Anesthesia depth monitoring systems, devices, electronic equipment, media and products

Abstract

The present invention discloses an anesthesia depth monitoring system, device, electronic device, medium and product, belonging to the field of medical equipment technology. The system includes a data acquisition device and a processor; the data acquisition device is used to obtain light intensity data for the blood flow of peripheral tissues of a target object; the processor is configured to execute the following anesthesia depth monitoring method, which includes: determining the blood flow index data corresponding to the light intensity data in the current first sliding window; determining the maximum difference, minimum difference and average value of the pulsation wave corresponding to the blood flow index data, the maximum difference being the difference between the maximum peak value and the trough value of the latest complete pulsation wave in the current first sliding window, and the minimum difference being the difference between the minimum peak value and the trough value of the latest complete pulsation wave; determining the anesthesia depth data of the target object based on the maximum difference, minimum difference and average value. The technical solution provided by the embodiment of the present invention can automatically determine the anesthesia depth data for the target object.

Description

Technical Field

[0001] The present invention relates to the technical field of medical equipment, and in particular to an anesthesia depth monitoring system, device, electronic equipment, medium and product. Background Art

[0002] Currently, clinicians typically determine a patient's current depth of anesthesia based on physiological indicators such as blood pressure, heart rate, respiratory rate, physical activity, and sweating. Because these indicators can be affected by multiple factors, their sensitivity and specificity for determining anesthesia depth vary. This results in the accuracy of existing anesthesia depth determination results being highly dependent on the clinician's experience.

[0003] Therefore, it is necessary to provide a real-time anesthesia depth monitoring system to provide doctors with real-time and accurate anesthesia depth data. Summary of the Invention

[0004] The present invention provides an anesthesia depth monitoring system, device, electronic equipment, medium and product to solve the problem that the prior art cannot automatically and accurately determine anesthesia depth data.

[0005] According to one aspect of the present invention, there is provided an anesthesia depth monitoring system, comprising a data acquisition device and a processor;

[0006] The data acquisition device is used to obtain light intensity data on blood flow in peripheral tissues of the target object;

[0007] The processor is configured to execute the following anesthesia depth monitoring method, the method comprising:

[0008] Determining blood flow index data corresponding to the light intensity data in a current first sliding window, wherein a window width of the current first sliding window is greater than a period of a pulse wave corresponding to the blood flow index data;

[0009] Determine the maximum difference, minimum difference, and average value of the pulsation wave corresponding to the blood flow index data, wherein the maximum difference is the difference between the maximum peak value and the trough value of the latest complete pulsation wave in the current first sliding window, the minimum difference is the difference between the minimum peak value and the trough value of the latest complete pulsation wave, and the average value is the average value of the latest complete pulsation wave;

[0010] Anesthesia depth data of the target object is determined according to the maximum difference, the minimum difference, and the average value.

[0011] According to another aspect of the present invention, there is provided an anesthesia depth monitoring device, comprising:

[0012] A blood flow index module, configured to determine blood flow index data corresponding to light intensity data in a current first sliding window, wherein the window width of the current first sliding window is greater than the period of the pulse wave corresponding to the blood flow index data;

[0013] an intermediate data module, configured to determine a maximum difference, a minimum difference, and an average value of the pulsation wave corresponding to the blood flow index data, wherein the maximum difference is the difference between the maximum peak value and the trough value of the latest complete pulsation wave in the current first sliding window, the minimum difference is the difference between the minimum peak value and the trough value of the latest complete pulsation wave, and the average value is the average value of the latest complete pulsation wave;

[0014] The anesthesia depth data determination module is used to determine the difference information between the maximum difference and the minimum difference, and determine the anesthesia depth data of the target object according to the difference information and the average value.

