CN115381444A - Rapid detection method for blood oxygen saturation - Google Patents
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- 238000001514 detection method Methods 0.000 title claims abstract description 21
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Abstract
The invention relates to a rapid detection method of blood oxygen saturation, which comprises the steps that terminal equipment shoots lip images of a detector (without a whole face image), image preprocessing is carried out on the basis of a multi-scale retina enhancement (MSRCR) algorithm with color recovery, a built-in blood oxygen saturation model is sent into the terminal equipment, the lip color is identified by the blood oxygen saturation model, and the currently predicted blood oxygen saturation of the detector is output and is used for monitoring the home blood oxygen saturation in real time.
Description
Technical Field
The invention relates to a detection technology, in particular to a rapid detection method for blood oxygen saturation.
Background
The intelligent terminal is more and more widely applied to health monitoring. Compared with the existing clinical monitoring means, the intelligent terminal does not need additional detection equipment, and is more portable, real-time and efficient. Greatly meets the requirement of monitoring the health of people at home, and provides an effective method for early detection and early diagnosis of diseases.
The blood oxygen saturation is an important physiological parameter of the respiratory cycle of a human body, and can directly reflect the oxygenation of the lung and the oxygen carrying capacity of hemoglobin in blood. Hypoxia directly affects the aerobic metabolism of tissues, thereby causing organ dysfunction and even failure. Hypoxia is present in diseases such as chronic obstructive pulmonary disease and some congenital heart diseases. At present, the blood oxygen saturation is mainly monitored clinically through a pulse oxygen saturation instrument and blood gas analysis. Both require specialized instrumentation. The former is portable, but has 2-3% measuring error under the influence of factors such as skin pigment, bilirubin and the like; the latter is the 'gold standard' for monitoring the blood oxygen saturation, but needs invasive blood sampling, and applies a specific blood gas analyzer to analyze data, so that the latter cannot be widely applied to monitoring of people at home.
Disclosure of Invention
Aiming at the problem of monitoring the blood oxygen saturation in real time, a rapid detection method for the blood oxygen saturation is provided.
The technical scheme of the invention is as follows: a method for quickly detecting the blood oxygen saturation includes such steps as taking the image of lip of person to be detected by terminal, sending it to the internal blood oxygen saturation model, recognizing the lip color by said model, and outputting the current blood oxygen saturation value of person to be detected for real-time monitoring the blood oxygen saturation at home.
Further, the blood oxygen saturation model training method comprises the following steps:
1) Data acquisition: acquiring a lip image of a subject, and simultaneously monitoring the blood oxygen saturation of the subject by using an oximeter; the subject loads the lip image and the corresponding oximeter display blood oxygen saturation value as image data;
2) Data processing: performing brightness correction on the lip image of the collected subject, extracting a multi-channel color mean value of the image according to a preset ROI, and constructing an image data set;
3) Constructing a blood oxygen saturation model based on a multi-channel color space, facilitating the image data set in the step 2) and the blood oxygen saturation value corresponding to the image in the step 1), training the blood oxygen saturation model, and predicting the blood oxygen saturation value corresponding to the lip color;
4) In the model training process, a data set is divided into a 60% training set and a 40% testing set, and the trained blood oxygen saturation model is verified by the testing set to obtain the final trained blood oxygen saturation model.
Further, the loss function of the blood oxygen saturation model:
wherein n is the number of lip image features; y represents the vector of the monitoring result of the oximeter, X is a lip image feature matrix, and w is the weight vector of the feature matrix.
Further, lip image lightness correction in the data processing: the image brightness equalization correction is completed by adopting an MCRCR algorithm and designing a multi-scale Gaussian filter and an image overall brightness difference correction algorithm, wherein the Gaussian function is as follows:
x and y represent coordinates of each pixel point, C is a normalization constant, the width of the Gaussian filter is represented by a parameter sigma, the larger sigma is, the larger the filtering action range is, in order to give consideration to dynamic range compression and local feature detail extraction, the values of multi-scale Gaussian filter sigma are respectively 20, 72 and 250, and the filtering results corresponding to the sigma values under each scale are subjected to equal weight averaging to obtain the final correction result.
Further, the multichannel color mean in the data processing comprises RGB and HSV dual-color mean.
Further, the blood oxygen saturation model is described as follows:
SpO 2(result) ＝W 1 C 1 +W 2 C 2 +...+W n C n +β，
wherein, W 1 …W n Representing the trained blood oxygen saturation model weight, beta is the trained blood oxygen saturation model bias term, C 1 …C n For two-way image of lips of a subjectThe trace color data is combined to form a feature vector.
