CN-121817814-B - Physiological signal real-time monitoring and intervention system for pain management

CN121817814BCN 121817814 BCN121817814 BCN 121817814BCN-121817814-B

Abstract

The invention relates to the technical field of medical informatics, in particular to a physiological signal real-time monitoring and intervention system for pain management, which comprises a determining module, a determining module and an intervention module, wherein the determining module is used for determining abnormal trends of a stimulation window updated in real time at each updating moment to obtain an abnormal trend sequence, the determining module is also used for determining similar fluctuation segments in the abnormal trend sequence, the determining module is also used for determining abnormal progress factors of physiological sensing signals at the current moment based on abnormal trend difference values and lengths of the similar fluctuation segments, the determining module is also used for determining triggering factors of abnormal pain states under the current moment and according to the abnormal progress factors of each physiological sensing signal at the previous moment, normalizing the triggering factors to obtain normalized triggering factors, and the intervention module is used for visualizing and intervening the pain degree of a target user based on the normalized triggering factors. The invention improves the accuracy and the sensitivity of physiological signal abnormality identification.

Inventors

ZHANG ZHI
ZHU XIA
LIU AN
CAO PENG
LIU YEHAO
XIA QIANHUI

Assignees

安徽医科大学

Dates

Publication Date: 20260508
Application Date: 20260310

Claims (10)

1. A pain management oriented physiological signal real-time monitoring and intervention system, comprising: the determining module is used for determining the abnormal tendency of the stimulation window updated in real time at each updating moment based on the real-time curve and the historical curve of each physiological sensing signal of the target user to obtain an abnormal tendency sequence; the determining module is further used for determining a similar fluctuation segment in the abnormal trend sequence according to the abnormal trend difference value between the latter item of data and the former item of data in the abnormal trend sequence; The determining module is further used for determining an abnormal progress factor of the physiological sensing signal at the current moment based on the abnormal tendency difference value and the length of each similar fluctuation segment; The determining module is further used for determining a trigger factor for evaluating the abnormal pain state at the current moment according to the abnormal progress factors of each physiological sensing signal at the current moment and the last moment, and carrying out normalization processing on the trigger factors to obtain normalized trigger factors; And the intervention module is used for visualizing and intervening the pain degree of the target user based on the normalized trigger factors.
2. The pain management oriented physiological signal real time monitoring and intervention system of claim 1, wherein the determination module is further configured to: Determining a waveform segment corresponding to a pair of minima at adjacent positions of a real-time curve of the physiological sensing signal as a current local stimulation behavior; Determining the stimulus effectiveness of the current local stimulus behavior based on a first difference value of any two points in a local sensor signal curve segment corresponding to the current local stimulus behavior, a second difference value of a history maximum value and a history average value of the history curve, and an average value of the duration of the current local stimulus behavior and the duration of the history local stimulus behavior corresponding to the physiological sensor signal; And determining the abnormal tendency of the real-time updated stimulation window at each updating moment according to the stimulation effectiveness of each local stimulation behavior in the real-time updated stimulation window, and obtaining an abnormal tendency sequence.
3. The pain management oriented physiological signal real time monitoring and intervention system of claim 2, wherein the determination module is further configured to: Determining the maximum difference value in the first difference values of any two points in the local sensing signal curve segment corresponding to the current local stimulation behavior; Calculating a first ratio between the maximum difference value and a second difference value between a historical maximum value and a historical average value of the historical curve, and a second ratio between the duration of the current local stimulation behavior and an average value of the duration of the historical local stimulation behavior corresponding to the physiological sensing signal; and determining the stimulus effectiveness of the current local stimulus behavior according to the first ratio and the second ratio.
4. The pain management oriented physiological signal real time monitoring and intervention system of claim 2, wherein the determination module is further configured to: Calculating a third difference value between the stimulation effectiveness of each local stimulation behavior and the mean value of the stimulation effectiveness of each local stimulation behavior in the real-time updated stimulation window; And determining the abnormal tendency according to the third difference value and the standard deviation of the stimulation effectiveness of each local stimulation behavior in the stimulation window updated in real time, and obtaining an abnormal tendency sequence.
5. The pain management oriented physiological signal real time monitoring and intervention system of claim 1, wherein the determination module is further configured to: calculating an abnormal tendency difference value between the latter item of data and the former item of data of the abnormal tendency sequence to obtain an abnormal tendency difference value sequence; and comparing the signs of adjacent abnormal trend differences in the abnormal trend difference sequences, and continuously marking the abnormal trends corresponding to the abnormal trend differences with the same signs in the abnormal trend difference sequences to obtain similar fluctuation segments in the abnormal trend sequences.
6. The pain management oriented physiological signal real time monitoring and intervention system of claim 5, wherein the determination module is further configured to: superposing the abnormal trend differences with the abnormal trend differences larger than a threshold value in the abnormal trend difference sequence to obtain a first superposition value, and superposing the abnormal trend differences with the abnormal trend differences smaller than the threshold value in the abnormal trend difference sequence to obtain a second superposition value; and determining an abnormal progress factor of the physiological sensing signal at the current moment based on the first added value, the second added value and the length of each similar fluctuation segment.
7. The pain management oriented physiological signal real time monitoring and intervention system of claim 6, wherein the determination module is further configured to: Calculating an absolute value of a third ratio between the first superimposed value and the second superimposed value, and a fourth difference between a maximum length of the lengths of the similar wave segments and an average length of the lengths of the similar wave segments; And determining an abnormal progress factor of the physiological sensing signal at the current moment according to the absolute value of the third ratio, the fourth difference value and the standard deviation of the lengths of the similar fluctuation segments.
8. The pain management oriented physiological signal real time monitoring and intervention system of claim 1, wherein the determination module is further configured to: Calculating a fourth ratio between a third superposition value of the abnormal progress factors of each physiological sensing signal at the current moment and a fourth superposition value of the abnormal progress factors of each physiological sensing signal at the previous moment; Calculating a fifth difference value between the abnormal progress factors of each physiological sensing signal at the current moment and the previous moment and a fifth ratio value between the average progress factors of the abnormal progress factors of each physiological sensing signal at the current moment; And determining a trigger factor for evaluating the abnormal pain state at the current moment according to the fourth ratio and the fifth ratio.
9. The pain management oriented physiological signal real time monitoring and intervention system of claim 1, wherein the intervention module is further configured to: And mapping the normalized trigger factors into corresponding colors through false colors, and performing corresponding intervention behaviors according to the colors, wherein different colors represent different pain degrees of a target user.
10. The physiological signal real-time monitoring and intervention system for pain management is characterized by comprising a processor and a memory, wherein the memory is used for storing a computer program which can be run on the processor, and the processor is used for executing the program stored on the memory to realize the following steps: Determining abnormal trends of the stimulation window updated in real time at each updating moment based on the real-time curve and the historical curve of each physiological sensing signal of the target user, and obtaining an abnormal trend sequence; Determining a similar fluctuation segment in the abnormal trend sequence according to the abnormal trend difference value between the latter item of data and the former item of data in the abnormal trend sequence; determining an abnormal progress factor of the physiological sensing signal at the current moment based on the abnormal tendency difference value and the length of each similar fluctuation segment; And determining a trigger factor for evaluating the abnormal pain state at the current moment according to the abnormal progress factors of each physiological sensing signal at the current moment and the last moment, normalizing the trigger factors to obtain normalized trigger factors, and visualizing the pain degree of the target user based on the normalized trigger factors.