[0015] According to another aspect of the present invention, an electronic device is provided, comprising:

[0016] at least one processor; and

[0017] a memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program executable by the at least one processor, wherein the computer program is executed by the at least one processor to enable the at least one processor to perform the following anesthesia depth monitoring method:

[0019] Determining blood flow index data corresponding to the light intensity data in a current first sliding window, wherein a window width of the current first sliding window is greater than a period of a pulse wave corresponding to the blood flow index data;

[0020] Determine the maximum difference, minimum difference, and average value of the pulsation wave corresponding to the blood flow index data, wherein the maximum difference is the difference between the maximum peak value and the trough value of the latest complete pulsation wave in the current first sliding window, the minimum difference is the difference between the minimum peak value and the trough value of the latest complete pulsation wave, and the average value is the average value of the latest complete pulsation wave;

[0021] Difference information between the maximum difference and the minimum difference is determined, and anesthesia depth data of the target object is determined according to the difference information and the average value.

[0022] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, wherein the computer instructions are configured to cause a processor to execute the method for monitoring the depth of anesthesia according to any embodiment of the present invention, the method comprising:

[0023] Determining blood flow index data corresponding to the light intensity data in a current first sliding window, wherein a window width of the current first sliding window is greater than a period of a pulse wave corresponding to the blood flow index data;

[0024] Determine the maximum difference, minimum difference, and average value of the pulsation wave corresponding to the blood flow index data, wherein the maximum difference is the difference between the maximum peak value and the trough value of the latest complete pulsation wave in the current first sliding window, the minimum difference is the difference between the minimum peak value and the trough value of the latest complete pulsation wave, and the average value is the average value of the latest complete pulsation wave;

[0025] Difference information between the maximum difference and the minimum difference is determined, and anesthesia depth data of the target object is determined according to the difference information and the average value.

[0026] According to another aspect of the present invention, a computer program product is provided, the computer program product comprising a computer program, wherein when the computer program is executed by a processor, the computer program performs the following method, the method comprising:

[0027] Determining blood flow index data corresponding to the light intensity data in a current first sliding window, wherein a window width of the current first sliding window is greater than a period of a pulse wave corresponding to the blood flow index data;

[0028] Determine the maximum difference, minimum difference, and average value of the pulsation wave corresponding to the blood flow index data, wherein the maximum difference is the difference between the maximum peak value and the trough value of the latest complete pulsation wave in the current first sliding window, the minimum difference is the difference between the minimum peak value and the trough value of the latest complete pulsation wave, and the average value is the average value of the latest complete pulsation wave;

[0029] Difference information between the maximum difference and the minimum difference is determined, and anesthesia depth data of the target object is determined according to the difference information and the average value.

[0030] In the technical solution provided by the embodiment of the present invention, the maximum difference data carries the maximum peak information of the pulsation wave corresponding to the blood flow index data of the target object, and the minimum difference data carries the minimum peak information of the pulsation wave corresponding to the blood flow index data. The mean can be used to reflect the overall level of the pulsation wave corresponding to the blood flow index data. Due to the difference information between the maximum difference and the minimum difference, the relative changes between the peaks of the pulsation wave corresponding to the blood flow index data can be reflected, thereby reflecting the changes in the main characteristics of the pulsation wave corresponding to the blood flow index data. Therefore, the anesthesia depth data in the technical solution of the present invention can change with the relative changes between the peaks of the pulsation wave corresponding to the blood flow index data, so that it has better real-time performance, sensitivity and accuracy.

[0031] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0033] Figure 1 2 is a schematic structural diagram of an anesthesia depth monitoring system according to an embodiment of the present invention;

[0034] Figure 2 is a flow chart of a method for monitoring depth of anesthesia provided in an embodiment of the present invention;

[0035] Figure 3 2. A schematic diagram of a pulse wave corresponding to blood flow index data provided in accordance with an embodiment of the present invention;

[0036] Figure 4A is another flow chart of a method for monitoring depth of anesthesia provided according to an embodiment of the present invention;

[0037] Figure 4B is a schematic diagram of a predetermined high-frequency data frequency range and a predetermined low-frequency data frequency range provided according to an embodiment of the present invention;

[0038] Figure 5 2 is a schematic structural diagram of an anesthesia depth monitoring device provided in accordance with an embodiment of the present invention;

[0039] Figure 6 is a schematic structural diagram of an electronic device provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0040] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0041] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0042] Figure 1 This is a structural diagram of an anesthesia depth monitoring system provided by an embodiment of the present invention. The system includes a data acquisition device 100 and a processor 11; the data acquisition device 100 is used to obtain light intensity data of blood flow in peripheral tissues of a target object; the processor 11 is configured to execute an anesthesia depth monitoring method. Figure 2 As shown, the method includes:

[0043] S110. Determine blood flow index data corresponding to the light intensity data in the current first sliding window, wherein the window width of the current first sliding window is greater than the period of the pulsation wave corresponding to the blood flow index data.

