CN114652276A - Pulse wave velocity detection method and device based on video image - Google Patents
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Abstract
The invention discloses a pulse wave velocity detection method and a device based on video images, wherein the method comprises the following steps: acquiring video data of preset duration of a preset body part of a person to be detected in a static state; processing the video data to obtain all frame images, respectively segmenting each frame image, and extracting a first target area and a second target area in each frame image; converting each frame image into a gray scale format, and calculating the actual distance between the gray scale gravity center of the first target area and the gray scale gravity center of the second target area; extracting pulse wave signals corresponding to the first target area and the second target area, and calculating the time difference between the pulse wave signal of the first target area and the pulse wave signal of the second target area by using a difference method; and calculating the pulse wave conduction velocity based on the processing result. The invention does not need a complex detection device, and reduces the workload and the injury to the human body.
Description
Technical Field
The invention relates to the technical field of non-contact physiological parameter detection, in particular to a pulse wave velocity detection method and device based on video images.
Background
The pulse wave refers to the Pulse Wave Velocity (PWV) at which the heart injects blood into the aorta in a fluctuating manner, and the aortic wall generates a pulse pressure wave, called pulse wave for short, which propagates along the vascular wall to the peripheral blood vessels at a certain velocity. The pulse wave velocity is an important standard for evaluating arteriosclerosis, and the detection of the pulse wave velocity can help clinicians to find the degree of vascular damage of patients with hypertension to a great extent; the pulse wave velocity has great significance in the assessment of cardiovascular and cerebrovascular disease risk and prognosis. Clinically, the pulse wave velocity is generally calculated by measuring and recording the pulse wave propagation time and distance between two artery parts, such as selecting brachial artery and ankle artery to determine arm-ankle PWV, selecting brachial artery and brachial artery to determine upper arm PWV, selecting carotid artery and femoral artery to determine cervical-femoral PWV, and the like.
Planar tension is one of the traditional methods for measuring PWV, and is mainly applicable to superficial arteries such as radial artery, femoral artery, carotid artery, and the like. The measurement process comprises the following steps: 1, selecting a measuring part and measuring the distance between two points; 2 placing the baroreceptors at the most obvious part of the pulsation of the measurement site; the PWV measurement device is started up 3. The planar tension method needs to pay attention to several influences, namely, the selection of the measuring position of the sensor needs to reduce the influence caused by the movement of an operator and a detected person, the probe needs to be perpendicular to the axis of the blood vessel as much as possible, and the pressing force needs to be enabled to just flatten the artery. Another method of measuring PWV is the catheterization method, which is an invasive method. Sending the radiography catheter into an artery sheath, sending the head end of the catheter into the root of an aorta, respectively connecting the tail end of the catheter and a side hole of the artery sheath with a pressure transducer, connecting the pressure transducers with a physiological recorder to obtain intra-arterial pressure waveforms of the root of the aorta and two points of the iliac artery, and measuring the distance between the starting points of the two pressure waveforms, namely the time difference T of pulse wave conduction1(ii) a The length of the contrast catheter entering the body is measured again, and the distance L between the two points is obtained by subtracting the length of the arterial sheath1Then the tip of the catheter is withdrawn to the abdominal aorta, and the above measurement is repeated to obtain T2And L2The distance between the aortic root and the abdominal aorta is L1-L2The transmission time of the pulse wave between these two points is T1-T2The pulse wave transmission distance is divided by the pulse wave transmission time to obtain the PWV. Although the catheterization method has high measurement accuracy, it has not been clinically widespread because it causes some harm to patients and is not accepted by most patients. In recent years, the relatively widely used method for measuring PWV in clinic is oscillography, and the oscillography measurement technology is used for measuring PWV by measuring ankle PWVAnd the measurement efficiency is improved due to the mobilization. The oscillography method has the advantages of simple operation, high repeatability and high accuracy. However, the conventional detection method needs a complicated detection device and has a large workload.
Disclosure of Invention
The invention provides a pulse wave velocity detection method and device based on video images, and aims to solve the technical problems that a detection device in the prior art is complex, large in workload and harmful to a human body.
In order to solve the technical problems, the invention provides the following technical scheme:
in one aspect, the present invention provides a pulse wave velocity detection method based on a video image, including:
acquiring video data of preset duration of a preset body part of a person to be detected in a static state;
processing the video data to obtain all frame images in the video data, segmenting each obtained frame image respectively, and extracting a first target area and a second target area in each frame image; in each frame of image, the position and the size of the first target area in the image are the same; meanwhile, in each frame of image, the position and the size of the second target area in the image are the same;
converting each frame image into a gray scale format, acquiring gray scale gravity centers of the first target area and the second target area, and calculating an actual distance between the gray scale gravity center of the first target area and the gray scale gravity center of the second target area; extracting pulse wave signals corresponding to the first target area and the second target area, and calculating the time difference between the pulse wave signal of the first target area and the pulse wave signal of the second target area by using a difference method;
and calculating the conduction velocity of the pulse wave by taking the actual distance between the two gray gravity centers as the conduction distance of the pulse wave and the time difference between the two pulse wave signals as the conduction time of the pulse wave.