Description

Physiological signal real-time monitoring and intervention system for pain management Technical Field The invention relates to the technical field of medical informatics, in particular to a physiological signal real-time monitoring and intervention system oriented to pain management. Background Pain is a common subjective discomfort symptom in clinic, not only affects the physiological comfort and quality of life of patients, but also can delay disease recovery, and scientific and effective pain management has become an important component of modern medicine. The core aim is to realize accurate pain control through reasonable intervention, minimize adverse reaction while maximally relieving pain, and balance curative effect and safety. Along with the fusion of biomedical engineering and artificial intelligence technology, pain management gradually changes to intelligent, and an intelligent monitoring system based on biological signal sensing becomes a research and development core. The system continuously collects multidimensional physiological signals such as heart rate, blood pressure, myoelectricity, electroencephalogram, and skin electricity activities through hardware such as wearable equipment, analyzes massive data in real time by means of an AI algorithm, builds a mapping relation between the physiological signals and pain intensity, realizes automatic evaluation of pain level, and can automatically trigger personalized intervention measures when the pain intensity exceeds the standard, thereby greatly improving management efficiency and effect. However, in some scenarios, the technical bottlenecks caused by individual patient variability in clinical applications are prominent. Different patients have obvious differences in physiological signal variation characteristics under pain states due to differences in age, constitution, basic diseases, pain tolerance threshold values and the like, and the signal response modes of the same patient can be changed under different disease stages and pain types. The current mainstream intelligent monitoring system mostly adopts a standardized model, and a general algorithm is trained based on large-scale crowd samples so as to fix logic recognition signal anomalies. When the model faces individuals with obvious feature differences, the problem of misjudgment or missed judgment is easy to occur, and pain related signal features of different patients are difficult to accurately capture, so that the physiological signals of the individuals with strong variation of the physiological signal variation features are not enough in the mode, and the abnormal recognition accuracy and sensitivity of the physiological signals are insufficient. Disclosure of Invention In order to solve the technical problems of insufficient accuracy and sensitivity of abnormal physiological signal identification of physiological signals of patients with strong variation characteristics of the physiological signals, the invention aims to provide a physiological signal real-time monitoring and intervention system oriented to pain management. In order to solve the technical problems, the adopted technical scheme is as follows: The embodiment of the invention provides a physiological signal real-time monitoring and intervention system for pain management, which comprises a determining module, a determining module and an intervention module, wherein the determining module is used for determining abnormal trends of a real-time updated stimulation window at each update time based on a real-time curve and a historical curve of each physiological sensing signal of a target user to obtain an abnormal trend sequence, the determining module is also used for determining similar fluctuation segments in the abnormal trend sequence according to abnormal trend difference values between later data and former data in the abnormal trend sequence, the determining module is also used for determining abnormal progress factors of the physiological sensing signals at the current time based on the abnormal trend difference values and lengths of the similar fluctuation segments, the determining module is also used for determining triggering factors of abnormal pain states under the current time according to the abnormal progress factors of each physiological sensing signal at the current time and the last time, normalizing the triggering factors to obtain normalized triggering factors, and the intervention module is used for visualizing and intervening the pain degree of the target user based on the normalized triggering factors. The determining module is further used for determining that a waveform segment corresponding to a pair of minima at adjacent positions of a real-time curve of the physiological sensing signal is a current local stimulation behavior, determining the stimulation effectiveness of the current local stimulation behavior based on a first difference value of any two points in the curve segment of the