[0044] S120. Determine the maximum difference, minimum difference, and average value of the pulsation wave corresponding to the blood flow index data, where the maximum difference is the difference between the maximum peak value and the trough value of the latest complete pulsation wave in the current first sliding window, the minimum difference is the difference between the minimum peak value and the trough value of the latest complete pulsation wave, and the average value is the average value of the latest complete pulsation wave.

[0045] S130 : Determine difference information between the maximum difference and the minimum difference, and determine anesthesia depth data of the target object based on the difference information and the average value.

[0046] After the target subject lies on the operating table, the optical probe of the data acquisition device is fixed to the surface of the peripheral tissue to be measured of the target subject. Continuous real-time acquisition of peripheral tissue blood flow data is started before the operation begins, and acquisition is stopped when the patient is fully awake after the operation. The light intensity data of tissue blood flow can be obtained using an existing data acquisition device, and this embodiment will not be described in detail here. The data acquisition device obtains the light intensity data of tissue blood flow based on near-infrared light, and specifically obtains the light intensity data of tissue blood flow based on diffuse correlation spectroscopy technology. Among them, diffuse correlation spectroscopy (DCS) is a non-invasive optical technology. The raw data collected is usually a signal of light intensity change over time, which can be called light intensity data.

[0047] The window width of the first sliding window is configured as a modifiable item, and the window width is greater than the pulse wave period and less than 10 times the pulse wave period. The end time of the first sliding window is the current time.

[0048] In one embodiment, in response to a window width modification operation, a window width configuration interface is displayed; window width configuration information is received based on the window width configuration interface; and parameter data of the first sliding window is updated based on the window width configuration information. This embodiment allows the user to flexibly determine the window width of the first sliding window based on actual conditions.

[0049] The Blood Flow Index (BFI) is an indicator used to assess blood flow in tissues or organs. It reflects the amount of blood flowing through a specific area per unit time. Existing techniques can be used to determine the BFI data corresponding to the light intensity data, and this embodiment will not be described in detail here.

[0050] It can be understood that if one cycle of the pulse wave includes M sampling points, then the waveform corresponding to the latest M sampling points in the current first sliding window is the latest complete pulse wave.

[0051] After the latest complete pulse wave is determined, Figure 3 As shown, the maximum peak (point c), minimum peak (point d), trough (point b), and period (the difference between the time corresponding to point a and the time corresponding to point b) of the latest complete pulsation wave (points f, e, and all points in between) are determined. Regarding the period, optionally, a first duration corresponding to the rising edge (point a to point c) and a second duration corresponding to the falling edge (point c to point b) of any complete pulsation wave in the first sliding window are determined. The sum of the first and second durations is used as the period of the latest complete pulsation wave.

[0052] The difference between the maximum peak and the trough is taken as the maximum difference, and the difference between the minimum peak and the trough is taken as the minimum difference; the sum of the amplitudes of the latest complete pulsation wave at all times is determined, and the ratio of the sum to the period is taken as the average value.

[0053] In one embodiment, the ratio between the difference information and the average value is used as the anesthesia depth data of the target object. This embodiment can simply, directly and quickly determine the anesthesia depth data of the target object.

[0054] In one embodiment, after the ratio between the difference information and the average value is determined, the ratio is substituted into a predetermined linear equation to obtain a corrected ratio; this corrected ratio is then used as the target subject's anesthesia depth. The coefficients and constants in the predetermined linear equation can be considered calibration data, used to correct for subtle differences between different anesthesia depth monitoring systems.

