所述的方法,其特征在于,
    所述预设的麻醉深度预测模型的决策树数量为至少200棵,并且每棵决策树的层数不超过3层。
  10. 一种基于脑电的麻醉深度监测装置,其特征在于,
    包括:
    采集模块,其通过传感器获取脑电信号;
    去噪模块,其对所述脑电信号进行去噪处理;
    提取模块,其提取去噪处理后的所述脑电信号的信号特征;以及
    计算模块,其根据所述信号特征和预设的麻醉深度预测模型,确定麻醉深度值。
  11. 根据权利要求10所述的装置,其特征在于,
    所述麻醉深度预测模型由麻醉深度数据库得到,所述麻醉深度数据库包含多个麻醉全程脑电信号及与各麻醉全程脑电信号中各段脑电信号对应的麻醉状态。
  12. 根据权利要求10或11所述的装置,其特征在于,
    在所述采集模块中,所获取的脑电信号是模拟脑电信号,并且
    在对所述脑电信号进行去噪之前,对所述模拟脑电信号进行前置放大处理和模数转换处理,获得数字脑电信号。
  13. 根据权利要求10或11所述的装置,其特征在于,
    在所述去噪模块中,所述去噪处理包括对生理干扰信号和非生理干扰信号进行处理。
  14. 根据权利要求10或11所述的装置,其特征在于,
    所述信号特征包括时域特征、频域特征和非线性域特征。
  15. 根据权利要求14所述的装置,其特征在于,
    所述时域特征包括爆发抑制比,所述频域特征包括脑电相关能量比,所述非线性域特征包括信息熵。
  16. 根据权利要求10或11所述的装置,其特征在于,
    所述各段脑电信号对应的麻醉状态包括清醒、镇静、一般麻醉、深度麻醉、过深麻醉、无脑电活动中的一个或多个。
  17. 根据权利要求10或11所述的装置,其特征在于,
    所述麻醉深度数据库还包括与麻醉全程脑电信号对应的人群信息、药物信息和手术类型中的一个或多个。
  18. 根据权利要求10或11所述的装置,其特征在于,
    所述预设的麻醉深度预测模型的决策树数量为至少200棵,并且每棵决策树的层数不超过3层。
  19. 一种基于脑电的麻醉深度监测装置,其特征在于,
    包括:
    传感器,采集脑电信号;
    存储器,存储采集到的所述脑电信号;
    处理器,执行下述步骤:
    提取所述脑电信号的信号特征;
    根据所述信号特征和预设的麻醉深度预测模型确定麻醉深度值。
  20. 根据权利要求19所述的装置,其特征在于,
    所述信号特征包括时域特征、频域特征和非线性域特征中的一种或多种。
  21. 根据权利要求20所述的方法,其特征在于,
    所述时域特征包括爆发抑制比,所述频域特征包括脑电相关能量 比,所述非线性域特征包括信息熵。
  22. 根据权利要求19所述的装置,其特征在于,在执行所述提取所述脑电信号的信号特征的步骤前,所述处理器还对获取的所述脑电信号进行去噪处理。
  23. 根据权利要求19所述的方法,其特征在于,
    所述麻醉深度预测模型由麻醉深度数据库得到,所述麻醉深度数据库包含多个麻醉全程脑电信号及与各麻醉全程脑电信号中各段脑电信号对应的麻醉状态。
  24. 根据权利要求19所述的方法,其特征在于,
    所述麻醉深度数据库还包括与麻醉全程脑电信号对应的人群信息、药物信息和手术类型中的一个或多个。
  25. 根据权利要求19所述的方法,其特征在于,
    所述预设的麻醉深度预测模型的决策树数量为至少200棵,并且每棵决策树的层数不超过3层。
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