CN-121971106-A - Method, system, device and storage medium for detecting nociceptive stimulus response based on electroencephalogram alpha power

CN121971106ACN 121971106 ACN121971106 ACN 121971106ACN-121971106-A

Abstract

The invention discloses a method, a system, a device and a storage medium for detecting a nociceptive stimulus response based on electroencephalogram alpha power. According to the invention, by innovatively adopting a secondary Whittaker smoothing strategy and setting differentiated smoothing coefficients, and through the synergistic effect of two-stage smoothing, alpha shedding detection with higher sensitivity and high specificity than the prior art is realized under the condition of ultra-short drug anesthesia, and a more reliable tool is provided for accurate anesthesia and nociception monitoring.

Inventors

YU WENLI
SUN YING
JIA LILI
REN YINGHUI
Meng Bochen
WANG JUNXI

Assignees

天津市第一中心医院

Dates

Publication Date: 20260505
Application Date: 20260126

Claims (10)

1. A method for detecting nociceptive stimulus responses based on electroencephalogram alpha power during general anesthesia, the method being performed by a computer and comprising the steps of: acquiring an electroencephalogram signal, namely acquiring a frontal area dual-channel EEG original signal of a patient receiving general anesthesia operation; Calculating an actual measurement alpha power sequence, namely calculating a peak alpha power time sequence of 7-17 Hz frequency bands based on an EEG original signal; acquiring drug concentration data, namely acquiring the concentration of the drug in an effector room of a patient undergoing general anesthesia operation; the method comprises the steps of calculating an expected alpha power sequence, namely inputting drug concentration data into a constructed prediction model, wherein the prediction model takes drug concentration as input and alpha power as output to obtain the expected alpha power sequence; Calculating a Residual sequence, namely subtracting the actual measured alpha power sequence from the expected alpha power sequence point by point to obtain an original Residual sequence residual_raw (t); Residual decomposition, namely applying a Whittaker smoothing filter to the residual_raw (t), setting a smoothing coefficient to epsilon 1=1, setting a smoothed sequence to Trend_1 (t), re-applying the Whittaker smoothing filter to the output sequence Trend_1 (t) after the first smoothing, setting the smoothing coefficient to epsilon 2=99, and setting the smoothed sequence to Trend_final (t); Alpha drop event determination, namely determining that an unexpected alpha drop event occurs in the time period when the value of Trend_final (t) is continuously lower than a threshold value; And outputting a classification result of whether the nociceptive stimulus response is generated.
2. The method according to claim 1, wherein the method for constructing the prediction model comprises the steps of calculating a peak alpha power time sequence of 7-17 Hz frequency bands based on EEG original signals, acquiring an effector room concentration time sequence of a drug used for general anesthesia surgery, and establishing a prediction model taking the drug concentration as an input and the alpha power as an output; preferably, a generalized linear model or a machine learning regression model is used to build a predictive model with drug concentration as input and alpha power as output.
3. The method of claim 1 or 2, wherein the drug used in the general anesthesia procedure comprises an ultrashort intravenous anesthetic and/or an ultrashort opioid analgesic.
4. The method of claim 3, wherein the ultra-short acting intravenous anesthetic is propofol.
5. The method of claim 3, wherein the ultrashort opioid analgesic is remifentanil.
6. The method of claim 1 wherein the step of computing the peak alpha power time series for the 7-17 Hz band based on the EEG raw signal comprises filtering the EEG signal, denoising the EEG signal, and computing the peak alpha power time series for the 7-17 Hz band using a short time Fourier transform.
7. The method of claim 6, wherein the filtering process employs a bandpass of 0.5-45 Hz.
8. A system for detecting nociceptive stimulus responses during general anesthesia based on electroencephalogram alpha power, the system comprising: the electroencephalogram signal acquisition module acquires frontal area double-channel EEG original signals of a patient receiving general anesthesia operation; The actually measured alpha power sequence calculation module is used for calculating a peak alpha power time sequence of 7-17 Hz frequency bands based on an EEG original signal; the medicine information input module inputs the concentration of medicine in the effector room of the patient who receives the general anesthesia operation; The expected alpha power sequence calculation module inputs the drug concentration data into a constructed prediction model, and the prediction model takes the drug concentration as input and alpha power as output to obtain an expected alpha power sequence; the Residual sequence calculation module is used for carrying out point-to-point subtraction on the actually measured alpha power sequence and the expected alpha power sequence to obtain an original Residual sequence residual_raw (t); The Residual decomposition module is used for applying a Whittaker smoothing filter to the residual_raw (t), setting the smoothing coefficient to epsilon 1=1, setting the smoothed sequence to Trend_1 (t), applying the Whittaker smoothing filter again to the output sequence Trend_1 (t) after the first smoothing, setting the smoothing coefficient to epsilon 2=99, and setting the smoothed sequence to Trend_final (t); An alpha drop event determination module that determines that an "unexpected alpha drop" event occurred during the time period when the value of trend_final (t) continues to be below a threshold; the result output module is used for outputting a classification result of whether the nociceptive stimulus response is generated or not; Preferably, the method for constructing the prediction model comprises the following steps of calculating a peak alpha power time sequence of 7-17 Hz frequency bands based on EEG original signals, acquiring an effector room concentration time sequence of a drug used for general anesthesia operation, and constructing a prediction model taking the drug concentration as input and alpha power as output by using a generalized linear model or a machine learning regression model; preferably, the drugs used in the general anesthesia surgery comprise ultra-short acting intravenous anesthetics and/or ultra-short acting opioid analgesics; Preferably, the ultra-short acting intravenous anesthetic is propofol; Preferably, the ultrashort opioid analgesic is remifentanil; preferably, the specific steps of calculating the peak alpha power time sequence of the 7-17 Hz frequency band based on the EEG original signal comprise the steps of filtering and denoising the EEG signal, and calculating the peak alpha power time sequence of the 7-17 Hz frequency band by adopting short-time Fourier transform; The filtering treatment adopts a bandpass of 0.5-45 Hz; Preferably, the system further comprises an alarm module.
9. An apparatus or electronic device for detecting a nociceptive stimulus response based on electroencephalogram alpha power during general anesthesia, the apparatus or electronic device comprising a memory and a processor; The memory is used for storing program instructions; the processor being operative to invoke program instructions which, when executed, implement the method of any of claims 1-7.
10. A computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of any of claims 1-7.