Further, acquiring the gray scale gravity centers of the first target region and the second target region includes:
calculating to obtain coordinate values of the gray gravity center of the corresponding target area in the single-frame image through the following formula; wherein the respective target region refers to the first target region or the second target region;
wherein, a0、b0Horizontal and vertical coordinates respectively representing the gray scale gravity center of the corresponding target area; q represents the corresponding target area; w (x, y) represents the gray value of a point with coordinates (x, y) in the corresponding target region;
and averaging the coordinates of the gray scale gravity centers of all the frame images to obtain the gray scale gravity center position of the corresponding target area.
Further, the calculating an actual distance between the gray scale gravity center of the first target region and the gray scale gravity center of the second target region includes:
calculating the distance between the gray scale gravity center of the first target area and the gray scale gravity center of the second target area in the image based on the gray scale gravity center position of the first target area and the gray scale gravity center position of the second target area;
and acquiring a scale of the distance and the actual distance in the image, and calculating the actual distance of the two gray gravity centers according to the scale and the distance of the two gray gravity centers in the image and the scale conversion.
Further, extracting the pulse wave signals corresponding to the first target area and the second target area includes:
calculating the third quartile of all pixel points in the corresponding target area of each frame of image, and solving the mean value of the third quartile of the pixel points corresponding to all the frames of images; for each frame of image, counting the total number of pixel points with gray values larger than the mean value in the corresponding target area, and taking the counted total number of pixel points as the intensity of the pulse wave signal at the moment corresponding to the current frame of image; drawing a curve by taking the frame number of the image as an abscissa and the intensity of the pulse wave signal corresponding to each frame of the image as an ordinate, wherein the curve is used as an original pulse wave signal corresponding to a corresponding target area; wherein the respective target region refers to the first target region or a second target region;
and carrying out empirical mode decomposition on the original pulse wave signal, decomposing signal components with different frequency components, filtering low-frequency baseline translation and high-frequency noise interference components according to the frequency characteristics of the pulse wave signal to obtain a filtered pulse wave signal, and taking the filtered pulse wave signal as a final pulse wave signal.
Further, the calculating a time difference between the pulse wave signal of the first target area and the pulse wave signal of the second target area by using a difference method includes:
respectively acquiring all wave crests and wave troughs of the filtered pulse wave signals corresponding to the first target area and the second target area; and calculating the average value of the difference values of the adjacent peak time frames and the average value of the difference values of the adjacent valley time frames of the two pulse wave signals, averaging the average value of the difference values of the peak time frames and the average value of the difference values of the valley time frames, and recording the calculation result as the time difference of the pulse wave signals of the two target areas.
In another aspect, the present invention further provides a pulse wave velocity detection apparatus based on a video image, including:
the video data acquisition module is used for acquiring video data of preset duration of a preset body part of a person to be detected in a static state;
a data processing module for performing the steps of:
processing the video data to obtain all frame images in the video data, segmenting each obtained frame image respectively, and extracting a first target area and a second target area in each frame image; in each frame image, the position and the size of the first target area in the image are the same; meanwhile, in each frame of image, the position and the size of the second target area in the image are the same;
converting each frame image into a gray scale format, acquiring gray scale gravity centers of the first target area and the second target area, and calculating an actual distance between the gray scale gravity center of the first target area and the gray scale gravity center of the second target area; extracting pulse wave signals corresponding to the first target area and the second target area, and calculating the time difference between the pulse wave signal of the first target area and the pulse wave signal of the second target area by using a difference method;
and calculating the conduction velocity of the pulse wave by taking the actual distance between the two gray gravity centers as the conduction distance of the pulse wave and the time difference between the two pulse wave signals as the conduction time of the pulse wave.
Further, the data processing module is specifically configured to:
calculating to obtain coordinate values of the gray gravity center of the corresponding target area in the single-frame image through the following formula; wherein the respective target region refers to the first target region or the second target region;
wherein, a0、b0Horizontal and vertical coordinates respectively representing the gray scale gravity center of the corresponding target area; q represents the corresponding target area; w (x, y) represents the gray value of a point with coordinates (x, y) in the corresponding target region;
and averaging the coordinates of the gray scale gravity centers of all the frame images to obtain the gray scale gravity center position of the corresponding target area.
Further, the data processing module is specifically further configured to:
calculating the distance between the gray scale gravity center of the first target area and the gray scale gravity center of the second target area in the image based on the gray scale gravity center position of the first target area and the gray scale gravity center position of the second target area;
and acquiring a scale of the distance and the actual distance in the image, and calculating the actual distance of the two gray barycenters according to the scale and the ratio conversion based on the distance of the two gray barycenters in the image.
