Electronic device and method for evaluating automatic adjustment mode of cerebral blood flow

Abstract

The invention provides an electronic device and a method for evaluating an automatic adjustment mode of cerebral blood flow. The method comprises the following steps: grouping each data point in a first data set according to a plurality of blood pressure ranges to generate a plurality of data groups respectively corresponding to the plurality of blood pressure ranges; calculating a plurality of blood flow velocity average values of the plurality of data groups; performing a first linear regression operation on the plurality of blood flow velocity average values to generate a first regression line; calculating a first slope of the first regression line to obtain a first index; grouping a plurality of data sets including the first data set according to the first index to generate a plurality of auto-regulation mode groups; determining that the second data set corresponds to one of a plurality of auto-regulation mode groups; and outputting the automatic adjustment mode of the second data set.

Description

Technical Field

[0001] The present invention relates to an electronic device and method for evaluating the autoregulatory mode of cerebral blood flow. Background Art

[0002] When the body's blood pressure changes, the autoregulatory mechanism of cerebral blood flow can adjust the caliber of cerebral blood vessels to generate cerebral perfusion pressure (CPP), thereby maintaining cerebral blood flow (CBF) within a fixed range. Currently, many studies have proposed methods of using cerebral blood flow to predict the occurrence of cerebrovascular disease. However, the autoregulatory mode of cerebral blood flow may be different for different patients, and existing medical equipment cannot measure the autoregulatory mode of cerebral blood flow of patients. In this way, if the autoregulatory mode of cerebral blood flow of patients cannot be measured, the prediction results of cerebrovascular disease generated by the patient's cerebral blood flow may be distorted. Summary of the invention

[0003] The present invention provides an electronic device and method for evaluating the automatic regulation mode of cerebral blood flow, which can be used to determine the automatic regulation mode of cerebral blood flow of a patient.

[0004] An electronic device for evaluating the automatic regulation mode of cerebral blood flow of the present invention comprises a processor and a transceiver. The processor is coupled to the transceiver and is configured to perform: receiving a first data set through the transceiver, wherein the data points in the first data set include blood pressure and blood flow velocity corresponding to the blood pressure; grouping each data point in the first data set according to multiple blood pressure ranges to generate multiple data groups corresponding to multiple blood pressure ranges respectively; calculating multiple blood flow velocity averages corresponding to multiple data groups respectively; performing a first linear regression operation on the multiple blood flow velocity averages to generate a first regression line; calculating the first slope of the first regression line to obtain a first indicator; grouping multiple data sets including the first data set according to the first indicator to generate multiple automatic regulation mode groups; receiving a second data set through the transceiver, and determining whether the second data set corresponds to one of the multiple automatic regulation mode groups; and outputting the automatic regulation mode of the second data set, wherein the automatic regulation mode corresponds to one of the multiple automatic regulation mode groups.

[0005] In one embodiment of the present invention, the processor is further configured to perform: calculating cerebral vascular resistance based on blood pressure and blood flow rate; calculating multiple cerebral vascular resistance average values ​​corresponding to multiple data groups respectively; performing a second linear regression operation on the multiple cerebral vascular resistance average values ​​to generate a second regression line; calculating a second slope of the second regression line to obtain a second indicator; and grouping multiple data sets according to the first indicator and the second indicator to generate multiple automatic adjustment mode groups.

[0006] In one embodiment of the present invention, the processor is further configured to execute: calculating a plurality of blood flow velocity standard deviations corresponding to a plurality of blood flow velocity average values ​​respectively; generating an image according to the plurality of blood flow velocity average values ​​and the plurality of blood flow velocity standard deviations; and outputting the image.

[0007] In one embodiment of the present invention, the processor is further configured to execute: communicatively connecting to an ultrasound device via a transceiver, and receiving the blood flow velocity of the data point from the ultrasound device.

[0008] In one embodiment of the present invention, the processor is further configured to execute: communicatively connecting to a blood pressure monitor via a transceiver, and receiving a blood pressure data point from the blood pressure monitor.

[0009] In one embodiment of the present invention, each data point in the first data set corresponds to each heartbeat of the subject.

[0010] In one embodiment of the present invention, the processor is further configured to execute: performing an interpolation operation on the first data set to upsample the first data set.

[0011] In one embodiment of the present invention, the processor is further configured to execute: in response to determining that the second data set corresponds to one of a plurality of automatic adjustment mode groups, outputting an alarm message.