Description

Method, system, device and storage medium for detecting nociceptive stimulus response based on electroencephalogram alpha power Technical Field The invention belongs to the field of intelligent medical treatment, and relates to a method, a system, a device and a storage medium for detecting a nociceptive stimulus response based on electroencephalogram alpha power. Background During general anesthesia, monitoring the patient's response to surgical nociceptive stimuli (i.e., nociception) accurately in real time is one of the core challenges of anesthesia management, aimed at avoiding risks of intra-operative awareness due to too shallow anesthesia or circulatory inhibition due to too deep anesthesia. Electroencephalogram (EEG) is an important tool reflecting the state of the cerebral cortex, where the power change of the frontal EEG in the extended alpha band (7-17 Hz) is closely related to the depth of anesthesia and nociceptive stimulation. It has been suggested that, with stable concentrations of narcotic and analgesic drugs, unexpected decreases in EEG alpha power (alpha shedding) that cannot be explained by changes in drug concentration may reflect depolarization of the thalamus cortex by nociceptive stimuli such as body cavity surgery, and may serve as a potential biomarker for nociception. However, existing anesthesia depth monitoring techniques, such as brain electrical Bispectrum Index (BIS), while better monitoring sedation levels, are relatively insensitive to analgesia (nociception) monitoring. Therefore, it is of great clinical value to develop EEG indices that specifically reflect nociceptive stimuli. Disclosure of Invention In order to solve the problems in the prior art, the invention provides a novel and specially optimized EEG alpha shedding detection method for general anesthesia under ultra-short-acting drug induction. The method aims to achieve two core targets, namely, firstly, through first low-coefficient smoothing (epsilon 1=1), enough signal details are reserved, all visible alpha power reduction events can be captured, the false negative rate is obviously reduced, and secondly, second higher-coefficient smoothing (epsilon 2=99) is carried out on the basis of first smoothing, and transient extreme noise caused by rapid changes of ultra-short medicaments and remained in residual residues after the first smoothing is filtered, so that the false positive rate is obviously reduced. Finally, through the synergistic effect of two-stage smoothing, the alpha shedding detection with higher sensitivity and high specificity than the prior art is realized under the ultra-short drug anesthesia scene, and a more reliable tool is provided for accurate anesthesia and nociception monitoring. According to one aspect of the present invention, there is provided a method of detecting a nociceptive stimulus response during general anesthesia based on electroencephalogram alpha power, the method being performed by a computer, comprising the steps of: acquiring an electroencephalogram signal, namely acquiring a frontal area dual-channel EEG original signal of a patient receiving general anesthesia operation; Calculating an actual measurement alpha power sequence, namely calculating a peak alpha power time sequence of 7-17 Hz frequency bands based on an EEG original signal; acquiring drug concentration data, namely acquiring the concentration of the drug in an effector room of a patient undergoing general anesthesia operation; the method comprises the steps of calculating an expected alpha power sequence, namely inputting drug concentration data into a constructed prediction model, wherein the prediction model takes drug concentration as input and alpha power as output to obtain the expected alpha power sequence; Calculating a Residual sequence, namely subtracting the actual measured alpha power sequence from the expected alpha power sequence point by point to obtain an original Residual sequence residual_raw (t); Residual decomposition, namely applying a Whittaker smoothing filter to the residual_raw (t), setting a smoothing coefficient to epsilon 1=1, setting a smoothed sequence to Trend_1 (t), re-applying the Whittaker smoothing filter to the output sequence Trend_1 (t) after the first smoothing, setting the smoothing coefficient to epsilon 2=99, and setting the smoothed sequence to Trend_final (t); Alpha drop event determination, namely determining that an unexpected alpha drop event occurs in the time period when the value of Trend_final (t) is continuously lower than a threshold value; And outputting a classification result of whether the nociceptive stimulus response is generated. In the specific embodiment of the invention, the method for constructing the prediction model comprises the following steps of calculating a peak alpha power time sequence of 7-17 Hz frequency bands based on an EEG original signal, obtaining an effector room concentration time sequence of a drug used for general anesthesia o