Further, the data processing module is specifically further configured to:
calculating the third quartile of all pixel points in the corresponding target area of each frame of image, and solving the mean value of the third quartile of the pixel points corresponding to all the frames of images; for each frame of image, counting the total number of pixel points with gray values larger than the mean value in the corresponding target area, and taking the counted total number of pixel points as the intensity of the pulse wave signal at the moment corresponding to the current frame of image; drawing a curve by taking the frame number of the image as an abscissa and the intensity of the pulse wave signal corresponding to each frame of the image as an ordinate, wherein the curve is used as an original pulse wave signal corresponding to a corresponding target area; wherein the respective target region refers to the first target region or a second target region;
and carrying out empirical mode decomposition on the original pulse wave signal, decomposing signal components with different frequency components, filtering low-frequency baseline translation and high-frequency noise interference components according to the frequency characteristics of the pulse wave signal to obtain a filtered pulse wave signal, and taking the filtered pulse wave signal as a final pulse wave signal.
Further, the data processing module is specifically further configured to:
respectively acquiring all wave crests and wave troughs of the filtered pulse wave signals corresponding to the first target area and the second target area; and calculating the average value of the difference values of the adjacent peak time frames and the average value of the difference values of the adjacent valley time frames of the two pulse wave signals, averaging the average value of the difference values of the peak time frames and the average value of the difference values of the valley time frames, and recording the calculation result as the time difference of the pulse wave signals of the two target areas.
In yet another aspect, the present invention also provides an electronic device comprising a processor and a memory; wherein the memory has stored therein at least one instruction that is loaded and executed by the processor to implement the above-described method.
In yet another aspect, the present invention also provides a computer-readable storage medium having at least one instruction stored therein, the instruction being loaded and executed by a processor to implement the above method.
The technical scheme provided by the invention has the beneficial effects that at least:
the pulse wave velocity detection scheme provided by the invention utilizes a smart phone or other video equipment to shoot 10s or 30s video image data of the wrist or arm part of a person to be detected in a normal video state, utilizes a computer and corresponding software to process collected data to obtain each frame of video image frame data of a video, divides the image frame to obtain two target areas, and counts the total number of the gray value of the target areas larger than the third quartile of all the image frames to obtain the original pulse wave signals of the two target areas; the method comprises the steps of carrying out empirical mode decomposition on signals, filtering out baseline translation and high-frequency noise to obtain relatively pure pulse wave signals, calculating positions of peaks and troughs of pulse waves of two target regions by using a difference method, calculating adjacent peak time differences and adjacent trough time differences of the pulse waves of the two target regions respectively, counting the mean value of the time differences, calculating gray level gravity centers of the two target regions by using a gray level gravity center method, measuring the distance between the two gravity centers, and converting according to an actual proportion to obtain the actual distance between the two gray level gravity centers. And finally, according to a PWV calculation formula, dividing the distance between the gray gravity centers of the two target areas by the time difference of the pulse waves of the two target areas to calculate the pulse wave conduction velocity. The invention adopts a non-contact video method to obtain the pulse wave signals, does not need a complex detection device, has simple and convenient operation, saves time, and reduces workload and harm to human bodies.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flow chart illustrating an implementation of a method for detecting pulse wave velocity based on video images according to an embodiment of the present invention;
FIG. 2 is a diagram of one frame of a 10s arm video taken by a mobile phone according to an embodiment of the present invention;
FIG. 3 is an image of a target region obtained by segmenting the image frame of FIG. 2 according to an embodiment of the present invention;
FIG. 4 is a schematic flowchart illustrating an implementation of a method for extracting an original pulse wave signal of a target region and calculating a time difference between the pulse wave signals of two target regions according to an embodiment of the present invention;
FIG. 5 is a graph of the raw pulse wave signal extracted from a target area above all the image frames according to the present invention;
FIG. 6A is a schematic diagram of various signal components obtained by empirical mode decomposition of the pulse wave signal shown in FIG. 5 according to an embodiment of the present invention;
FIG. 6B is a graph illustrating a relatively pure pulse wave signal obtained after de-noising the pulse wave signal shown in FIG. 5 according to an embodiment of the present invention;
fig. 7 is a schematic flow chart of an implementation of the method for obtaining the gray centers of gravity of two target regions and calculating the actual distance between the gray centers of gravity of the two target regions according to the embodiment of the present invention;
fig. 8 is a pulse wave graph of two target areas of 10s video captured by a mobile phone according to the embodiment of the present invention after pulse wave extraction and filtering; wherein, data1 is the solid curve representing the pulse wave signal of the upper target area, and data2 is the dashed curve representing the pulse wave signal of the lower target area.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