The invention has the beneficial effects that: the invention relates to a rapid detection method of blood oxygen saturation, which acquires a lip image instead of a whole face image. Under the condition of blood oxygen saturation change, particularly under the condition of oxygen deficiency, the color change of lips is more sensitive and remarkable; the operation method of machine learning is different from the RGB image algorithm of the prior patent, and can eliminate the difference of RGB colors of images under different light rays and different photographing environments, thereby realizing the accurate detection of the blood oxygen saturation; the method can be applied to all intelligent terminal equipment with cameras or with a picture reading function, and the rapid detection of the blood oxygen saturation can be realized through the APP.
Drawings
FIG. 1 is a flow chart of an implementation of the rapid detection method of blood oxygen saturation according to the present invention;
FIG. 2 is a comparison graph of the MCRCR algorithm for correcting the brightness of an image according to the method of the present invention;
FIG. 3 is an APP analysis interface diagram of the intelligent terminal of the present invention;
FIG. 4 is a comparison graph of the detection and oximeter of the instant invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
The color of the skin and mucous membranes changes with the color of the blood stream. When the reduced hemoglobin in the blood is >3mg/L, the skin and mucous membrane are changed in bluish purple, i.e. cyanosis. Cyanosis can occur in the whole skin and mucous membrane, but it is more pronounced in thin skin, less pigmented and capillary-rich areas such as lips and tongue.
A rapid detection method for blood oxygen saturation comprises the steps that terminal equipment shoots lip images of a detector, sends the lip images into a built-in blood oxygen saturation model of the detector, and outputs a current blood oxygen saturation value of the detector for monitoring the blood oxygen saturation of a house in real time. The blood oxygen saturation degree model training method specifically comprises the following steps:
1. data acquisition: acquiring lip images of a subject and simultaneously monitoring the blood oxygen saturation of the subject by using an oximeter; the lip image of the subject is obtained by a device with a photographing and shooting function, or the sample image data is transmitted by a medium and loaded in a local database. Oximeters are used to monitor the oxygen saturation of a subject's blood.
2. Algorithm (MSRCR) based image brightness correction; since home or clinical monitoring of blood oxygen saturation is usually performed in different environments, this will cause instability of the acquired lip image, thereby interfering with the monitoring result. Therefore, on the premise of keeping the image undistorted, based on the MCRCR algorithm, the image brightness equalization correction is completed by designing the multi-scale gaussian filter and the image overall brightness difference correction algorithm, as shown in the image brightness correction contrast diagram in fig. 2. The gaussian function used is as follows:
wherein x and y represent coordinates of each pixel point, C is a normalization constant, the width of the gaussian filter is represented by a parameter σ, and the larger σ is, the larger the filtering action range is, so that in order to give consideration to dynamic range compression and local feature detail extraction, the values of the multi-scale gaussian filter σ are respectively 20, 72 and 250, and the filtering results corresponding to the σ values under each scale are subjected to equal weight averaging, namely, the final correction result is obtained.
3. Extracting an image color mean value according to a preset ROI; the method supports the preset ROI size, and automatically extracts the multichannel color mean value (including RGB and HSV) by the program background based on the image after brightness correction.
4. Constructing a blood oxygen saturation degree model and a data set based on a multi-channel color space; because each channel of the RGB color space is related to the wavelength of reflected light of a substance, when the color of the lips changes due to oxygen saturation, the RGB channel color value of the lip image changes along with the color of the lips, and therefore the RGB channel color value has important significance as a training feature; in the HSV space, H visually defines the hue, so that the lip color change range can be better excavated and limited. Therefore, the RGB and HSV dual-color spaces are combined, and the data set and the blood oxygen saturation model are constructed based on the feature combination of the dual-color spaces and the detection value of the oximeter.
The machine learning algorithm integrated in the intelligent terminal is described as follows:
SpO 2(result) ＝W 1 C 1 +W 2 C 2 +...+W n C n +β，
wherein, W 1 …W n Representing the trained blood oxygen saturation model weight, beta is the trained blood oxygen saturation model bias term, C 1 …C n Is a feature vector composed of a two-channel color data combination of the lip images of the subject.