[0012] A method for evaluating the automatic regulation mode of cerebral blood flow of the present invention comprises: receiving a first data set, wherein the data points in the first data set include blood pressure and blood flow velocity corresponding to the blood pressure; grouping each data point in the first data set according to multiple blood pressure ranges to generate multiple data groups corresponding to the multiple blood pressure ranges respectively; calculating multiple blood flow velocity average values ​​corresponding to the multiple data groups respectively; performing a first linear regression operation on the multiple blood flow velocity average values ​​to generate a first regression line; calculating a first slope of the first regression line to obtain a first indicator; grouping multiple data sets including the first data set according to the first indicator to generate multiple automatic regulation mode groups; receiving a second data set, and determining whether the second data set corresponds to one of the multiple automatic regulation mode groups; and outputting the automatic regulation mode of the second data set, wherein the automatic regulation mode corresponds to one of the multiple automatic regulation mode groups.

[0013] Based on the above, the electronic device of the present invention can evaluate the patient's cerebral blood flow autoregulation mode based on data points including blood pressure and blood flow velocity. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A schematic diagram of an electronic device for evaluating the autoregulation mode of cerebral blood flow is shown according to an embodiment of the present invention.

[0015] Figure 2 A schematic diagram of a regression line is shown according to an embodiment of the present invention.

[0016] Figure 3 A schematic diagram of a regression line is shown according to an embodiment of the present invention.

[0017] Figure 4 A schematic diagram of a plurality of automatic adjustment mode groups is shown according to an embodiment of the present invention.

[0018] Figure 5 A flow chart of a method for evaluating the autoregulation mode of cerebral blood flow is shown according to an embodiment of the present invention.

[0019] Description of Reference Numerals

[0020] 10: Electronic devices;

[0021] 110: processor;

[0022] 120: storage media;

[0023] 130: transceiver;

[0024] 20: average blood flow rate;

[0025] 21: standard deviation of blood flow rate;

[0026] 30: average value of cerebral vascular resistance;

[0027] 31: standard deviation of cerebrovascular resistance;

[0028] 51, 52: regression line;

[0029] G1, G2: automatic adjustment mode group;

[0030] m1: first index;

[0031] m2: second index;

[0032] R1, R2, R3, R4, R5: data group;

[0033] S501, S502, S503, S504, S505, S506, S507, S508: steps. DETAILED DESCRIPTION

[0034] Reference will now be made in detail to exemplary embodiments of the present invention, examples of which are illustrated in the accompanying drawings. Whenever possible, the same reference numerals are used in the drawings and the description to refer to the same or like parts.

[0035] In order to make the content of the present invention more understandable, the following embodiments are given as examples that the present invention can be implemented. In addition, wherever possible, the elements / components / steps with the same reference numerals in the drawings and embodiments represent the same or similar parts.

[0036] Figure 1 A schematic diagram of an electronic device 10 for evaluating the autoregulation mode of cerebral blood flow is shown according to an embodiment of the present invention. The electronic device 10 may include a processor 110 , a storage medium 120 , and a transceiver 130 .

[0037] The processor 110 is, for example, a central processing unit (CPU), or other programmable general-purpose or special-purpose micro control unit (MCU), microprocessor, digital signal processor (DSP), programmable controller, application specific integrated circuit (ASIC), graphics processing unit (GPU), image signal processor (ISP), image processing unit (IPU), arithmetic logic unit (ALU), complex programmable logic device (CPLD), field programmable gate array (FPGA), or other similar elements or combinations thereof. The processor 110 may be coupled to the storage medium 120 and the transceiver 130, and access and execute multiple modules and various applications stored in the storage medium 120.

[0038] The storage medium 120 is, for example, any type of fixed or removable random access memory (RAM), read-only memory (ROM), flash memory, hard disk drive (HDD), solid state drive (SSD) or similar element or a combination of the above elements, and is used to store multiple modules or various applications that can be executed by the processor 110.

[0039] The transceiver 130 transmits and receives signals in a wireless or wired manner. The transceiver 130 may also perform operations such as low noise amplification, impedance matching, mixing, up or down frequency conversion, filtering, amplification, and the like.