First embodiment
The embodiment provides a pulse wave velocity detection method based on a video image, which can be implemented by an electronic device, and requires a smart phone or other video equipment. The method is based on the basic principle that a video device is used for shooting an arm part for 10s or 30s in a normal video state to acquire video image data, a computer and corresponding software are used for processing the acquired data to obtain each frame of image frame data of a video, the image frame is divided to obtain two target areas, the total number of the gray values of the target areas, which are larger than the third quartile number of all the image frames, is counted to obtain original pulse wave signals of the two target areas, the signals are subjected to empirical mode decomposition to filter out baseline translation and high-frequency noise to obtain pure pulse wave signals, the waveform time difference is calculated, the gray gravity center of the target areas is calculated by using a gray gravity center method, the actual distance between the two gravity centers is measured, and then the pulse wave conduction speed is calculated according to a calculation formula of PWV. Specifically, the execution flow of the pulse wave velocity detection method based on the video image is shown in fig. 1, and includes the following steps:
s1, acquiring video data of preset duration of a preset body part of a person to be detected in a static state;
it should be noted that, in this embodiment, the extraction of the pulse wave signal is derived from a video shot by a smart phone, a camera, or other video equipment; therefore, during detection, video recording equipment is required to be used for shooting video data of 10 seconds or 30 seconds of the arm of the person to be detected in a static state under a normal working state, and the video data is transmitted to relevant computer software for storage by using connecting equipment, so that the video data can be processed by using corresponding software in the following process.
It is understood that, firstly, the device for capturing video is not limited to a smart phone, and other devices with capturing functions such as a camera and a monitor may be used as the capturing device of the present invention to obtain the original video, and in order to ensure that sufficient pulse wave information can be obtained from the video, the respective rate of the video image should be not lower than 1920 × 1080; secondly, the part of the person to be detected is shot without being limited to the arm of the person to be detected, the limbs and even the face of the body of the person to be detected can also be used as a video source for acquiring the pulse wave signals, and compared with other parts of the body, the arm has the advantages of rich blood vessels, strong pulse wave signals and straight arterial blood vessels; and finally, when the video is shot, the static state of the shot part is ensured as much as possible, the jitter is reduced, the time length of shooting the video is not limited to 10s and 30s, and 10 s-30 s are more appropriate time length intervals.
S2, processing the video data to obtain all frame images in the video data, segmenting each obtained frame image respectively, and extracting a first target area and a second target area in each frame image; in each frame image, the position and the size of the first target area in the image are the same; meanwhile, in each frame of image, the position and the size of the second target area in the image are the same;
specifically, in this embodiment, the duration of the captured video is 10.56s, the video frame rate is 30.0583, the total number of frames of the video is 317, the video resolution is 1920 × 1080, the video format is RGB24, and one frame of image of the video is as shown in fig. 2. All frame images of the video are extracted, each image frame is divided to obtain two target areas, the result of dividing the image frame shown in fig. 2 is shown in fig. 3, wherein the areas marked by two black boxes are the target areas (upper target area and lower target area). The size of the upper black box is 301 × 301, and the size of the lower black box is 401 × 401, it should be noted that the selection of the target region is not limited to the above region size, and it needs to be determined according to different conditions of different shooting sites, and the selection principle is to include the vascular artery as much as possible, and in addition, it needs to be adjusted according to the difference of different limbs of different people.
S3, converting each frame image into a gray scale format, acquiring gray scale gravity centers of the first target area and the second target area, and calculating the actual distance between the gray scale gravity center of the first target area and the gray scale gravity center of the second target area; extracting pulse wave signals corresponding to the first target area and the second target area, and calculating the time difference between the pulse wave signal of the first target area and the pulse wave signal of the second target area by using a difference method;
in this embodiment, in order to facilitate the calculation of the subsequent gray center of gravity, the image format of each frame is converted from the RGB format to the gray format, in step S3, an original pulse wave curve is obtained by counting the gray features of the pixel points in the two target regions in all the frames of the video, then the original pulse wave is subjected to empirical mode decomposition to remove the baseline translation and the high-frequency noise, so as to obtain a relatively pure pulse wave signal, and the time difference between the pulse wave signals in the two target regions is calculated by using a difference method based on the baseline translation and the high-frequency noise. And the above S3 also obtains the actual distance by obtaining the gray scale barycenter of the two target regions, calculating the distance between the gray scale barycenter of the two target regions, and measuring.
