Apparatus and method for evaluating cerebral autoregulation.

Abstract

An apparatus and method for evaluating cerebral autoregulation ability are disclosed. The apparatus for evaluating cerebral autoregulation ability according to one embodiment includes a data acquisition unit that acquires blood pressure data and oxygen saturation data of a patient during surgery, a correlation coefficient calculation unit that calculates a correlation coefficient between the acquired blood pressure data and the acquired oxygen saturation data, a filtering unit that filters the calculated correlation coefficient using a moving average filter having a predetermined time window, and a cerebral autoregulation ability evaluation unit that evaluates the cerebral autoregulation ability of the patient during the surgery based on the filtered correlation coefficient.

Description

Technical Field

[0001] The present invention relates to a technique for evaluating cerebral autoregulation in real time during surgery.

Background Art

[0002] Cerebral autoregulation is a physiological mechanism that maintains constant cerebral blood flow even when cerebral perfusion pressure changes. As long as cerebral autoregulation is maintained normally, the brain can protect itself from excessive hyperperfusion and hypoperfusion regardless of changes in cerebral perfusion pressure. However, impaired cerebral autoregulation can lead to negative outcomes in various neurological conditions such as traumatic brain injury, intracranial hemorrhage, and cerebral infarction.

[0003] Moyamoya disease is one of the cerebrovascular diseases associated with impaired cerebral autoregulation. Moyamoya disease refers to a disease in which stenosis or occlusion occurs at the distal end of the intracranial internal carotid artery, that is, at the origin of the anterior cerebral artery and middle cerebral artery, for no specific reason, and abnormal blood vessels called moyamoya vessels are observed in the vicinity. The onset of cerebral infarction in patients with moyamoya disease is the main form of neurological damage that is closely associated with impaired cerebral autoregulation.

[0004] Therefore, in order to predict and prevent postoperative complications in patients with moyamoya disease, it is necessary to develop a technique for evaluating cerebral autoregulation in real time during surgery.

Summary of the Invention

Problem to be Solved by the Invention

[0005] An object of the present invention is to provide an apparatus and a method for evaluating cerebral autoregulation in real time during surgery.

Means for Solving the Problem

[0006] An evaluation device for cerebral autoregulation according to one embodiment may include: a data acquisition unit that acquires blood pressure data and oxygen saturation data of a patient during surgery; a correlation coefficient calculation unit that calculates a correlation coefficient between the acquired blood pressure data and the acquired oxygen saturation data; a filtering unit that filters the calculated correlation coefficient using a moving average filter having a predetermined time window; and a cerebral autoregulation evaluation unit that evaluates the cerebral autoregulation of the patient during surgery based on the filtered correlation coefficient.

[0007] The aforementioned predetermined time window may be 25 minutes or more and 30 minutes or less.

[0008] The correlation coefficient calculation unit can calculate the correlation coefficient between blood pressure data and oxygen saturation data at a second time interval during the first time period.

[0009] The first time may be 5 minutes, and the second time may be 10 seconds.

[0010] The cerebral autoregulatory function evaluation unit can evaluate the patient's cerebral autoregulatory function during surgery as normal if the absolute value of the filtered correlation coefficient is in a first interval below a predetermined threshold, and can evaluate the patient's cerebral autoregulatory function during surgery as abnormal if the absolute value of the filtered correlation coefficient is in a second interval above the predetermined threshold.

[0011] The device for evaluating the cerebral autoregulatory function may further include an alarm unit that outputs an alarm based on the evaluation result of the cerebral autoregulatory function.

[0012] Other methods for evaluating cerebral autoregulation may include the steps of: acquiring blood pressure data and oxygen saturation data of a patient during surgery; calculating a correlation coefficient between the acquired blood pressure data and the acquired oxygen saturation data; filtering the calculated correlation coefficient using a moving average filter having a predetermined time window; and evaluating the cerebral autoregulation of the patient during surgery based on the filtered correlation coefficient.

