CN111387959A - Non-contact physiological parameter detection method based on IPPG - Google Patents
Non-contact physiological parameter detection method based on IPPG Download PDFInfo
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- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/024—Detecting, measuring or recording pulse rate or heart rate
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Abstract
The invention discloses a non-contact physiological parameter detection method based on IPPG. Belongs to the field of video image processing; the method comprises the following steps: recording a video, Euler amplifying, face recognition, eliminating motion artifacts, acquiring a heart rate and acquiring heart rate variability and a respiratory rate; the physiological parameter detection system is designed for non-contact physiological parameter detection; the method has the advantages of no contact with a tested part, simple and easy operation and the like, can solve the problem that patients with skin burn or limb deformity and infants are difficult to use contact instruments to measure physiological parameters, extracts the region of interest by facial segmentation, avoids the influence of blinking of eyes on signals, effectively removes motion errors by adopting a combined sparse spectrum reconstruction algorithm, improves the signals, and can be suitable for detecting various physiological parameters.
Description
Technical Field
The invention relates to the field of video image processing, in particular to a non-contact physiological parameter detection system method based on IPPG.
Background
The burden of cardiovascular diseases is gradually increased, which becomes a great public health problem and the prevention and treatment of cardiovascular diseases are not easy. Among them, heart rate is an important physiological parameter for measuring cardiovascular health status, and plays an important role in daily monitoring and diagnosis and treatment. The fast resting heart rate can be used as an independent risk index to predict the morbidity and mortality of cardiovascular diseases, and the fast exercise heart rate can reflect the cardio-pulmonary function and also help to keep the exercise intensity at a proper level.
Currently, although Photoplethysmography (PPG) has been widely used in the biomedical field, there are still inherent limitations. The ability to achieve only a single point of monitoring, the detection process requires the sensor to be in contact with the skin, etc., limiting its utility in situations such as perfusion imaging and healing assessment or where free motion is required. Therefore, Imaging Photoplethysmography (IPPG), a non-contact measurement technique, has been increasingly emphasized in recent years, has the advantages of no contact with a tested part, simple and easy operation, and the like, and can solve the problem that patients with skin burn or limb disability and infants are difficult to measure physiological parameters by using contact instruments.
Disclosure of Invention
Aiming at the problems, the invention provides a non-contact physiological parameter detection system method based on IPPG; thereby solving the problem that the application of the current IPPG technology is limited by imaging equipment.
The technical scheme of the invention is as follows: a non-contact physiological parameter detection method based on IPPG comprises the following steps:
(1.1), recording video: the tested object is seated in front of the computer, the face of the tested object is aligned to the camera, and the camera carried by the computer is used for recording a video of one minute;
(1.2), Euler amplification: performing Euler amplification processing on the recorded video;
(1.3), region of interest selection: the method comprises the following steps of firstly obtaining the position of an eye region through face recognition, and then carrying out face segmentation on a face, wherein the segmentation step comprises the following steps:
(1.3.1) setting the length of the eye region as a and the width as b;
(1.3.2) selecting a face area with the length of a and the width of 1.5b below the face area as an interested area, and avoiding errors caused by inevitable physiological activities such as winking and the like;
(1.4) obtaining three-channel signals of the IPPG signal; calculating the gray average value of R, G, B three channels of the interesting region of each frame, performing linear combination operation on the three channels to make the signal closer to the real IPPG signal,
(1.5) elimination of motion artifacts: eliminating the motion artifact of the IPPG signal by adopting a joint sparse spectrum reconstruction algorithm; separating spectral regions that affect skin absorption; removing the spectral range with small influence factors;
(1.6), acquisition of heart rate: setting a frequency range of heart rate, extracting effective peak values in the frequency range, and converting the effective peak values into time domains to obtain heart rate values;
(1.7), acquisition of heart rate variability and respiration rate: respectively extracting 100 continuous RR interval data, performing phase space reconstruction on the RR interval data, constructing a probability density function curve, and distinguishing atrial fibrillation from normal sinus rhythm through a skewness coefficient of the probability density function curve and an abscissa position corresponding to a curve peak value so as to detect heart rate variability;
the results show that the method can rapidly detect heart rate variability by using 100 continuous pulse interval data to distinguish atrial fibrillation from normal sinus rhythm, wherein the accuracy rate is 0.94.
