CN111887824A - Arteriosclerosis detection device based on millimeter waves and neural network - Google Patents

Arteriosclerosis detection device based on millimeter waves and neural network Download PDF

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CN111887824A
CN111887824A CN202010751557.4A CN202010751557A CN111887824A CN 111887824 A CN111887824 A CN 111887824A CN 202010751557 A CN202010751557 A CN 202010751557A CN 111887824 A CN111887824 A CN 111887824A
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沈皓哲
李文钧
岳克强
李宇航
王超
陈石
张汝林
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Hangzhou Dianzi University
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Abstract

The invention discloses an arteriosclerosis detecting device based on millimeter waves and a neural network, which comprises: the device comprises a millimeter wave detection device, a preprocessing module, a feature extraction module and a CNN neural network model, wherein the preprocessing module is respectively connected with the millimeter wave detection device and the feature extraction module, and the feature extraction module is connected with the CNN neural network model; the millimeter wave detection device is used for collecting pulse waves of a user; the preprocessing module is used for preprocessing the pulse wave; the feature extraction module comprises a feature point extraction module and a feature parameter calculation module and is used for extracting features of the preprocessed pulse waves; and the CNN neural network model is used for training and classifying according to the extracted characteristic parameters.

Description

Arteriosclerosis detection device based on millimeter waves and neural network
Technical Field
The invention relates to the field of cardiovascular disease detection and judgment, in particular to an arteriosclerosis detection device based on millimeter waves and a neural network.
Background
Arteriosclerosis is a non-inflammatory lesion of an artery, and can thicken and harden the wall of an artery, lose elasticity and narrow a lumen. Arteriosclerosis is classified into various types, such as cerebral infarction in the brain, transient cerebral ischemia, angina pectoris in the heart, renal dysfunction in the kidney, and intermittent claudication in the peripheral blood vessels. Cerebral infarction, myocardial infarction and gangrene can occur in severe cases. Arteriosclerosis is a systemic disease, all known as atherosclerotic disease, and its harm is to affect the whole body, including cerebral vessels, cardiovascular vessels, renal vessels, peripheral vessels of brain, and even vessels to the fundus. Arteriosclerosis has become a next very common disease, and appears with the increase of age, and the rule is that the arteriosclerosis usually occurs in teenagers and aggravates and attacks the diseases in the middle-aged and old. More men than women are in the group of the disease, and in recent years, arteriosclerosis is gradually increased in China and becomes one of the main causes of death of the old. Therefore, early detection, early prevention, becomes critical away from arteriosclerosis.
With the continuous progress of scientific technology, the measurement of arteriosclerosis is accumulated for many years, and various methods are verified and discovered. The method is divided into two modes of invasive and non-invasive, wherein the invasive method comprises an angiography method and an intravascular ultrasound technology. Non-invasive methods fall into two broad categories: direct measurement and indirect measurement. The direct measurement uses magnetic resonance imaging, CT scanning, color doppler, etc. and the indirect measurement determines the arteriosclerosis degree by measuring the conduction velocity of the pulse wave, which is mainly divided into two modes: cervical-femoral pulse wave, brachial-ankle pulse wave. The main measuring instrument of the pulse wave is VP series of Japan ohm Longkolin, and the market share is higher in the world at present. Another instrument for measuring the velocity of the cervical-femoral pulse wave is the Complior detection system, available from French corporation. However, most of the above detection means are very complicated and the equipment is expensive.
Disclosure of Invention
In order to solve the defects of the prior art, realize accurate measurement, reduce complicated detection means and reduce cost, the invention adopts the following technical scheme:
an arteriosclerosis detecting device based on millimeter wave and neural network, comprising: the device comprises a millimeter wave detection device, a preprocessing module, a feature extraction module and a CNN neural network model, wherein the preprocessing module is respectively connected with the millimeter wave detection device and the feature extraction module, and the feature extraction module is connected with the CNN neural network model;
the millimeter wave detection device is used for collecting pulse waves of a user;
the preprocessing module is used for preprocessing the pulse wave;