[0013] The aforementioned predetermined time window may be 25 minutes or more and 30 minutes or less.

[0014] The step of calculating the correlation coefficient allows for the calculation of the correlation coefficient between blood pressure data and oxygen saturation data at a second time interval over a first time period.

[0015] The first time may be 5 minutes, and the second time may be 10 seconds.

[0016] The step of evaluating the cerebral autoregulatory function allows the patient's cerebral autoregulatory function during surgery to be evaluated as normal if the absolute value of the filtered correlation coefficient falls in a first interval below a predetermined threshold, and to be evaluated as abnormal if the absolute value of the filtered correlation coefficient falls in a second interval above the predetermined threshold.

[0017] The method for evaluating the cerebral autoregulatory function may further include the step of outputting an alarm based on the evaluation result of the cerebral autoregulatory function. [Effects of the Invention]

[0018] This technology allows for real-time assessment of a patient's cerebral autoregulatory capacity during surgery. This enables the prediction of potential postoperative side effects and helps medical staff make appropriate decisions during surgery, thereby preventing potential postoperative complications. [Brief explanation of the drawing]

[0019] [Figure 1] Figure 1 is a block diagram showing an evaluation device for cerebral autoregulation according to an exemplary embodiment. [Figure 2] Figure 2 is a block diagram illustrating and illustrating a computing environment, including computing devices suitable for use in an exemplary embodiment. [Figure 3]FIG. 3 is a flowchart illustrating a method for evaluating cerebral autoregulation according to an exemplary embodiment. [Figure 4] FIG. 4 is a diagram showing results of evaluating the level at which a cerebral infarction occurrence group can be discriminated while changing the time window of a moving average filter. DETAILED DESCRIPTION OF EMBODIMENTS

[0020] Hereinafter, an embodiment of the present invention will be described in detail with reference to the accompanying drawings. It should be noted that in assigning reference numerals to components in each drawing, the same reference numerals are used as much as possible for identical components even if they are shown in different drawings. Further, in describing the present invention, if it is determined that a detailed description of related known functions or configurations may obscure the gist of the present invention, the detailed description thereof will be omitted.

[0021] On the other hand, unless a specific order is clearly stated in the context for each step in each process, the steps may be performed in an order different from the explicitly stated order. That is, the steps may be performed in the same order as explicitly stated, may be performed substantially simultaneously, or may be performed in the reverse order.

[0022] Terms described below are defined in consideration of functions in the present invention, and may vary depending on the intention or custom of a user or an operator. Therefore, the definitions thereof should be made based on the content throughout the present specification.

[0023] Terms such as first and second are used to describe various components, but said components are not limited by said terms. Said terms are only used for the purpose of distinguishing one component from another. Singular expressions include plural expressions unless the context clearly dictates otherwise. Terms such as "comprising" or "having" indicate the presence of features, numbers, steps, acts, components, parts, or combinations thereof described in the specification, and do not preclude the presence or addition of one or more other features, numbers, steps, acts, components, parts, or combinations thereof in advance.

[0024] In addition, the division of components in the present specification is merely performed according to the main function undertaken by each component. That is, two or more components may be combined into one component, or one component may be divided into two or more components according to more detailed functions. In addition, each component may additionally perform part or all of the functions undertaken by other components in addition to its own main function, and part of the main function undertaken by each component may be exclusively performed by another component. Each component may be implemented by hardware, software, or a combination of hardware and software.

[0025] FIG. 1 is a block diagram showing an apparatus for evaluating cerebral autoregulation according to an exemplary embodiment.

[0026] The apparatus 100 for evaluating cerebral autoregulation according to an exemplary embodiment is an apparatus capable of evaluating a patient's cerebral autoregulation in real time during surgery based on the patient's blood pressure data and oxygen saturation data during surgery, and can be mounted on an electronic device or implemented as a separate device. Here, the electronic device may include cart-type devices and portable devices, and the portable device may include, but is not limited to, personal computers, notebook computers, tablets, and the like.