Specifically, in the present embodiment, an execution flow of the method for extracting an original pulse wave signal of a target area and calculating a time difference between pulse wave signals of two target areas is shown in fig. 4, and includes the following steps:
step 1, respectively counting the gray features of two target areas, and calculating to obtain the original pulse wave signals of the two target areas based on the counted gray features, specifically as follows:
calculating the third quartile of all pixels in the target area of each frame of image, calculating the mean value of the third quartile of all the pixels, counting the total number of the pixels of which the gray value of the target area is greater than the mean value for each frame of image, taking the total value of the pixels as the intensity of the pulse wave signal at the moment of the image frame, and drawing a curve, namely a pulse wave signal curve, of the value of all the image frames and the number of the image frames, as shown in fig. 5, wherein the horizontal axis F represents the number of the image frames, and the vertical axis S represents the intensity of the acquired pulse wave signal, and the pulse wave shown in fig. 5 contains a large amount of noise interference, so that the interference of baseline translation can be obviously seen.
And 2, carrying out empirical mode decomposition on the original pulse wave signal, decomposing signal components with different frequency components, filtering low-frequency baseline translation and high-frequency noise interference components according to the frequency characteristics of the pulse wave signal, and taking the relatively pure pulse wave signal obtained after filtering as the final pulse wave signal.
It should be noted that, performing empirical mode decomposition on the pulse wave signal is to obtain an eigenmode function. According to the concept of the eigenmode functions, any signal is composed of a plurality of eigenmode functions, and the eigenmode functions are mutually overlapped and influenced to form a composite function. Therefore, the empirical mode decomposition of the pulse wave signal is to obtain the eigenmode functions of the respective orders of the pulse wave signal. The specific steps of performing empirical mode decomposition on the pulse wave signal are as follows. The method comprises the steps of firstly, obtaining all maximum value points of an original pulse wave signal, fitting by using a cubic spline function to obtain an upper envelope curve, secondly, obtaining all minimum value points of the original pulse wave signal, fitting by using the cubic spline function to obtain a lower envelope curve, thirdly, recording the mean value of the upper envelope curve and the lower envelope curve as an average envelope curve, subtracting the average envelope curve from the original pulse wave signal to obtain a new signal which is an eigenmode function of the original pulse wave, and fourthly, obtaining first-order or high-order differential of the original pulse wave, and repeating the steps from one to three to obtain the eigenmode function of the original pulse wave. As shown in fig. 6A, the signal components of imf1 and imf2 belong to low-frequency noise interference, the signal components of imf5 and imf6 belong to high-frequency noise, and res is residual interference caused by baseline shift according to the frequency characteristics of the pulse wave signals, so that the sum of the signal components of imf3 and imf4 is the extracted pulse wave signal component, and as shown in fig. 6B, the result of the empirical mode decomposition of the original pulse wave shown in fig. 5 is a relatively pure pulse wave signal obtained after the pulse wave shown in fig. 5 is subjected to the empirical mode decomposition filtering. Wherein the horizontal axis F represents the number of frames of images, and the vertical axis S represents the intensity of the acquired pulse wave signals.
Fig. 8 shows a graph of the pulse wave obtained after the filtering process of the pulse wave extracted from two target areas in the 10s video captured by the mobile phone; wherein, data1 is the solid curve representing the pulse wave signal of the upper target area, and data2 is the dashed curve representing the pulse wave signal of the lower target area.
Step 3, calculating the time difference of the pulse wave signals of the two target areas by using a difference method, wherein the time difference is as follows:
the first order difference is carried out on the pulse wave after filtering, namely the pulse wave signals of two adjacent frames are subjected to difference, at the peak, the adjacent difference value in the front is positive number, and the adjacent difference value in the back is negative number, so all peaks of the pulse wave can be obtained by the characteristic, but because the wavelet peak in the pulse wave is not the peak of the real pulse wave waveform, the embodiment sets 1.2 times of the average value of the pulse wave intensity as the threshold, and if the detected peak is smaller than the value, the peak is discarded; in the valley, all valleys of the pulse wave can be obtained by the same method as the above-mentioned peak obtaining method, in which the adjacent difference value before the valley is a negative number and the adjacent difference value after the valley is a positive number. After obtaining all peaks and troughs of the filtered pulse wave signals corresponding to the two target areas respectively, firstly calculating the average value of the difference values of adjacent peak time frames and the average value of the difference values of adjacent trough time frames of the two pulse waves, then averaging the average value of the difference values of the peak time frames and the average value of the difference values of the trough time frames, and recording the result as the waveform time difference of the pulse waves of the two target areas.
Further, the execution flow of the method for acquiring the gray scale barycenters of the two target regions and calculating the actual distance between the gray scale barycenters of the two target regions in the embodiment is shown in fig. 7, and includes the following steps:
step 1, obtaining gray scale gravity center positions of two target areas, which is as follows:
for a frame of image of a video, the gray value square of all pixel points in a target area is taken as a weight, the horizontal and vertical coordinates of all the pixel points are multiplied by the weight, and then the weight is averaged to obtain the horizontal and vertical coordinates of the gray gravity center, wherein the formula is as follows:
wherein, a0、b0Horizontal and vertical coordinates respectively representing the gray scale gravity center of the corresponding target area; q represents the corresponding target area; w (x, y) represents the gray value of a point with coordinates (x, y) in the corresponding target region;
and averaging the coordinates of the gray scale gravity centers of all the frame images to obtain the gray scale gravity center position of the corresponding target area.