Further, in the euler amplification in the step (1.2), the brightness value of the pixel point in each detection image in the time sequence is analyzed, the low-frequency part is selected for amplification, and then the low-frequency part and the original image are superposed to synthesize a final image, so that weak change information is amplified.
Further, the euler amplification processing is performed on the recorded video in the step (1.2), and the euler amplification processing method includes the following steps:
(1.2.1) decomposing each frame image of the video signal into different spatial resolutions;
(1.2.2) performing time domain band-pass filtering processing on the image with each spatial resolution to extract an interested frequency band;
(1.2.3) amplifying the filtering result, namely multiplying the signal of the interested frequency band by an amplification factor;
(1.2.4) adding the amplified images with different spatial resolutions and the corresponding original images, and synthesizing the images with different spatial resolutions to obtain a final image.
Further, the acquisition of the heart rate in step (1.6) includes calculation in the time domain and calculation in the frequency domain;
wherein, the calculation in the time domain: a method of calculating heart rate with reference to the PPG technique, since the IPPG technique is derived based on the PPG technique; designing a self-fitting quadratic curve which fits the variation trend between two peak values, so that the interference of the dicrotic wave can be avoided; repeating the above process to find all peak points;
calculation in the frequency domain: the frequency domain adopts a Fourier transform method to convert the IPPG signal into the frequency domain; according to medical knowledge, a frequency range of the heart rate is set, effective peak values in the frequency range are extracted and converted into a time domain, and a heart rate result is obtained.
Further, in step (1.2), a laplacian image sequence is generated during euler expansion, and the change of the pixel value of the edge part of the image sequence is stored in the laplacian image sequence, that is, the signal related to the breathing motion is stored in the edge of the expanded image sequence; and taking the time sequence signal corresponding to each edge point as a variable, and extracting a first principal component signal from all edge point signals by using principal component analysis, wherein the signal is a respiratory signal.
The invention has the beneficial effects that: the physiological parameter detection system is designed for non-contact physiological parameter detection; the method has the advantages of no contact with a tested part, simple and easy operation and the like, can solve the problem that patients with skin burn or limb deformity and infants are difficult to use contact instruments to measure physiological parameters, extracts the region of interest by facial segmentation, avoids the influence of blinking of eyes on signals, effectively removes motion errors by adopting a combined sparse spectrum reconstruction algorithm, improves the signals, and can be suitable for detecting various physiological parameters.
Drawings
FIG. 1 is a flow chart of the architecture of the present invention;
fig. 2 is a schematic diagram of region of interest selection in the present invention.
Detailed Description
In order to more clearly illustrate the technical solution of the present invention, the present invention will be further described below; obviously, the following description is only a part of the embodiments, and it is obvious for a person skilled in the art to apply the technical solutions of the present invention to other similar situations without creative efforts; in order to more clearly illustrate the technical solution of the present invention, the following detailed description is made with reference to the accompanying drawings:
as shown in the figure; a non-contact physiological parameter detection method based on IPPG comprises the following steps:
(1.1), recording video: the tested object is seated in front of the computer, the face of the tested object is aligned to the camera, and the camera carried by the computer is used for recording a video of one minute;
(1.2), Euler amplification: performing Euler amplification processing on the recorded video;
(1.3), region of interest selection: the method comprises the following steps of firstly obtaining the position of an eye region through face recognition, and then carrying out face segmentation on a face, wherein the segmentation step comprises the following steps:
(1.3.1) setting the length of the eye region as a and the width as b;
(1.3.2) selecting a face area with the length of a and the width of 1.5b below the face area as an interested area, and avoiding errors caused by inevitable physiological activities such as winking and the like;
(1.4) obtaining three-channel signals of the IPPG signal; calculating the gray average value of R, G, B three channels of the interesting region of each frame, performing linear combination operation on the three channels to make the signal closer to the real IPPG signal,
(1.5) elimination of motion artifacts: eliminating the motion artifact of the IPPG signal by adopting a joint sparse spectrum reconstruction algorithm; separating spectral regions that affect skin absorption; removing the spectral range with small influence factors;
(1.6), acquisition of heart rate: setting a frequency range of heart rate, extracting effective peak values in the frequency range, and converting the effective peak values into time domains to obtain heart rate values;
(1.7), acquisition of heart rate variability and respiration rate: respectively extracting 100 continuous RR interval data, performing phase space reconstruction on the RR interval data, constructing a probability density function curve, and distinguishing atrial fibrillation from normal sinus rhythm through a skewness coefficient of the probability density function curve and an abscissa position corresponding to a curve peak value so as to detect heart rate variability;
the results show that the method can rapidly detect heart rate variability by using 100 continuous pulse interval data to distinguish atrial fibrillation from normal sinus rhythm, wherein the accuracy rate is 0.94.