[0055] In one embodiment, after the blood flow index data is determined, the blood flow index data is preprocessed to obtain the preprocessed blood flow index data. The preprocessing includes adaptive filtering, smoothing, and physiological and non-physiological interference detection. The maximum difference, minimum difference, and average value of the pulse wave corresponding to the preprocessed blood flow index data are determined. This embodiment can improve the accuracy of the blood flow index data used in subsequent calculations.

[0056] Among them, adaptive filtering is a filtering method that can automatically adjust the filter coefficients according to the signal characteristics of the blood flow index data to minimize errors. Smoothing is a denoising method that aims to reduce noise and fluctuations while retaining the main trends or features. Specifically, through averaging or filtering methods, random noise and fluctuations in the signal are eliminated to make the data smoother. Physiological interference detection aims to identify and eliminate noise or artifacts in physiological signals to ensure that blood flow index data is accurate and reliable. Non-physiological interference detection aims to identify and eliminate noise caused by the external environment or equipment to ensure the accuracy of blood flow index data.

[0057] In one embodiment, after the anesthesia depth data is determined, the anesthesia depth data is aligned with the blood flow index data in the time dimension to obtain an alignment result, which is then displayed in real time on a visual interface. Temporally aligning the anesthesia depth data with the blood flow index data allows the update rate of the anesthesia depth data to be consistent with the update rate of the blood flow index data; this allows users to directly access the latest anesthesia depth data and its corresponding blood flow index data on the display interface.

[0058] In the technical solution provided by the embodiment of the present invention, the maximum difference data carries the maximum peak information of the pulsation wave corresponding to the blood flow index data of the target object, the minimum difference data carries the minimum peak information of the pulsation wave corresponding to the blood flow index data, and the mean is used to reflect the overall level of the pulsation wave corresponding to the blood flow index data. Due to the difference information between the maximum difference and the minimum difference, the relative changes between the peaks of the pulsation wave corresponding to the blood flow index data can be reflected, thereby reflecting the changes in the main characteristics of the pulsation wave corresponding to the blood flow index data. Therefore, the anesthesia depth data in the technical solution of the present invention can change with the relative changes between the peaks of the pulsation wave corresponding to the blood flow index data, so that it has better real-time performance, sensitivity and accuracy.

[0059] Figure 4A This is a flow chart of the method for determining anesthesia depth data provided by an embodiment of the present invention. This embodiment adds a step of determining the anesthesia state classification status identifier based on blood flow index data on the basis of the previous embodiment. Figure 4A As shown, the method includes:

[0060] S210: Determine the blood flow index data corresponding to the light intensity data in the current first sliding window, wherein the window width of the current first sliding window is greater than the period of the pulsation wave corresponding to the blood flow index data.

[0061] S220. Determine the maximum difference, minimum difference, and average value of the pulsation wave corresponding to the blood flow index data, where the maximum difference is the difference between the maximum peak value and the trough value of the latest complete pulsation wave in the current first sliding window, the minimum difference is the difference between the minimum peak value and the trough value of the latest complete pulsation wave, and the average value is the average value of the latest complete pulsation wave.

[0062] S230 : Determine difference information between the maximum difference and the minimum difference, and determine anesthesia depth data of the target object based on the difference information and the average value.

[0063] S240. Determine the power spectral density of the blood flow index data corresponding to the light intensity data under the current second sliding window. The window width of the current second sliding window is greater than 10 times the pulse wave period corresponding to the blood flow index data and less than half of the predetermined monitoring time. The window width of the current second sliding window is greater than the width of the current first sliding window.

[0064] The predetermined monitoring duration can be understood as the anesthesia monitoring duration of the target subject, which is longer than the operation duration. It can be configured as a modifiable item.

[0065] The window width of the second sliding window is greater than 10 times the pulse wave period corresponding to the blood flow index data, and is intended to obtain continuous change information of the blood flow index data over a period of time, rather than instantaneous change information.

[0066] S250: Extracting predetermined high-frequency data and predetermined low-frequency data from the power spectrum density.