And 2, calculating the distance between the gray gravity centers of the two target areas in the image according to a distance formula.
And 3, acquiring a scale of the distance and the actual distance in the image, and calculating the actual distance of the two gray gravity centers according to the scale and the distance of the two gray gravity centers in the image and the scale conversion.
Specifically, in the embodiment, the arm positions corresponding to the upper and lower boundaries in the shot video are recorded, the actual distances of the upper and lower boundaries of the shot arm video are obtained through measurement, the ratio of the shot video to the actual distances is obtained by combining the pixel distances of the upper and lower boundaries, and then the actual distances of the two gray gravity centers are obtained through conversion according to the distance between the two gray gravity centers in the image and the ratio between the shot video and the actual distances.
And S4, calculating the conduction velocity of the pulse wave by taking the actual distance between the two gray centers as the conduction distance of the pulse wave and the time difference between the two pulse wave signals as the conduction time of the pulse wave.
Specifically, the present embodiment obtains the pulse wave velocity of the captured arm video by dividing the gray scale center of gravity actual distance calculated in S3 by the pulse wave time difference calculated in S3.
In summary, the pulse wave velocity detection method of the embodiment utilizes a smart phone or other video recording devices to shoot 10s or 30s video image data of the wrist or arm part of a person to be detected in a normal video recording state, utilizes a computer and corresponding software to process the collected data to obtain each frame of video image frame data of a video, divides the image frame to obtain two target areas, and counts the total number of the gray values of the target areas larger than the third quartile of all the image frames to obtain the original pulse wave signals of the two target areas; the method comprises the steps of carrying out empirical mode decomposition on signals, filtering baseline translation and high-frequency noise to obtain relatively pure pulse wave signals, calculating positions of peaks and troughs of pulse waves of two target regions by using a difference method, calculating adjacent peak time differences and adjacent trough time differences of the pulse waves of the two target regions respectively, counting the mean value of the time differences, finally taking the mean value as the waveform time differences of the pulse waves of the two target regions, calculating gray gravity centers of the two target regions by using a gray gravity center method, measuring the distance between the two gravity centers, and converting according to an actual proportion to obtain the actual distance between the two gray gravity centers. And finally, according to a PWV calculation formula, dividing the distance between the gray gravity centers of the two target areas by the time difference of the pulse waves of the two target areas to calculate the pulse wave conduction velocity. The invention adopts a non-contact video method to obtain the pulse wave signals, does not need a complex detection device, has simple and convenient operation, saves time, and reduces workload and harm to human bodies.
Second embodiment
The embodiment provides a pulse wave velocity detection device based on video images, which comprises:
the video data acquisition module is used for acquiring video data of preset duration of a preset body part of a person to be detected in a static state;
a data processing module for performing the steps of:
processing the video data to obtain all frame images in the video data, segmenting each obtained frame image respectively, and extracting a first target area and a second target area in each frame image; in each frame image, the position and the size of the first target area in the image are the same; meanwhile, in each frame of image, the position and the size of the second target area in the image are the same;
converting each frame image into a gray scale format, acquiring gray scale gravity centers of the first target area and the second target area, and calculating an actual distance between the gray scale gravity center of the first target area and the gray scale gravity center of the second target area; extracting pulse wave signals corresponding to the first target area and the second target area, and calculating the time difference between the pulse wave signal of the first target area and the pulse wave signal of the second target area by using a difference method;
and calculating the conduction velocity of the pulse wave by taking the actual distance between the two gray gravity centers as the conduction distance of the pulse wave and the time difference between the two pulse wave signals as the conduction time of the pulse wave.
The pulse wave velocity detection apparatus based on video images of the present embodiment corresponds to the pulse wave velocity detection method based on video images of the first embodiment described above; the pulse wave velocity detection apparatus based on video images of the present embodiment is used to implement the pulse wave velocity detection method based on video images of the first embodiment; the functions implemented by the functional modules in the device for detecting pulse wave velocity based on video images of the present embodiment correspond to the flow steps in the method for detecting pulse wave velocity based on video images of the first embodiment one by one; therefore, it is not described herein.
Third embodiment
The present embodiment provides an electronic device, which includes a processor and a memory; wherein the memory has stored therein at least one instruction that is loaded and executed by the processor to implement the method of the first embodiment.
The electronic device may have a relatively large difference due to different configurations or performances, and may include one or more processors (CPUs) and one or more memories, where at least one instruction is stored in the memory, and the instruction is loaded by the processor and executes the method.