Further, in the euler amplification in the step (1.2), the brightness value of the pixel point in each detection image in the time sequence is analyzed, the low-frequency part is selected for amplification, and then the low-frequency part and the original image are superposed to synthesize a final image, so that weak change information is amplified.
Further, the euler amplification processing is performed on the recorded video in the step (1.2), and the euler amplification processing method includes the following steps:
(1.2.1) decomposing each frame image of the video signal into different spatial resolutions;
(1.2.2) performing time domain band-pass filtering processing on the image with each spatial resolution to extract an interested frequency band;
(1.2.3) amplifying the filtering result, namely multiplying the signal of the interested frequency band by an amplification factor;
(1.2.4) adding the amplified images with different spatial resolutions and the corresponding original images, and synthesizing the images with different spatial resolutions to obtain a final image.
Further, the acquisition of the heart rate in step (1.6) includes calculation in the time domain and calculation in the frequency domain;
wherein, the calculation in the time domain: a method of calculating heart rate with reference to the PPG technique, since the IPPG technique is derived based on the PPG technique; designing a self-fitting quadratic curve which fits the variation trend between two peak values, so that the interference of the dicrotic wave can be avoided; repeating the above process to find all peak points;
calculation in the frequency domain: the frequency domain adopts a Fourier transform method to convert the IPPG signal into the frequency domain; according to medical knowledge, a frequency range of the heart rate is set, effective peak values in the frequency range are extracted and converted into a time domain, and a heart rate result is obtained.
Further, in step (1.2), a laplacian image sequence is generated during euler expansion, and the change of the pixel value of the edge part of the image sequence is stored in the laplacian image sequence, that is, the signal related to the breathing motion is stored in the edge of the expanded image sequence; and taking the time sequence signal corresponding to each edge point as a variable, and extracting a first principal component signal from all edge point signals by using principal component analysis, wherein the signal is a respiratory signal.
The acquisition of the IPPG signal mainly comprises the steps of Euler amplification, region of interest selection, acquisition of an IPPG three-channel signal and the like.
Euler's amplification is to analyze the brightness value of the pixel point in each image in the time sequence, select the low frequency part to amplify, then superpose with the original image and synthesize the final image, thus amplify the weak change information.
The detected area can not completely cover the camera view, so that the shot video inevitably contains background objects irrelevant to calculation; the signal-to-noise ratio of the signal can be weakened due to the irregular change of the background objects, and the next calculation is influenced; therefore, the image should be segmented first, and only human tissue is reserved after the background environment is removed.
Because the volume of the blood vessel changes, the intensity of the reflected light on the skin surface of each part changes, so when the IPPG technology is used for measurement, the skin at any position can be selected theoretically; however, considering the amount of blood vessels, the convenience of measurement, the difficulty of identifying the measured part, and the like, all or part of the facial area of a person is generally selected to collect signals; the subject is to avoid errors caused by unavoidable physiological activities such as blinking by dividing a face region and selecting the face region below eyes as an interested region.
According to the light absorption curve and the three channel absorption curves of the CMOS camera, the light absorption overlapping part of the green channel and the skin is the largest, so that the signal with better signal-to-noise ratio compared with the blue channel and the red channel can be obtained by extracting the green channel IPPG signal; in order to enable the signal to be closer to the IPPG signal, linear operation is carried out on the three channels, the spectral interval influencing the skin absorption can be separated, and the spectral range with small influence factors is removed; the three channels are linearly combined to construct a new signal, so that other noise influences can be removed as far as possible.