5. Training and applying the blood oxygen saturation model; in the model training process, the data set is divided into a 60% training set and a 40% testing set, and the blood oxygen saturation model is trained by optimizing the following loss function:
wherein n is the number of the lip image features; y represents the vector of the monitoring result of the oximeter, X is a lip image feature matrix, and w is the weight vector of the feature matrix.
After training, the model is deployed in the intelligent terminal APP to conveniently complete rapid oxyhemoglobin saturation monitoring. Therefore, after the lip image is shot, the background of the program automatically finishes brightness correction and extracts color signals, and finally, the feature vector is automatically reconstructed and input to the model to finish monitoring. The whole process can be automatically completed in the intelligent terminal APP. The APP analysis interface diagram of the intelligent terminal is shown in FIG. 3.
Blood oxygen saturation (SaO) 2 ) Is oxyhemoglobin (HbO) bound by oxygen in blood 2 ) The percentage of total bindable hemoglobin capacity. The method shown in fig. 4 is used for detecting blood oxygen saturation by comparing the color of lip or cyanosis with the oximeter detection chart obtained by trained machine learning modelThe consistency is very high when the comparison is carried out. The model trained by the invention is combined with the terminal to realize the home real-time monitoring of the blood oxygen saturation and effectively evaluate the state of illness of congenital heart disease.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that various changes and modifications can be made by those skilled in the art without departing from the spirit of the invention, and these changes and modifications are all within the scope of the invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (6)
1. A rapid detection method for blood oxygen saturation is characterized in that a terminal device shoots a lip image of a detector, the lip image is sent to a built-in blood oxygen saturation model of the detector, the blood oxygen saturation model identifies lip color, and a current blood oxygen saturation value of the detector is output and used for monitoring the blood oxygen saturation of a house in real time.
2. The rapid detection method of blood oxygen saturation according to claim 1, characterized in that the training method of blood oxygen saturation model is as follows:
1) Data acquisition: acquiring a lip image of a subject, and simultaneously monitoring the blood oxygen saturation of the subject by using an oximeter; the subject loads the lip image and the corresponding oximeter display blood oxygen saturation value as image data;
2) Data processing: performing brightness correction on the lip image of the collected subject, extracting a multi-channel color mean value of the image according to a preset ROI, and constructing an image data set;
3) Constructing a blood oxygen saturation model based on a multi-channel color space, facilitating the image data set in the step 2) and the blood oxygen saturation value corresponding to the image in the step 1), training the blood oxygen saturation model, and predicting the blood oxygen saturation value corresponding to the lip color;
4) In the model training process, a data set is divided into a 60% training set and a 40% testing set, and the trained blood oxygen saturation degree model is verified by the testing set to obtain the final trained blood oxygen saturation degree model.
3. The rapid detection method of blood oxygen saturation according to claim 2, characterized in that the loss function of the blood oxygen saturation model is:
wherein n is the number of lip image features; y represents the vector of the monitoring result of the oximeter, X is a lip image feature matrix, and w is the weight vector of the feature matrix.
4. The method of rapid detection of blood oxygen saturation according to claim 2 or 3, characterized in that lip image brightness correction in the data processing: the image brightness equalization correction is completed by adopting an MCRCR algorithm and designing a multi-scale Gaussian filter and an image overall brightness difference correction algorithm, wherein the Gaussian function is as follows:
wherein x and y represent the coordinates of each pixel point, C is a normalization constant, the width of the Gaussian filter is represented by a parameter sigma, the larger sigma is, the larger the filtering action range is, in order to give consideration to dynamic range compression and local feature detail extraction, the values of multi-scale Gaussian filter sigma are respectively 20, 72 and 250, and the filtering results corresponding to the sigma values under each scale are subjected to equal weight averaging, namely, the final correction result is obtained.
5. The method for rapidly detecting blood oxygen saturation degree based on machine learning according to claim 4, characterized in that the multichannel color mean value in data processing includes RGB and HSV two-color mean values.
6. The machine learning-based rapid detection method of blood oxygen saturation according to claim 5, characterized in that the blood oxygen saturation model is described as follows:
SpO 2(result) ＝W 1 C 1 +W 2 C 2 +...+W n C n +β，
wherein, W 1 …W n Representing the trained blood oxygen saturation model weight, beta is the trained blood oxygen saturation model bias term, C 1 …C n Is a feature vector formed by combining two channels of color data of the lip image of the subject.
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| CN116758069B (en) * | 2023-08-17 | 2023-11-14 | 济南宝林信息技术有限公司 | Medical image enhancement method for intestinal endoscope |
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