Fourth embodiment
The present embodiment provides a computer-readable storage medium, in which at least one instruction is stored, and the instruction is loaded and executed by a processor to implement the method of the first embodiment. The computer readable storage medium may be, among others, ROM, random access memory, CD-ROM, magnetic tape, floppy disk, optical data storage device, and the like. The instructions stored therein may be loaded by a processor in the terminal and perform the above-described method.
Furthermore, it should be noted that the present invention may be provided as a method, apparatus or computer program product. Accordingly, embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present invention may take the form of a computer program product embodied on one or more computer-usable storage media having computer-usable program code embodied in the medium.
Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, embedded processor, or other programmable data processing terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks. These computer program instructions may also be loaded onto a computer or other programmable data processing terminal to cause a series of operational steps to be performed on the computer or other programmable terminal to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It is further noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or terminal that comprises the element.
Finally, it should be noted that while the above describes a preferred embodiment of the invention, it will be appreciated by those skilled in the art that, once the basic inventive concepts have been learned, numerous changes and modifications may be made without departing from the principles of the invention, which shall be deemed to be within the scope of the invention. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the embodiments of the invention.
Claims (10)
1. A pulse wave velocity detection method based on video images is characterized by comprising the following steps:
acquiring video data of preset duration of a preset body part of a person to be detected in a static state;
processing the video data to obtain all frame images in the video data, segmenting each obtained frame image respectively, and extracting a first target area and a second target area in each frame image; in each frame image, the position and the size of the first target area in the image are the same; meanwhile, in each frame of image, the position and the size of the second target area in the image are the same;
converting each frame image into a gray scale format, acquiring gray scale gravity centers of the first target area and the second target area, and calculating an actual distance between the gray scale gravity center of the first target area and the gray scale gravity center of the second target area; extracting pulse wave signals corresponding to the first target area and the second target area, and calculating the time difference between the pulse wave signal of the first target area and the pulse wave signal of the second target area by using a difference method;
and calculating the conduction velocity of the pulse wave by taking the actual distance between the two gray gravity centers as the conduction distance of the pulse wave and the time difference between the two pulse wave signals as the conduction time of the pulse wave.
2. The method of detecting pulse wave velocity based on video image according to claim 1, wherein obtaining the gray center of gravity of the first target region and the second target region comprises:
calculating to obtain coordinate values of the gray gravity center of the corresponding target area in the single-frame image through the following formula; wherein the respective target region refers to the first target region or the second target region;
wherein, a0、b0Horizontal and vertical coordinates respectively representing the gray scale gravity center of the corresponding target area; q represents the corresponding target area; w (x, y) represents a gray value of a point of coordinates (x, y) in the corresponding target region;
and averaging the coordinates of the gray scale gravity centers of all the frame images to obtain the gray scale gravity center position of the corresponding target area.
3. The method according to claim 2, wherein the calculating an actual distance between the center of gravity of the gray scale of the first target region and the center of gravity of the gray scale of the second target region comprises:
calculating the distance between the gray scale gravity center of the first target area and the gray scale gravity center of the second target area in the image based on the gray scale gravity center position of the first target area and the gray scale gravity center position of the second target area;
and acquiring a scale of the distance and the actual distance in the image, and calculating the actual distance of the two gray barycenters according to the scale and the ratio conversion based on the distance of the two gray barycenters in the image.
4. The method for detecting pulse wave velocity based on video image of claim 1, wherein the extracting the pulse wave signals corresponding to the first target area and the second target area comprises:
calculating the third quartile of all pixel points in the corresponding target area of each frame of image, and solving the mean value of the third quartile of the pixel points corresponding to all the frames of images; for each frame of image, counting the total number of pixel points with gray values larger than the mean value in the corresponding target area, and taking the counted total number of pixel points as the intensity of the pulse wave signal at the moment corresponding to the current frame of image; drawing a curve by taking the frame number of the image as an abscissa and the intensity of the pulse wave signal corresponding to each frame of image as an ordinate, and taking the curve as an original pulse wave signal corresponding to a corresponding target area; wherein the respective target region refers to the first target region or a second target region;
and carrying out empirical mode decomposition on the original pulse wave signal, decomposing signal components with different frequency components, filtering low-frequency baseline translation and high-frequency noise interference components according to the frequency characteristics of the pulse wave signal to obtain a filtered pulse wave signal, and taking the filtered pulse wave signal as a final pulse wave signal.
5. The method for detecting pulse wave velocity based on video images of claim 4, wherein the calculating the time difference between the pulse wave signal of the first target area and the pulse wave signal of the second target area by using the difference method comprises:
respectively acquiring all wave crests and wave troughs of the filtered pulse wave signals corresponding to the first target area and the second target area; and calculating the average value of the difference values of the adjacent peak time frames and the average value of the difference values of the adjacent valley time frames of the two pulse wave signals, averaging the average value of the difference values of the peak time frames and the average value of the difference values of the valley time frames, and recording the calculation result as the time difference of the pulse wave signals of the two target areas.