[0027] Referring to Figure 1, the cerebral autoregulatory function evaluation device 100 according to an exemplary embodiment may include a data acquisition unit 110, a correlation coefficient calculation unit 120, a filtering unit 130, and a cerebral autoregulatory function evaluation unit 140.

[0028] The data acquisition unit 110 can acquire blood pressure data and oxygen saturation data of patients during surgery. Here, the blood pressure data and oxygen saturation data may be time-series data.

[0029] For example, the data acquisition unit 110 includes a blood pressure measuring device and an oxygen saturation measuring device, and by using these devices to measure the blood pressure and oxygen saturation of the patient during surgery, blood pressure data and oxygen saturation data of the patient during surgery can be acquired. Here, the blood pressure measuring device may be a device that measures blood pressure using an invasive method, and the oxygen saturation measuring device may be a device that measures oxygen saturation using near-infrared spectroscopy, but these are merely embodiments and are not limited to these.

[0030] As another example, the data acquisition unit 110 can acquire blood pressure data and oxygen saturation data of a patient during surgery by receiving blood pressure data and oxygen saturation data of the patient during surgery from an external device that measures and / or stores blood pressure and / or oxygen saturation. In this case, the data acquisition unit 110 can use wired or wireless communication technology. Here, wireless communication technology includes, but is not limited to, Bluetooth® communication, BLE (Bluetooth Low Energy) communication, Near Field Communication (NFC), WLAN communication, Zigbee® communication, Infrared Data Association (IrDA) communication, WFD (Wi-Fi Direct) communication, UWB (ultra-wideband) communication, Ant+ communication, WIFI communication, RFID (Radio Frequency Identification) communication, 3G communication, 4G communication, and 5G communication.

[0031] The correlation coefficient calculation unit 120 can calculate the correlation coefficient between the acquired blood pressure data and oxygen saturation data. In this case, the correlation coefficient may be the Pearson correlation coefficient, but is not limited to it. The Pearson correlation coefficient is a numerical value that quantifies the linear correlation between two variables and takes a value between +1 and -1. Here, +1 can mean a perfect positive linear correlation, 0 means no linear correlation, and -1 means a perfect negative linear correlation.

[0032] For example, the correlation coefficient calculation unit 120 can calculate the correlation coefficient between blood pressure data and oxygen saturation data at a second time interval during the first time period. In this case, the first time period may be 5 minutes and the second time period may be 10 seconds, but these are merely examples and are not limited to these embodiments.

[0033] The correlation coefficient between blood pressure data and oxygen saturation data can be called the cerebral oxygen measurement index (COx).

[0034] The filtering unit 130 can filter the correlation coefficients calculated by the correlation calculation unit 120 using a moving average filter having a predetermined time window. In this case, the predetermined time window may be a value experimentally derived to enable real-time evaluation of the patient's cerebral autoregulation ability during surgery. For example, the predetermined time window may be 25 minutes or more, preferably 25 minutes or more and 30 minutes or less.

[0035] The cerebral automodulation evaluation unit 140 can evaluate the cerebral automodulation of a patient during surgery based on correlation coefficients filtered with a moving average filter having a predetermined time window.

[0036] For example, the cerebral autoregulation evaluation unit 140 can evaluate that the patient's cerebral autoregulation is functioning well during surgery if the absolute value of the correlation coefficient filtered by the moving average filter is small.

[0037] As another example, the cerebral autoregulation evaluation unit 140 divides the absolute value of the filtered correlation coefficient into a first interval below a predetermined threshold and a second interval above a predetermined threshold. If the absolute value of the filtered correlation coefficient is in the first interval, the cerebral autoregulation of the patient during surgery is evaluated as normal. If the absolute value of the filtered correlation coefficient is in the second interval, the cerebral autoregulation of the patient during surgery is evaluated as abnormal (damaged).

[0038] According to an exemplary embodiment, the cerebral autoregulatory function evaluation device 100 may further include a preprocessing unit 150 and / or an alarm unit 160.