[0040] The processor 110 may receive a historical data set including a plurality of historical data points through the transceiver 130, wherein each historical data point may include information such as blood pressure and blood flow velocity corresponding to the blood pressure. In one embodiment, the processor 110 may be communicatively connected to an ultrasonic instrument through the transceiver 130, and receive the blood flow velocity of the historical data point from the ultrasonic instrument. In one embodiment, the processor 110 may be communicatively connected to a sphygmomanometer through the transceiver 130, and receive the blood pressure of the historical data point from the sphygmomanometer. Each historical data point in the historical data set may correspond to each heartbeat of the subject. For example, when a heartbeat of the subject occurs, the ultrasonic instrument and the sphygmomanometer may respectively measure the blood flow velocity and blood pressure corresponding to the heartbeat, thereby generating a historical data point. The processor 110 may receive a historical data set including a plurality of historical data points corresponding to multiple heartbeats through the transceiver 130.

[0041] In one embodiment, the processor 110 may further perform an interpolation operation on the historical data set to upsample the historical data set, thereby increasing the number of historical data points in the historical data set.

[0042] The processor 110 may group each historical data point in the historical data set according to multiple blood pressure ranges to generate multiple data groups corresponding to the multiple blood pressure ranges. The processor 110 may further calculate multiple blood flow velocity averages corresponding to the multiple data groups. Figure 2A schematic diagram of a regression line 51 is shown according to an embodiment of the present invention. After obtaining a historical data set including a plurality of historical data points, the processor 110 may group each historical data point according to the blood pressure of each historical data point to generate a plurality of data groups including data groups R1, R2, R3, R4, and R5. Taking data group R1 as an example, the processor 110 may calculate the average blood flow rate of all historical data points in data group R1 to generate a blood flow rate average value 20. Based on similar steps, the processor 110 may calculate a corresponding blood flow rate average value for each of data groups R1, R2, R3, R4, and R5.

[0043] In one embodiment, the processor 110 may calculate the blood flow rate standard deviation corresponding to the blood flow rate average value according to the blood flow rate of each historical data point in the data group. The processor 110 may generate the following according to multiple blood flow rate average values ​​and multiple blood flow rate standard deviations respectively corresponding to the multiple blood flow rate average values: Figure 2 The image shown in FIG. 1 is output through the transceiver 130 for reference by the user of the electronic device 10. Taking the data group R1 as an example, the processor 110 can calculate the blood flow velocity standard deviation 21 corresponding to the blood flow velocity average value 20 according to the blood flow velocity of each historical data point in the data group R1. Based on similar steps, the processor 110 can calculate the corresponding blood flow velocity standard deviation for each of the data groups R1, R2, R3, R4 and R5. The processor 110 can generate the following image according to the blood flow velocity average value and blood flow velocity standard deviation of the data groups R1, R2, R3, R4 and R5: Figure 2 The image shown is output for user reference.

[0044] After obtaining a plurality of blood flow rate average values ​​corresponding to a plurality of data groups, the processor 110 may perform a linear regression operation on the plurality of blood flow rate average values ​​to generate a regression line. The processor 110 may calculate the slope of the regression line to obtain a first indicator corresponding to the historical data set. Figure 2 For example, the processor 110 may perform a linear regression operation on a plurality of blood flow velocity average values ​​(including the blood flow velocity average value 20 of the data group R1) to generate a regression line 51. The processor 110 may calculate the slope of the regression line 51 to obtain a first indicator corresponding to the historical data set.

[0045] On the other hand, the processor 110 may calculate the corresponding cerebrovascular resistance (CVR) for each historical data point in the historical data set according to formula (1), where CVR represents cerebrovascular resistance, ΔP represents the blood pressure of the historical data point, and F represents the blood flow rate of the historical data point.

[0046]

[0047] Next, the processor 110 may calculate a plurality of cerebral vascular resistance average values ​​respectively corresponding to the plurality of data groups. Figure 3 A schematic diagram of the regression line 52 is shown according to an embodiment of the present invention. After the processor 110 groups each historical data point according to the blood pressure of each historical data point to generate data groups R1, R2, R3, R4 and R5, taking data group R1 as an example, the processor 110 can calculate the average value of the cerebrovascular resistance of all historical data points in the data group R1 to generate the cerebrovascular resistance average value 30. Based on similar steps, the processor 110 can calculate the corresponding cerebrovascular resistance average value for each of the data groups R1, R2, R3, R4 and R5.