[0067] A first frequency range (e.g., 0.0095-0.021 Hz) and a second frequency range (e.g., 0.021-0.052 Hz) are predetermined; predetermined high-frequency data are extracted from the power spectrum density according to the first frequency range; predetermined low-frequency data are extracted from the power spectrum density according to the second frequency range (see Figure 4B This embodiment can ensure that the predetermined high-frequency data and the predetermined low-frequency data only include data of required frequencies, thereby ensuring the accuracy of the predetermined high-frequency data and the predetermined low-frequency data.

[0068] S260: Determine a first area corresponding to predetermined high-frequency data and a second area corresponding to predetermined low-frequency data.

[0069] The area under the curve corresponding to the predetermined high-frequency data is determined, and the area is used as the first area. The area under the curve corresponding to the predetermined low-frequency data is determined, and the area is used as the second area.

[0070] S270. Determine the target object's anesthesia status classification identifier based on the proportion of the first area or the second area in the total area. The anesthesia status classification identifier is the first identifier or the second identifier. The first identifier is used to represent anesthesia, and the second identifier is used to represent wakefulness. The total area is equal to the sum of the first area and the second area.

[0071] Specifically, if the ratio between the first area and the total area is greater than the target threshold, the anesthetic state classification identifier of the target object is determined to be the first identifier; otherwise, the anesthetic state classification identifier of the target object is determined to be the second identifier.

[0072] The target threshold can be determined based on the first frequency range, the second frequency range and the window width of the second sliding window. Therefore, in this embodiment, the window width of the second sliding window is preferably an integer multiple of the pulse wave period corresponding to the blood flow index.

[0073] The technical solution provided by the embodiment of the present invention extracts predetermined high-frequency data and predetermined low-frequency data from the power spectral density of the blood flow index, and determines the first area and the second area corresponding to the two respectively, and determines the anesthesia status classification identifier of the target object according to the proportion of the first area or the second area in the total area, thereby determining whether the target object is in an anesthesia state or an awake state; this technical solution is used in combination with the anesthesia depth data in the aforementioned embodiment to ensure the accuracy of the anesthesia status identification of the target object. For example, after surgery, when both the anesthesia depth data and the anesthesia status classification identifier indicate that the user is awake, an attempt can be made to wake up the target object.

[0074] Figure 5 Schematic diagram of the structure of the anesthesia depth monitoring device provided by the embodiment of the present invention. Figure 5 As shown, the device includes:

[0075] A blood flow index module 31 is configured to determine blood flow index data corresponding to the light intensity data in a current first sliding window, wherein the window width of the current first sliding window is greater than the period of the pulse wave corresponding to the blood flow index data;

[0076] an intermediate data module 32 for determining a maximum difference, a minimum difference, and an average value of the pulsation wave corresponding to the blood flow index data, wherein the maximum difference is the difference between the maximum peak value and the trough value of the latest complete pulsation wave in the current first sliding window, the minimum difference is the difference between the minimum peak value and the trough value of the latest complete pulsation wave, and the average value is the average value of the latest complete pulsation wave;

[0077] The anesthesia depth data determination module 33 is configured to determine difference information between the maximum difference and the minimum difference, and determine anesthesia depth data of the target object based on the difference information and the average value.

[0078] In one embodiment, the anesthesia depth data determination module 33 is specifically configured to:

[0079] The ratio between the difference information and the average value is used as the anesthesia depth data of the target object.

[0080] In one embodiment, the anesthesia depth data determination module 33 is specifically configured to:

[0081] Determining a power spectral density of blood flow index data corresponding to the light intensity data in the current second sliding window, wherein a window width of the current second sliding window is greater than 10 times the pulse wave period corresponding to the blood flow index data and less than half of a predetermined monitoring duration, and the window width of the current second sliding window is greater than a width of the current first sliding window;

[0082] Based on a segmentation threshold, dividing the power spectrum density into predetermined high-frequency data and predetermined low-frequency data;

[0083] determining a first area corresponding to the predetermined high-frequency data and a second area corresponding to the predetermined low-frequency data;

[0084] Based on the proportion of the first area or the second area in the total area, the anesthesia status classification identifier of the target object is determined, and the anesthesia status classification identifier is the first identifier or the second identifier. The first identifier is used to characterize anesthesia, and the second identifier is used to characterize wakefulness. The total area is equal to the sum of the first area and the second area.