The combined sparse spectrum reconstruction algorithm is applied to more strenuous physical exercise by a subject, the elimination effect of motion artifacts is better, and meanwhile, the algorithm can be better applied under the condition of lower sampling rate; therefore, the elimination of the motion artifact of the IPPG signal is carried out by adopting a joint sparse spectrum reconstruction algorithm.
Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of embodiments of the present invention; other variations are possible within the scope of the invention; thus, by way of example, and not limitation, alternative configurations of embodiments of the invention may be considered consistent with the teachings of the present invention; accordingly, the embodiments of the invention are not limited to the embodiments explicitly described and depicted.
Claims (5)
1. A non-contact physiological parameter detection method based on IPPG is characterized by comprising the following steps:
(1.1), recording video: the tested object is seated in front of the computer, the face of the tested object is aligned to the camera, and the camera carried by the computer is used for recording a video of one minute;
(1.2), Euler amplification: performing Euler amplification processing on the recorded video;
(1.3), region of interest selection: the method comprises the following steps of firstly obtaining the position of an eye region through face recognition, and then carrying out face segmentation on a face, wherein the segmentation step comprises the following steps:
(1.3.1) setting the length of the eye region as a and the width as b;
(1.3.2) selecting a face area with the length a and the width 1.5b below the face area as an interested area;
(1.4) obtaining three-channel signals of the IPPG signal; calculating the gray average value of R, G, B three channels of the interesting region of each frame, performing linear combination operation on the three channels to make the signal closer to the real IPPG signal,
(1.5) elimination of motion artifacts: eliminating the motion artifact of the IPPG signal by adopting a joint sparse spectrum reconstruction algorithm; separating spectral regions that affect skin absorption;
(1.6), acquisition of heart rate: setting a frequency range of heart rate, extracting effective peak values in the frequency range, and converting the effective peak values into time domains to obtain heart rate values;
(1.7), acquisition of heart rate variability and respiration rate: and respectively extracting 100 continuous RR interval data, performing phase space reconstruction on the RR interval data, constructing a probability density function curve, and distinguishing atrial fibrillation from normal sinus rhythm through the skewness coefficient of the probability density function curve and the abscissa position corresponding to the peak value of the curve so as to detect the heart rate variability.
2. The IPPG-based non-contact physiological parameter detection method according to claim 1, wherein in the euler's expansion in step (1.2), the brightness value of the pixel point in each detected image in the time series is analyzed, the low frequency part is selected for expansion, and then the low frequency part is overlapped with the original image to synthesize the final image, so as to expand the weak variation information.
3. The IPPG-based non-contact physiological parameter detection method according to claims 1 and 2, wherein the euler expansion process is performed on the recorded video in step (1.2), which comprises the following steps:
(1.2.1) decomposing each frame image of the video signal into different spatial resolutions;
(1.2.2) performing time domain band-pass filtering processing on the image with each spatial resolution to extract an interested frequency band;
(1.2.3) amplifying the filtering result, namely multiplying the signal of the interested frequency band by an amplification factor;
(1.2.4) adding the amplified images with different spatial resolutions and the corresponding original images, and synthesizing the images with different spatial resolutions to obtain a final image.
4. The IPPG-based non-contact physiological parameter detection method according to claim 1, wherein the heart rate acquisition in step (1.6) comprises a calculation in the time domain and a calculation in the frequency domain;
wherein, the calculation in the time domain: designing a self-fitting quadratic curve, fitting the curve with the variation trend between two peak values, repeating the process, and finding all peak value points;
calculation in the frequency domain: the frequency domain adopts a Fourier transform method to convert the IPPG signal into the frequency domain; setting a frequency range of the heart rate, extracting effective peak values in the frequency range, and converting the effective peak values into a time domain to obtain a heart rate result.
5. The IPPG-based non-contact physiological parameter detection method according to claim 1, wherein in step (1.2), a laplacian image sequence is generated during euler expansion, and the changes of the pixel values of the edge portion of the image sequence are stored in the laplacian image sequence, i.e. the signals related to respiratory motion are stored in the edge of the expanded image sequence; and taking the time sequence signal corresponding to each edge point as a variable, and extracting a first principal component signal from all edge point signals by using principal component analysis, wherein the signal is a respiratory signal.
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