[0048] In one embodiment, the processor 110 may calculate the cerebrovascular resistance standard deviation corresponding to the cerebrovascular resistance average value according to the cerebrovascular resistance of each historical data point in the data group. The processor 110 may generate the following data according to the multiple cerebrovascular resistance average values ​​and the multiple cerebrovascular resistance standard deviations respectively corresponding to the multiple cerebrovascular resistance average values: Figure 3 The image shown in FIG. 1 is output through the transceiver 130 for reference by the user of the electronic device 10. Taking the data group R1 as an example, the processor 110 can calculate the cerebrovascular resistance standard deviation 31 corresponding to the cerebrovascular resistance average value 30 according to the cerebrovascular resistance of each historical data point in the data group R1. Based on similar steps, the processor 110 can calculate the corresponding cerebrovascular resistance standard deviation for each of the data groups R1, R2, R3, R4 and R5. The processor 110 can generate the following image based on the cerebrovascular resistance average value and cerebrovascular resistance standard deviation of the data groups R1, R2, R3, R4 and R5: Figure 3 The image shown is output for user reference.

[0049] After obtaining a plurality of cerebrovascular resistance average values ​​corresponding to a plurality of data groups, the processor 110 may perform a linear regression operation on the plurality of cerebrovascular resistance average values ​​to generate a regression line. The processor 110 may calculate the slope of the regression line to obtain a second indicator corresponding to the historical data set. Figure 3 For example, the processor 110 may perform a linear regression operation on a plurality of cerebrovascular resistance average values ​​(including the cerebrovascular resistance average value 30 of the data group R1) to generate a regression line 52. The processor 110 may calculate the slope of the regression line 52 to obtain a second indicator corresponding to the historical data set.

[0050] The processor 110 may calculate the corresponding first index and second index for each of the multiple historical data sets according to the above steps. Then, the processor 110 may group the multiple historical data sets according to the first index and second index of each historical data set to generate multiple automatic adjustment mode groups. The multiple automatic adjustment mode groups may be used as models for evaluating the automatic adjustment mode. Figure 4 According to an embodiment of the present invention, a schematic diagram of a plurality of automatic adjustment mode groups is shown, wherein Figure 4 Each point in represents a historical data set, m1 represents a first indicator, and m2 represents a second indicator. The processor 110 can group the multiple historical data sets into automatic adjustment mode groups G1 and automatic adjustment mode groups G2 based on the first indicator m1 and the second indicator m2 based on the grouping algorithm. The automatic adjustment mode groups G1 and the automatic adjustment mode groups G2 can be used as models for evaluating the automatic adjustment mode.

[0051] Specifically, the processor 110 may receive a current data set including a plurality of current data points through the transceiver 130, wherein each current data point may include information such as blood pressure and blood flow velocity corresponding to the blood pressure. The processor 110 may group each current data point in the current data set according to a plurality of blood pressure ranges to generate a plurality of data groups corresponding to the plurality of blood pressure ranges, respectively. The processor 110 may further calculate a plurality of blood flow velocity average values ​​and a plurality of cerebrovascular resistance average values ​​corresponding to the plurality of data groups, respectively. Then, the processor 110 may perform a linear regression operation on the plurality of blood flow velocity average values ​​to generate a regression line, thereby generating a first indicator corresponding to the current data set. The processor 110 may also perform a linear regression operation on the plurality of cerebrovascular resistance average values ​​to generate a regression line, thereby generating a second indicator corresponding to the current data set.

[0052] After obtaining the first index and the second index of the current data set, the processor 110 may determine which automatic adjustment mode group the current data set belongs to according to the first index and the second index of the current data set. The processor 110 may allocate the current data set to a specific automatic adjustment mode group (e.g., automatic adjustment mode group G1) among a plurality of automatic adjustment mode groups (e.g., automatic adjustment mode groups G1 and G2) based on a grouping algorithm according to the first index and the second index of the current data set. The processor 110 may output the grouping result of the current data set (i.e., the above-mentioned specific automatic adjustment mode group) through the transceiver 130 for user reference.

[0053] Figure 5 According to one embodiment of the present invention, a flow chart of a method for evaluating the autoregulation mode of cerebral blood flow is shown, wherein the method may be performed as follows: Figure 1The electronic device 10 shown is implemented. In step S501, a first data set is received, wherein the data points in the first data set include blood pressure and blood flow velocity corresponding to the blood pressure. In step S502, each data point in the first data set is grouped according to multiple blood pressure ranges to generate multiple data groups corresponding to the multiple blood pressure ranges respectively. In step S503, multiple blood flow velocity averages corresponding to the multiple data groups are calculated. In step S504, a first linear regression operation is performed on the multiple blood flow velocity averages to generate a first regression line. In step S505, a first slope of the first regression line is calculated to obtain a first indicator. In step S506, multiple data sets including the first data set are grouped according to the first indicator to generate multiple automatic adjustment mode groups. In step S507, a second data set is received by a transceiver, and it is determined that the second data set corresponds to one of the multiple automatic adjustment mode groups. In step S508, the automatic adjustment mode of the second data set is output, wherein the automatic adjustment mode corresponds to one of the multiple automatic adjustment mode groups.