[0085] In one embodiment, the intermediate data module 32 is used to:

[0086] Preprocessing the blood flow index data to obtain preprocessed blood flow index data, wherein the preprocessing includes adaptive filtering, smoothing, physiological interference detection, and non-physiological interference detection;

[0087] The maximum difference, the minimum difference and the average value of the pulse wave corresponding to the pre-processed blood flow index data are determined.

[0088] In one embodiment, the window width of the first sliding window is configured as a modifiable item.

[0089] In one embodiment, the device further comprises a display module, wherein the display module is configured to:

[0090] In the time dimension, completing the alignment of the anesthesia depth data and the blood flow index data to obtain an alignment result;

[0091] The alignment results are displayed in real time in a visual interface.

[0092] In the technical solution provided by the embodiment of the present invention, the maximum difference data carries the maximum peak information of the pulsation wave corresponding to the blood flow index data of the target object, the minimum difference data carries the minimum peak information of the pulsation wave corresponding to the blood flow index data, and the mean is used to reflect the overall level of the pulsation wave corresponding to the blood flow index data. Due to the difference information between the maximum difference and the minimum difference, the relative changes between the peaks of the pulsation wave corresponding to the blood flow index data can be reflected, thereby reflecting the changes in the main characteristics of the pulsation wave corresponding to the blood flow index data. Therefore, the anesthesia depth data in the technical solution of the present invention can change with the relative changes between the peaks of the pulsation wave corresponding to the blood flow index data, so that it has better real-time performance, sensitivity and accuracy.

[0093] The anesthesia depth monitoring device provided in the embodiment of the present invention can execute the anesthesia depth monitoring method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0094] Figure 6 A schematic diagram of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0095] like Figure 6 As shown, electronic device 10 includes at least one processor 11 and memory, such as read-only memory (ROM) 12 and random access memory (RAM) 13, communicatively connected to at least one processor 11. The memory stores computer programs executable by the at least one processor. Processor 11 can perform various appropriate actions and processes based on the computer programs stored in ROM 12 or loaded from storage unit 18 into RAM 13. RAM 13 can also store various programs and data required for the operation of electronic device 10. Processor 11, ROM 12, and RAM 13 are interconnected via bus 14. An input / output (I / O) interface 15 is also connected to bus 14.

[0096] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0097] The processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any other suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the anesthesia depth monitoring method.

[0098] In some embodiments, the anesthesia depth monitoring method can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the anesthesia depth monitoring method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to execute the anesthesia depth monitoring method in any other appropriate manner (e.g., via firmware).

[0099] Various embodiments of the systems and techniques described above can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0100] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0101] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or apparatus. A computer-readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or apparatus, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media may include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0102] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device that has: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0103] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0104] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within a cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0105] An embodiment of the present invention further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the anesthesia depth monitoring method provided in any embodiment of the present application.

[0106] During implementation, the computer program product may be written in one or more programming languages ​​or a combination thereof to perform the operations of the present invention. The programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0107] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0108] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. An anesthesia depth monitoring system, characterized in that: including a data acquisition device and a processor; The data acquisition device is used to obtain light intensity data on blood flow in peripheral tissues of the target object; The processor is configured to execute the following anesthesia depth monitoring method, the method comprising: Determining blood flow index data corresponding to the light intensity data in a current first sliding window, wherein a window width of the current first sliding window is greater than a period of a pulse wave corresponding to the blood flow index data; Determine the maximum difference, minimum difference, and average value of the pulsation wave corresponding to the blood flow index data, wherein the maximum difference is the difference between the maximum peak value and the trough value of the latest complete pulsation wave in the current first sliding window, the minimum difference is the difference between the minimum peak value and the trough value of the latest complete pulsation wave, and the average value is the average value of the latest complete pulsation wave; Determining difference information between the maximum difference and the minimum difference, and determining anesthesia depth data of the target object based on the difference information and the average value, including: using a ratio between the difference information and the average value as the anesthesia depth data of the target object; Determining a power spectral density of blood flow index data corresponding to light intensity data in a current second sliding window, wherein a window width of the current second sliding window is greater than 10 times a pulse wave period corresponding to the blood flow index data and less than half a predetermined monitoring duration, and the window width of the current second sliding window is greater than a window width of the current first sliding window; extracting predetermined high-frequency data and predetermined low-frequency data from the power spectrum density; determining a first area corresponding to the predetermined high-frequency data and a second area corresponding to the predetermined low-frequency data; Based on the proportion of the first area or the second area in the total area, the anesthesia status classification identifier of the target object is determined, and the anesthesia status classification identifier is the first identifier or the second identifier. The first identifier is used to characterize anesthesia, and the second identifier is used to characterize wakefulness. The total area is equal to the sum of the first area and the second area.