the characteristic extraction module comprises a characteristic point extraction module and a characteristic parameter calculation module and is used for extracting the characteristics of the preprocessed pulse wave, the characteristic points comprise an initial point, a main wave peak point, a wave valley point of a pre-counterpulsation wave, a wave peak point of a pre-counterpulsation wave, a wave valley point of a counterpulsation wave and a wave peak point of a counterpulsation wave, the characteristic parameters comprise the relative height and the K value of the wave peak of the counterpulsation wave, K is (Pm-Pd)/(Ps-Pd), Ps represents the systolic pressure, Pd represents the diastolic pressure, Pm represents the average pressure, the characteristic parameter calculation module calculates the characteristic parameters through the characteristic points extracted by the characteristic extraction module, the relative height of the dicrotic wave peak can reflect arterial compliance, and is an index of the degree of arteriosclerosis, the larger the relative height of the dicrotic wave peak is, the better the arterial compliance is, and the K value can well reflect indexes of elasticity of a blood vessel wall, resistance of peripheral blood vessels and the like of a human body;
and the CNN neural network model is used for training and classifying according to the extracted characteristic parameters.
The method comprises the steps of acquiring pulse wave data of a user by using a millimeter wave detection device, preprocessing the pulse wave data, extracting features of the preprocessed data, using the extracted features as input of a CNN neural network model, training a large amount of effective data through the CNN neural network model to obtain the trained CNN neural network model, classifying normal pulse and arteriosclerosis pulse through the trained CNN neural network model, and judging whether the user suffers from arteriosclerosis or not according to the pulse wave detection condition of the user.
The millimeter wave detection device comprises a millimeter wave transmitting and receiving device, and can be used for carrying out non-contact signal acquisition and acquiring pulse beating analog signals.
The preprocessing module comprises a filtering processing module and a low-noise amplifying circuit, the filtering processing module is used for filtering out clutter, and the low-noise amplifying circuit is used for amplifying pulse wave signals.
The CNN neural network adopts an EfficientNeT neural network, and the model accuracy rate formula is as follows:
Figure BDA0002610154140000021
Figure BDA0002610154140000022
Figure BDA0002610154140000023
Figure BDA0002610154140000024
Figure BDA0002610154140000025
representing the entire convolutional network, w represents the size of the convolutional kernel, d represents the neural network depth, r represents the resolution, i represents the ith convolutional layer, 1 … s represents the signals of a plurality of convolutional layers with the same structure, FiDenotes the convolution operation on the i-th layer, LiIs represented by FiIn the ith plurality of convolution layers of the same structure, there is LiA convolution layer of uniform structure, XiRepresenting the input tensor, Hi、Wi、CiRespectively represent the dimensions of the input of the ith layer,
Figure BDA0002610154140000026
representing a custom operator, achieves the best model accuracy by optimizing the parameters d, w, and r.
The EfficientNeT neural network uses a composite coefficient
Figure BDA0002610154140000027
And (3) carrying out compound adjustment on the parameters of d, w and r:
Figure BDA0002610154140000028
Figure BDA0002610154140000029
Figure BDA00026101541400000210
s.t.α*β2*γ2≈2(α≥1，β≥1，γ≥1)
alpha, beta and gamma are constants, the optimal alpha, beta and gamma are determined through the adjustment of the basic network, and the reference network is expanded through the optimal alpha, beta and gamma, so that the expanded network also has higher accuracy and efficiency.
The invention has the advantages and beneficial effects that:
through the technique that millimeter wave and deep learning combined together for arteriosclerosis detection device improves the convenience of using when guaranteeing to measure the accuracy, reduces product cost, reduction measurement cost.
Drawings
Fig. 1 is a schematic structural view of the present invention.
Fig. 2a is a diagram of a baseline network in accordance with the present invention.
FIG. 2b is a diagram of an expanded network for increasing the receptive field in the present invention.
Fig. 2c is an extended network diagram of increasing network depth in the present invention.
Fig. 2d is a diagram of an extended network for increased resolution in the present invention.
Fig. 2e is a diagram of a hybrid parameter extended network in accordance with the present invention.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present invention, are given by way of illustration and explanation only, not limitation.
As shown in fig. 1, an arteriosclerosis detecting device based on millimeter waves and a neural network includes a millimeter wave detecting device, a preprocessing module, a feature extraction module, a CNN neural network model, and an output module.
The millimeter wave detection device is used for collecting the pulse waves of a user in a time period, and non-contact signal collection is carried out through the millimeter wave transmitting and receiving device to obtain pulse beating analog signals.
The preprocessing module preprocesses the pulse wave, filters the acquired analog signal, filters out clutter, and amplifies the signal by using a low-noise amplifying circuit.