[0039] The preprocessing unit 150 can preprocess the acquired blood pressure data and oxygen saturation data. For example, the preprocessing unit 150 can remove noise from the acquired blood pressure data and oxygen saturation data. In this case, the preprocessing unit 150 can utilize various publicly available noise reduction techniques.

[0040] The alarm unit 160 can output an alarm based on the evaluation results of the patient's cerebral automodulation during surgery. For example, if the evaluation results of cerebral automodulation are determined to be abnormal and the abnormal state persists for a predetermined time, the alarm unit 160 can generate and output an alarm. As another example, the alarm unit 160 can generate and output an alarm if the cumulative duration of the abnormal state in the evaluation results of cerebral automodulation exceeds a predetermined time.

[0041] Figure 2 is a block diagram illustrating a computing environment, including computing devices suitable for use in an exemplary embodiment. In the illustrated embodiment, each component may have different functions and capabilities other than those described below, and additional components other than those described below may be included.

[0042] The illustrated computing environment 200 may include a computing device 210. In one embodiment, the computing device 210 may be a device 100 for evaluating cerebral autoregulation.

[0043] The computing device 210 may include at least one processor 211, a computer-readable storage medium 212, and a communication bus 213. The processor 211 can cause the computing device 210 to operate according to the exemplary embodiments described above. For example, the processor 211 can execute one or more programs stored in the computer-readable storage medium 212. One or more programs may include one or more computer-executable instructions. The computer-executable instructions can be configured so that, when executed by the processor 211, the computing device 210 performs the operation according to the exemplary embodiments.

[0044] The computer-readable storage medium 212 can be configured to store computer-executable instructions or program code, program data, and / or other suitable forms of information. The program 214 stored in the computer-readable storage medium 212 may include a set of instructions that can be executed by the processor 211. In one embodiment, the computer-readable storage medium 212 may be memory (volatile memory such as random-access memory, non-volatile memory, or a suitable combination thereof), one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, other forms of storage media that can be accessed by the computing device 210 to store desired information, or a suitable combination thereof.

[0045] The communication bus 213 can interconnect various other components of the computing device 210.

[0046] The computing device 210 may also include one or more input / output interfaces 215 that provide interfaces for one or more input / output devices 220, and one or more network communication interfaces 216. The input / output interfaces 215 and network communication interfaces 216 may be connected to a communication bus 213. The input / output devices 220 may be connected to other components of the computing device 210 via the input / output interfaces 215. Exemplary input / output devices 220 may include input devices such as pointing devices (such as a mouse or trackpad), keyboards, touch input devices (such as a touchpad or touchscreen), voice or sound input devices, various types of sensor devices and / or imaging devices, and / or output devices such as display devices, printers, speakers and / or network cards. Exemplary input / output devices 220 may be included within the computing device 210 as one component of the computing device 210, or they may be connected to the computing device 210 as separate devices distinct from the computing device 210.

[0047] Figure 3 is a flowchart showing a method for evaluating cerebral autoregulation according to an exemplary embodiment. The method for evaluating cerebral autoregulation shown in Figure 3 can be performed using the cerebral autoregulation evaluation device 100 shown in Figure 1.

[0048] Referring to Figure 3, the device for evaluating cerebral autoregulation can acquire blood pressure and oxygen saturation data from patients during surgery (310).

[0049] For example, a device for evaluating cerebral autoregulation includes a blood pressure measuring device and an oxygen saturation measuring device. By using these devices to measure the blood pressure and oxygen saturation of a patient during surgery, blood pressure data and oxygen saturation data of the patient during surgery can be obtained.

[0050] As another example, a device for evaluating cerebral autoregulation can acquire blood pressure and oxygen saturation data of a patient during surgery by receiving blood pressure and oxygen saturation data from an external device that measures and / or stores blood pressure and / or oxygen saturation.