6. A pulse wave velocity detection apparatus based on a video image, comprising:
the video data acquisition module is used for acquiring video data of preset duration of a preset body part of a person to be detected in a static state;
a data processing module for performing the steps of:
processing the video data to obtain all frame images in the video data, segmenting each obtained frame image respectively, and extracting a first target area and a second target area in each frame image; in each frame image, the position and the size of the first target area in the image are the same; meanwhile, in each frame of image, the position and the size of the second target area in the image are the same;
converting each frame image into a gray scale format, acquiring gray scale gravity centers of the first target area and the second target area, and calculating an actual distance between the gray scale gravity center of the first target area and the gray scale gravity center of the second target area; extracting pulse wave signals corresponding to the first target area and the second target area, and calculating the time difference between the pulse wave signal of the first target area and the pulse wave signal of the second target area by using a difference method;
and calculating the conduction velocity of the pulse wave by taking the actual distance between the two gray gravity centers as the conduction distance of the pulse wave and the time difference between the two pulse wave signals as the conduction time of the pulse wave.
7. The video-image-based pulse wave velocity detection apparatus according to claim 6, wherein the data processing module is specifically configured to:
calculating to obtain coordinate values of the gray gravity center of the corresponding target area in the single-frame image through the following formula; wherein the respective target region refers to the first target region or the second target region;
wherein, a0、b0Horizontal and vertical coordinates respectively representing the gray scale gravity center of the corresponding target area; q represents the corresponding target area; w (x, y) represents the gray value of a point with coordinates (x, y) in the corresponding target region;
and averaging the coordinates of the gray scale gravity centers of all the frame images to obtain the gray scale gravity center position of the corresponding target area.
8. The video-image-based pulse wave velocity detection apparatus according to claim 7, wherein the data processing module is further configured to:
calculating the distance between the gray scale gravity center of the first target area and the gray scale gravity center of the second target area in the image based on the gray scale gravity center position of the first target area and the gray scale gravity center position of the second target area;
and acquiring a scale of the distance and the actual distance in the image, and calculating the actual distance of the two gray barycenters according to the scale and the ratio conversion based on the distance of the two gray barycenters in the image.
9. The video-image-based pulse wave velocity detection apparatus according to claim 6, wherein the data processing module is further configured to:
calculating the third quartile of all pixel points in the corresponding target area of each frame of image, and solving the mean value of the third quartile of the pixel points corresponding to all the frames of images; for each frame of image, counting the total number of pixel points with gray values larger than the mean value in the corresponding target area, and taking the counted total number of pixel points as the intensity of the pulse wave signal at the moment corresponding to the current frame of image; drawing a curve by taking the frame number of the image as an abscissa and the intensity of the pulse wave signal corresponding to each frame of the image as an ordinate, wherein the curve is used as an original pulse wave signal corresponding to a corresponding target area; wherein the respective target region refers to the first target region or a second target region;
and performing empirical mode decomposition on the original pulse wave signal, decomposing signal components with different frequency components, filtering low-frequency baseline translation and high-frequency noise interference components according to the frequency characteristics of the pulse wave signal to obtain a filtered pulse wave signal, and taking the filtered pulse wave signal as a final pulse wave signal.
10. The video-image-based pulse wave velocity detection apparatus according to claim 9, wherein the data processing module is further configured to:
respectively acquiring all wave crests and wave troughs of the filtered pulse wave signals corresponding to the first target area and the second target area; and calculating the average value of the difference values of the adjacent peak time frames and the average value of the difference values of the adjacent valley time frames of the two pulse wave signals, averaging the average value of the difference values of the peak time frames and the average value of the difference values of the valley time frames, and recording the calculation result as the time difference of the pulse wave signals of the two target areas.
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| CN115500803A (en) * | 2022-09-29 | 2022-12-23 | 联想(北京)有限公司 | Information determination method and electronic equipment |
| CN116019435A (en) * | 2022-12-27 | 2023-04-28 | 北京镁伽机器人科技有限公司 | Heart-like heart rate determining method and device, electronic equipment and storage medium |
| CN116098598A (en) * | 2022-12-27 | 2023-05-12 | 北京镁伽机器人科技有限公司 | Heart-like wave crest detection and heart rate determination methods and related products |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115500803A (en) * | 2022-09-29 | 2022-12-23 | 联想(北京)有限公司 | Information determination method and electronic equipment |
| CN116019435A (en) * | 2022-12-27 | 2023-04-28 | 北京镁伽机器人科技有限公司 | Heart-like heart rate determining method and device, electronic equipment and storage medium |
| CN116098598A (en) * | 2022-12-27 | 2023-05-12 | 北京镁伽机器人科技有限公司 | Heart-like wave crest detection and heart rate determination methods and related products |
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