The feature extraction module performs feature extraction on the processed pulse waves, extracts six feature points of the pulse waves and selects feature parameters. The characteristic points of the pulse wave include: the initiation point, i.e. the point at which the aortic valve opens; a main wave peak point, namely a maximum amplitude point; the wave valley point of the pre-dicrotic wave, namely the point when the pressure of the aorta expansion begins to drop; the peak point of the wave before dicrotic beat, i.e. the beginning of the relaxation of the left ventricle; the wave valley point of the counterpulsation wave, namely the starting point of the counterpulsation wave; the peak point of the dicrotic wave, i.e. the point at which the dicrotic wave reaches maximum pressure. Calculating the relative height (f/H) and K value of the peak of the dicrotic wave as characteristic parameters through six characteristic points, wherein the relative height of the peak of the dicrotic wave reflects the compliance of the artery and is an index of the degree of angiosclerosis, and the larger the f/H is, the better the compliance of the artery is; k is (Pm-Pd)/(Ps-Pd), Ps and Pd are systolic and diastolic pressures, respectively, and Pm is the mean pressure. The K value can well reflect indexes of elasticity of the vessel wall, resistance of peripheral vessels and the like of a human body.
The CNN neural network is trained by the extracted features, and the EfficientNeT neural network is run by using a TensorFlow framework. The EfficientNet utilizes the compound coefficient to uniformly scale all dimensions of the model, and consists of Stem +16 Blocks + Con2D + GlobavalagePooling 2D + Dense in total, so that the effect of highest precision and highest efficiency is achieved. The method comprises three coefficients of w, d and r, wherein w represents the size of a convolution kernel and determines the size of a receptive field; d represents the neural network depth; r represents the resolution size. As shown in fig. 2, fig. 2a is a base network, which may also be understood as a small network; FIG. 2b is a manner of expanding the network to increase the receptive field; FIG. 2c is a manner of extending the network in a manner that increases the depth of the network; FIG. 2d is a manner of increasing resolution to expand the network; FIG. 2e is a hybrid parameter expansion approach; the efficiency of model scaling relies heavily on the baseline network, which is developed using a network structure search and then scaled to obtain a series of models called EfficientNets.
The mathematical formula of the neural network is as follows:
Figure BDA0002610154140000041
Figure BDA0002610154140000042
Figure BDA0002610154140000043
Figure BDA0002610154140000044
wherein: xiIs the input tensor so that,
Figure BDA0002610154140000045
representing the entire convolutional network, i represents the ith convolutional layer, 1 … s represents stage (multiple convolutional layers of the same structure)Signal, FiDenotes the convolution operation on the i-th layer, LiIs represented by FiIn the ith stage, there is LiA convolution layer of uniform structure Hi、Wi、CiRespectively represent the dimension of the i-th layer input, where Hi、WiRepresents the convolution kernel size, C, of the ith convolution layeriIndicates the number of channels in the ith convolutional layer,
Figure BDA0002610154140000046
is a custom operator that can be defined as | _ indicating a multiply-by-multiply operation. The formula shows how the parameters d, w and r are optimized to achieve the best model accuracy.
The normalized composite parameter adjusting method of EfficientNet uses a composite coefficient
Figure BDA0002610154140000047
To make composite adjustments to the d, w and r parameters.
Figure BDA0002610154140000048
Figure BDA0002610154140000049
Figure BDA00026101541400000410
s.t.α*β2*γ2≈2(α≥1，β≥1，γ≥1)
Wherein alpha, beta and gamma are constants which can be obtained by grid search, and
Figure BDA00026101541400000411
optimization by manual adjustment is required. The optimization method comprises the following steps: in the first step, the optimal alpha, beta and gamma can be adjusted and determined through a base network, and then the parameter is used for expanding or amplifying the base network into a large network, so that the large network also has higher accuracy and efficiency. Incorporating the selected features intoThe number is used as input to train the model.
The classification and identification of normal pulse waves and pulse waves with arteriosclerosis are completed through a trained model, the pulse waves of a detected user are subjected to preprocessing such as noise reduction and amplification, characteristic parameters are extracted, the characteristic parameters are used as network input, and whether the user has the arteriosclerosis or not is identified according to the model classification.
The method comprises the steps of acquiring pulse wave data of a user by using a millimeter wave detection device, preprocessing the pulse wave data, extracting features of the preprocessed data, using the extracted features as input of a CNN neural network model, training a large amount of effective data through the CNN neural network model to obtain the trained CNN neural network model, classifying normal pulse and arteriosclerosis pulse through the trained CNN neural network model, and judging whether the user suffers from arteriosclerosis or not according to the pulse wave detection condition of the user.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims (5)