[0051] The device for evaluating cerebral autoregulation can calculate the correlation coefficient between acquired blood pressure data and oxygen saturation data (320). In this case, the correlation coefficient may be the Pearson correlation coefficient, but is not limited to it.

[0052] For example, a device for evaluating cerebral autoregulation can calculate the correlation coefficient between blood pressure data and oxygen saturation data at two time intervals. In this case, the first time interval may be 5 minutes and the second time interval may be 10 seconds, but these are merely examples and are not limited to these embodiments.

[0053] The device for evaluating cerebral autoregulation can filter correlation coefficients using a moving average filter with a predetermined time window (330). In this case, the predetermined time window can be experimentally derived so that the cerebral autoregulation of a patient during surgery can be evaluated in real time during surgery, and may be 25 to 30 minutes.

[0054] The device for evaluating cerebral automodulation can evaluate the cerebral automodulation of a patient during surgery based on correlation coefficients filtered by a moving average filter having a predetermined time window (340)

[0055] For example, a device for evaluating cerebral autoregulation can assess that the patient's cerebral autoregulation is functioning well during surgery if the absolute value of the correlation coefficient filtered by the moving average filter is small.

[0056] As another example, a device for evaluating cerebral autoregulation divides the absolute value of the filtered correlation coefficient into a first interval below a predetermined threshold and a second interval above a predetermined threshold. If the absolute value of the filtered correlation coefficient falls within the first interval, the patient's cerebral autoregulation during surgery is evaluated as normal. If the absolute value of the filtered correlation coefficient falls within the second interval, the patient's cerebral autoregulation during surgery is evaluated as abnormal.

[0057] According to an exemplary embodiment, the cerebral autoregulation evaluation device can preprocess the acquired blood pressure data and oxygen saturation data (315). For example, the cerebral autoregulation evaluation device can remove noise from the blood pressure data and oxygen saturation data acquired in step 310 using various noise reduction techniques.

[0058] According to an exemplary embodiment, the cerebral automodulation evaluation device can output an alarm based on the evaluation results of the cerebral automodulation of a patient during surgery (345). For example, the cerebral automodulation evaluation device can generate and output an alarm if the evaluation result of cerebral automodulation is determined to be abnormal and the abnormal state persists for a predetermined time. As another example, the cerebral automodulation evaluation device can generate and output an alarm if the cumulative duration of the abnormal state in the cerebral automodulation evaluation result exceeds a predetermined time.

[0059] [Example of experiment] To evaluate cerebral autoregulation, surgical signals were collected from patients with moyamoya disease, a representative cerebrovascular disease. Patients were divided into two groups based on whether or not they developed postoperative cerebral infarction, and an experiment was conducted to evaluate how cerebral autoregulation differed between the two groups. Out of a total of 68 surgical records, 10 were classified as the group that developed cerebral infarction.

[0060] The mean correlation coefficient between blood pressure and oxygen saturation collected throughout the entire surgical time was at the AUROC (area under the receiver operating characteristic curve) level of 0.78, significantly classifying the two groups. However, real-time evaluation of the correlation coefficient revealed no significant difference between the two groups. Therefore, we attempted to identify differences between the groups by applying moving average filters with different time windows.

[0061] We evaluated the level to which the group with cerebral infarction could be identified by varying the time window of the moving average filter, and obtained the results shown in Figure 4.

[0062] Referring to Figure 4, it can be seen that when the time window size of the moving average filter is 25 minutes, the AUROC predicting cerebral infarction is approximately 0.75, and when the time window size is 30 minutes, the AUROC predicting cerebral infarction is approximately 0.74. Furthermore, when the time window size is between 30 minutes and 300 minutes, the AUROC predicting cerebral infarction shows a value between approximately 0.72 and approximately 0.82, and when the time window size is 300 minutes or more, it is maintained at approximately 0.77.

[0063] In other words, it was confirmed that setting the time window size of the moving average filter to 25 minutes or more allows for the identification of the group that developed cerebral infarction after surgery at a relatively high level. This confirms that setting the time window size to 25 minutes or more, preferably 25 minutes to 30 minutes, allows for the real-time evaluation of the cerebral autoregulatory capacity during surgery at a relatively high level.