2. The system according to claim 1, wherein: Determining the maximum difference, minimum difference, and average value of the pulsation waveform corresponding to the blood flow index data includes: Preprocessing the blood flow index data to obtain preprocessed blood flow index data, wherein the preprocessing includes adaptive filtering, smoothing, physiological interference detection, and non-physiological interference detection; The maximum difference, the minimum difference and the average value of the pulse wave corresponding to the pre-processed blood flow index data are determined.

3. The system according to claim 1, wherein: The window width of the first sliding window is configured as a modifiable item.

4. The system according to claim 1, wherein: Also includes: In the time dimension, completing alignment of the anesthesia depth data and the blood flow index data to obtain an alignment result; The alignment results are displayed in real time in a visual interface.

5. An anesthesia depth monitoring device, characterized in that: include: A blood flow index module, configured to determine blood flow index data corresponding to light intensity data in a current first sliding window, wherein the window width of the current first sliding window is greater than the period of the pulse wave corresponding to the blood flow index data; an intermediate data module, configured to determine a maximum difference, a minimum difference, and an average value of the pulsation wave corresponding to the blood flow index data, wherein the maximum difference is the difference between the maximum peak value and the trough value of the latest complete pulsation wave in the current first sliding window, the minimum difference is the difference between the minimum peak value and the trough value of the latest complete pulsation wave, and the average value is the average value of the latest complete pulsation wave; An anesthesia depth data determination module is configured to determine difference information between the maximum difference and the minimum difference, and determine anesthesia depth data of a target object based on the difference information and the average value, including: using a ratio between the difference information and the average value as the anesthesia depth data of the target object; determining a power spectral density of blood flow index data corresponding to light intensity data in a current second sliding window, wherein a window width of the current second sliding window is greater than 10 times the pulse wave period corresponding to the blood flow index data and less than half a predetermined monitoring duration, and the window width of the current second sliding window is greater than the window width of the current first sliding window; extracting predetermined high-frequency data and predetermined low-frequency data from the power spectral density; determining a first area corresponding to the predetermined high-frequency data and a second area corresponding to the predetermined low-frequency data; and determining an anesthesia state classification identifier of the target object based on a proportion of the first area or the second area in the total area, wherein the anesthesia state classification identifier is a first identifier or a second identifier, the first identifier being used to characterize anesthesia and the second identifier being used to characterize wakefulness, and the total area being equal to the sum of the first area and the second area.

6. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, wherein the computer program is executed by the at least one processor to enable the at least one processor to perform the following steps: Determining blood flow index data corresponding to the light intensity data in a current first sliding window, wherein a window width of the current first sliding window is greater than a period of a pulse wave corresponding to the blood flow index data; Determine the maximum difference, minimum difference, and average value of the pulsation wave corresponding to the blood flow index data, wherein the maximum difference is the difference between the maximum peak value and the trough value of the latest complete pulsation wave in the current first sliding window, the minimum difference is the difference between the minimum peak value and the trough value of the latest complete pulsation wave, and the average value is the average value of the latest complete pulsation wave; Determining difference information between the maximum difference and the minimum difference, and determining anesthesia depth data of the target object based on the difference information and the average value, including: using a ratio between the difference information and the average value as the anesthesia depth data of the target object; Determining a power spectral density of blood flow index data corresponding to light intensity data in a current second sliding window, wherein a window width of the current second sliding window is greater than 10 times a pulse wave period corresponding to the blood flow index data and less than half a predetermined monitoring duration, and the window width of the current second sliding window is greater than a window width of the current first sliding window; extracting predetermined high-frequency data and predetermined low-frequency data from the power spectrum density; determining a first area corresponding to the predetermined high-frequency data and a second area corresponding to the predetermined low-frequency data; Based on the proportion of the first area or the second area in the total area, the anesthesia status classification identifier of the target object is determined, and the anesthesia status classification identifier is the first identifier or the second identifier. The first identifier is used to characterize anesthesia, and the second identifier is used to characterize wakefulness. The total area is equal to the sum of the first area and the second area.