1. An arteriosclerosis detecting device based on millimeter wave and neural network, characterized by comprising: the device comprises a millimeter wave detection device, a preprocessing module, a feature extraction module and a CNN neural network model, wherein the preprocessing module is respectively connected with the millimeter wave detection device and the feature extraction module, and the feature extraction module is connected with the CNN neural network model;
the millimeter wave detection device is used for collecting pulse waves of a user;
the preprocessing module is used for preprocessing the pulse wave;
the feature extraction module comprises a feature point extraction module and a feature parameter calculation module, and is used for performing feature extraction on the preprocessed pulse wave, wherein the feature point comprises an initial point, a main peak point, a wave valley point of a pre-counterpulsation wave, a wave peak point of a pre-counterpulsation wave, a wave valley point of a counterpulsation wave and a wave peak point of a counterpulsation wave, the feature parameter comprises the relative height and the K value of the wave peak of the counterpulsation wave, K is (Pm-Pd)/(Ps-Pd), Ps represents the systolic pressure, Pd represents the diastolic pressure, Pm represents the average pressure, and the feature parameter calculation module calculates the feature parameter through the feature point extracted by the feature extraction module;
and the CNN neural network model is used for training and classifying according to the extracted characteristic parameters.
2. The arteriosclerosis detecting device based on millimeter wave and neural network as claimed in claim 1, wherein the millimeter wave detecting device comprises millimeter wave transmitting and receiving device.
3. The arteriosclerosis detecting device according to claim 1, wherein the preprocessing module comprises a filtering processing module and a low noise amplifying circuit.
4. The arteriosclerosis detecting device based on millimeter wave and neural network as claimed in claim 1, wherein the CNN neural network adopts EfficientNeT neural network, and the model accuracy formula is as follows:
Figure FDA0002610154130000011
Figure FDA0002610154130000012
Figure FDA0002610154130000013
Figure FDA0002610154130000014
Figure FDA0002610154130000018
representing the entire convolutional network, w represents the size of the convolutional kernel, d represents the neural network depth, r represents the resolution, i represents the ith convolutional layer, 1 … s represents the signals of a plurality of convolutional layers with the same structure, FiDenotes the convolution operation on the i-th layer, LiIs represented by FiIn the ith plurality of convolution layers of the same structure, there is LiA convolution layer of uniform structure, XiRepresenting the input tensor, Hi、Wi、CiRespectively represent the dimensions of the input of the ith layer,
Figure FDA0002610154130000019
representing a custom operator.
5. The arteriosclerosis detecting device as claimed in claim 4, wherein the EfficientNeT neural network uses complex coefficient
Figure FDA00026101541300000110
And (3) carrying out compound adjustment on the parameters of d, w and r:
depth:
Figure FDA0002610154130000015
width:
Figure FDA0002610154130000016
resoluton:
Figure FDA0002610154130000017
s.t.α*β2*γ2≈2(α≥1，β≥1，γ≥1)
alpha, beta and gamma are constants, optimal alpha, beta and gamma are determined through base line network adjustment, and the reference network is expanded through the optimal alpha, beta and gamma.
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CN114642409A (en) * 2022-01-19 2022-06-21 北京邮电大学 Human body pulse wave sensing, heart rate monitoring and blood pressure monitoring method and related device
CN114642409B (en) * 2022-01-19 2022-10-18 北京邮电大学 Human body pulse wave sensing method, heart rate monitoring method and blood pressure monitoring device

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Application publication date: 20201106