[0064] The present invention has been described above, focusing on its preferred embodiments. Those with ordinary skill in the art to which the present invention pertains will understand that the present invention can be embodied in modified forms that do not depart from its essential characteristics. Therefore, the scope of the present invention is not limited to the embodiments described above, but must be analyzed to include a variety of embodiments that fall within a scope equivalent to that described in the claims.

Claims

1. A data acquisition unit that acquires blood pressure data and oxygen saturation data of patients during surgery, A correlation coefficient calculation unit calculates the correlation coefficient between the acquired blood pressure data and the acquired oxygen saturation data, A filtering unit that filters the calculated correlation coefficient using a moving average filter having a predetermined time window, Includes a cerebral automodulation evaluation unit that evaluates the cerebral automodulation ability of the patient during surgery based on the filtered correlation coefficient, A device for evaluating the autoregulatory function of the cerebrum.

2. The aforementioned predetermined time window is 25 minutes or more and 30 minutes or less. The apparatus for evaluating the autoregulatory function of the cerebrum according to claim 1.

3. The correlation coefficient calculation unit, The correlation coefficient between blood pressure data and oxygen saturation data during the first hour is calculated at two time intervals. The apparatus for evaluating the autoregulatory function of the cerebrum according to claim 1.

4. The first time is 5 minutes, and the second time is 10 seconds. The device for evaluating the autoregulatory function of the cerebrum according to claim 3.

5. The aforementioned cerebral autoregulatory function evaluation unit is, If the absolute value of the filtered correlation coefficient falls within a first interval below a predetermined threshold, the patient's cerebral autoregulation during surgery is evaluated as normal. If the absolute value of the filtered correlation coefficient falls within a second interval equal to or greater than the predetermined threshold, the patient's cerebral autoregulation during surgery is evaluated as abnormal. The apparatus for evaluating the autoregulatory function of the cerebrum according to claim 1.

6. The system further includes an alarm unit that outputs an alarm based on the evaluation results of the cerebral autoregulatory function. The apparatus for evaluating the autoregulatory function of the cerebrum according to claim 1.

7. A method for evaluating cerebral autoregulatory function performed by a device for evaluating cerebral autoregulatory function, Steps include obtaining blood pressure data and oxygen saturation data from a patient during surgery, The steps include: calculating the correlation coefficient between the acquired blood pressure data and the acquired oxygen saturation data; The steps include filtering the calculated correlation coefficient using a moving average filter having a predetermined time window, The step of evaluating the patient's cerebral autoregulatory capacity during surgery based on the filtered correlation coefficients, Methods for evaluating the cerebral autoregulatory function.

8. The aforementioned predetermined time window is 25 minutes or more and 30 minutes or less. The method for evaluating the autoregulatory capacity of the cerebrum according to claim 7.

9. The step of calculating the correlation coefficient is: The correlation coefficient between blood pressure data and oxygen saturation data during the first hour is calculated at two time intervals. The method for evaluating the autoregulatory capacity of the cerebrum according to claim 7.

10. The first time is 5 minutes, and the second time is 10 seconds. The method for evaluating the autoregulatory capacity of the cerebrum according to claim 9.

11. The step of evaluating the cerebral autoregulatory capacity is, If the absolute value of the filtered correlation coefficient falls within a first interval below a predetermined threshold, the patient's cerebral autoregulation during surgery is evaluated as normal. If the absolute value of the filtered correlation coefficient falls within a second interval equal to or greater than the predetermined threshold, the patient's cerebral autoregulation during surgery is evaluated as abnormal. The method for evaluating the autoregulatory capacity of the cerebrum according to claim 7.

12. The further step includes outputting an alarm based on the evaluation results of the cerebral autoregulatory function, The method for evaluating the autoregulatory capacity of the cerebrum according to claim 7.

Images