7. A computer-readable storage medium, characterized in that The computer readable storage medium stores computer instructions, and the computer instructions are used to cause the processor to perform the following steps: Determining blood flow index data corresponding to the light intensity data in a current first sliding window, wherein a window width of the current first sliding window is greater than a period of a pulse wave corresponding to the blood flow index data; Determine the maximum difference, minimum difference, and average value of the pulsation wave corresponding to the blood flow index data, wherein the maximum difference is the difference between the maximum peak value and the trough value of the latest complete pulsation wave in the current first sliding window, the minimum difference is the difference between the minimum peak value and the trough value of the latest complete pulsation wave, and the average value is the average value of the latest complete pulsation wave; Determining difference information between the maximum difference and the minimum difference, and determining anesthesia depth data of the target object based on the difference information and the average value, including: using a ratio between the difference information and the average value as the anesthesia depth data of the target object; Determining a power spectral density of blood flow index data corresponding to light intensity data in a current second sliding window, wherein a window width of the current second sliding window is greater than 10 times a pulse wave period corresponding to the blood flow index data and less than half a predetermined monitoring duration, and the window width of the current second sliding window is greater than a window width of the current first sliding window; extracting predetermined high-frequency data and predetermined low-frequency data from the power spectrum density; determining a first area corresponding to the predetermined high-frequency data and a second area corresponding to the predetermined low-frequency data; Based on the proportion of the first area or the second area in the total area, the anesthesia status classification identifier of the target object is determined, and the anesthesia status classification identifier is the first identifier or the second identifier. The first identifier is used to characterize anesthesia, and the second identifier is used to characterize wakefulness. The total area is equal to the sum of the first area and the second area.

8. A computer program product, characterized in that The computer program product comprises a computer program which, when executed by a processor, performs the following steps: Determining blood flow index data corresponding to the light intensity data in a current first sliding window, wherein a window width of the current first sliding window is greater than a period of a pulse wave corresponding to the blood flow index data; Determine the maximum difference, minimum difference, and average value of the pulsation wave corresponding to the blood flow index data, wherein the maximum difference is the difference between the maximum peak value and the trough value of the latest complete pulsation wave in the current first sliding window, the minimum difference is the difference between the minimum peak value and the trough value of the latest complete pulsation wave, and the average value is the average value of the latest complete pulsation wave; Determining difference information between the maximum difference and the minimum difference, and determining anesthesia depth data of the target object based on the difference information and the average value, including: using a ratio between the difference information and the average value as the anesthesia depth data of the target object; Determining a power spectral density of blood flow index data corresponding to light intensity data in a current second sliding window, wherein a window width of the current second sliding window is greater than 10 times a pulse wave period corresponding to the blood flow index data and less than half a predetermined monitoring duration, and the window width of the current second sliding window is greater than a window width of the current first sliding window; extracting predetermined high-frequency data and predetermined low-frequency data from the power spectrum density; determining a first area corresponding to the predetermined high-frequency data and a second area corresponding to the predetermined low-frequency data; Based on the proportion of the first area or the second area in the total area, the anesthesia status classification identifier of the target object is determined, and the anesthesia status classification identifier is the first identifier or the second identifier. The first identifier is used to characterize anesthesia, and the second identifier is used to characterize wakefulness. The total area is equal to the sum of the first area and the second area.

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