[0054] In summary, the electronic device of the present invention can perform a linear regression operation on a data set containing data points of blood pressure and blood flow rate according to parameters such as blood flow rate, cerebral vascular resistance and blood pressure, so as to generate an index of the data set. The electronic device can group the data set according to the index to determine the automatic regulation mode of the data set. Accordingly, the present invention can evaluate the automatic regulation mode of cerebral blood flow of patients in a non-invasive manner.

[0055] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An electronic device for evaluating the autoregulation mode of cerebral blood flow, characterized in that include: Transceiver; as well as A processor is coupled to the transceiver and is configured to execute: receiving a first data set by the transceiver, wherein the data points in the first data set include blood pressure and a blood flow velocity corresponding to the blood pressure; Grouping each data point in the first data set according to a plurality of blood pressure ranges to generate a plurality of data groups corresponding to the plurality of blood pressure ranges; Calculating a plurality of blood flow velocity average values ​​respectively corresponding to the plurality of data groups; performing a first linear regression operation on the plurality of blood flow velocity average values ​​to generate a first regression line; Calculating a first slope of the first regression line to obtain a first index; Grouping a plurality of data sets including the first data set according to the first indicator to generate a plurality of automatic adjustment mode groups; receiving a second data set by the transceiver, and determining that the second data set corresponds to one of the plurality of automatic adjustment mode groups; as well as An automatic adjustment mode of the second data set is output, wherein the automatic adjustment mode corresponds to the one of the plurality of automatic adjustment mode groups.

2. The electronic device of claim 1, wherein the processor is further configured to perform: calculating cerebral vascular resistance according to the blood pressure and the blood flow velocity; Calculating a plurality of cerebral vascular resistance average values ​​respectively corresponding to the plurality of data groups; performing a second linear regression operation on the plurality of cerebral vascular resistance average values ​​to generate a second regression line; calculating a second slope of the second regression line to obtain a second index; as well as The plurality of data sets are grouped according to the first indicator and the second indicator to generate the plurality of automatic adjustment mode groups.

3. The electronic device of claim 1 , wherein the processor is further configured to perform: calculating a plurality of blood flow velocity standard deviations respectively corresponding to a plurality of blood flow velocity average values; generating an image according to the plurality of blood flow velocity average values ​​and the plurality of blood flow velocity standard deviations; and The image is output.

4. The electronic device of claim 1, wherein the processor is further configured to perform: The transceiver is communicatively connected to an ultrasound device and receives the blood flow rate at the data point from the ultrasound device.

5. The electronic device of claim 1, wherein the processor is further configured to perform: The transceiver is communicatively connected to a blood pressure monitor and receives the blood pressure at the data point from the blood pressure monitor. The electronic device of claim 1 , wherein each data point in the first data set corresponds to each heartbeat of the subject.

7. The electronic device of claim 1, wherein the processor is further configured to perform: An interpolation operation is performed on the first data set to upsample the first data set.

8. The electronic device of claim 1, wherein the processor is further configured to perform: In response to determining that the second data set corresponds to the one of the plurality of automatic adjustment mode groups, an alarm message is output.

9. A method for evaluating the autoregulatory mode of cerebral blood flow, characterized in that include: receiving a first data set, wherein data points in the first data set include blood pressure and a blood flow velocity corresponding to the blood pressure; Grouping each data point in the first data set according to a plurality of blood pressure ranges to generate a plurality of data groups corresponding to the plurality of blood pressure ranges; Calculating a plurality of blood flow velocity average values ​​respectively corresponding to the plurality of data groups; performing a first linear regression operation on the plurality of blood flow velocity average values ​​to generate a first regression line; Calculating a first slope of the first regression line to obtain a first index; Grouping a plurality of data sets including the first data set according to the first indicator to generate a plurality of automatic adjustment mode groups; receiving a second data set, and determining that the second data set corresponds to one of the plurality of automatic adjustment mode groups; as well as An automatic adjustment mode of the second data set is output, wherein the automatic adjustment mode corresponds to the one of the plurality of automatic adjustment mode groups.

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