阅读说明：本技术 一种基于卷积神经网络的二便无损检测装置 (Portable nondestructive testing device based on convolutional neural network ) 是由 张铁 黄泽铨 于 2021-10-15 设计创作，主要内容包括：本发明公开了一种基于卷积神经网络的二便无损检测装置。本发明通过获取多个受试者的实时生理数据和二便标签；对获取的生理数据进行预处理,将预处理后的生理数据组成一个二维数据集,将二维数据集分为训练集和测试集；将训练集和与训练集中的生理数据对应的二便标签输入到卷积神经网络中训练二便预警模型,并通过测试集选择预测效果最佳的二便预警模型,确定最终二便预测模型；将数据滤波算法和最终二便预测模型移植到可嵌入式电脑子系统中,对人体是否产生便意进行实时预测。本发明通过传感器进行病人的生理数据采集,解决了现有采集生理数据时均为有创监测的问题,并实时对当前信号进行分析处理。(The invention discloses a portable nondestructive testing device based on a convolutional neural network. The invention obtains real-time physiological data and stool labels of a plurality of subjects; preprocessing the acquired physiological data, forming a two-dimensional data set by the preprocessed physiological data, and dividing the two-dimensional data set into a training set and a testing set; inputting the training set and the shit labels corresponding to the physiological data in the training set into a convolutional neural network to train a shit early warning model, selecting the shit early warning model with the best prediction effect through the test set, and determining a final shit prediction model; and transplanting the data filtering algorithm and the final stool prediction model into an embeddable computer subsystem to predict whether the human body generates stool or not in real time. The invention collects the physiological data of the patient through the sensor, solves the problem that invasive monitoring is carried out when the physiological data is collected at present, and analyzes and processes the current signal in real time.)

1. A stool nondestructive testing device based on a convolutional neural network is characterized by comprising a signal acquisition module, a signal preprocessing module, a stool prediction model training module and an embeddable computer subsystem;

the signal acquisition module acquires physiological signal data of a patient in real time and inputs the physiological signal data into the signal preprocessing module for preprocessing, the signal preprocessing module inputs the preprocessed physiological signal data into the binary prediction model training module, a final binary prediction model is obtained through training and is input into the embeddable computer subsystem, a data filtering algorithm and a final binary prediction model are transplanted on the embeddable computer subsystem, the physiological signal data sent by the signal acquisition module are received in real time, the physiological signal data are preprocessed through the data filtering algorithm and then input into the final binary prediction model for prediction, and a binary early warning signal is obtained.

2. The convolutional neural network-based nondestructive testing apparatus according to claim 1, wherein the signal acquisition module comprises:

the electronic stethoscope is used for measuring the bowel sounding signals of the patient; the electronic stethoscope comprises a stethoscope head, a sound guide tube and an ear hook;

a gastric electrical sensor for measuring a gastric electrical signal of the patient; the stomach electric sensor comprises a measuring electrode and a transmission interface;

the electrocardio sensor is used for measuring electrocardiosignals of a patient; the electrocardio sensor comprises a measuring electrode and a transmission interface;

the physiological signal data comprises intestinal tract bowel sounding signals, stomach electric signals and electrocardiosignals.

3. The nondestructive testing device of claim 2, wherein the real-time physiological signal data is collected in the morning, the noon and the evening, and the real-time physiological signal data is collected according to different situations, including fasting, non-fasting, stool and stool;

the real-time physiological signal data acquisition process is as follows:

intestinal borygmus signals, namely, tightly attaching an auscultation head to the skin, and respectively collecting the borygmus signals from the upper right quadrant, the upper left quadrant and the lower left quadrant of the abdomen of a subject;

the electrocardiosignal, the measuring electrode is clung to the chest part of the testee and collects the electrocardiosignal;

and (3) a stomach electric signal, namely tightly attaching a measuring electrode to the stomach of the subject and collecting the stomach electric signal.

4. The convolutional neural network-based nondestructive testing device of claim 1, wherein in the signal preprocessing module, the physiological signal data acquired by the signal acquisition module is preprocessed through a data filtering algorithm, including noise reduction, filtering and normalization, and specifically the following steps are performed:

adopting wavelet to reduce noise of physiological signal data, selecting wavelet function to decompose physiological signal data, and setting the original signal expression of physiological signal data as:

wherein the content of the first and second substances,to representSignalThe time in (1) is (a),by a set of functionsExpanding, namely representing a space formed by the piecewise constant functions of which all the discontinuous points are only in the integer set;is a non-negative integer representing spaceThe highest resolution of (c);is a set of integersThe elements (A) and (B) in (B),is a signalDecomposed in spaceTo (1) aWavelet packet coefficients;representing signalsIn spaceMapping of (2);

is a scale function defined as:

is thatFor short, decompose into:then, there are:

thenCan be expressed as:

wherein the content of the first and second substances,is a signalDecomposed in space2 ndWavelet packet coefficients;is a signalDecomposed in spaceTo (1) aWavelet packet coefficients;is formed byStretching the linear combination of (1);representing signalsIs/are as followsA component;representing signalsIs/are as followsA component;

is a wavelet function expressed as:

the decomposition process being followed bySubstitutionContinuing handleIs decomposed intoObtaining:

；

method for constructing FIR filter by adopting window function design, L-order FIR filter inputAndthe output relational expression is as follows:

wherein the content of the first and second substances,respectively representing an input signal time and an output signal time,is the unit sample response of the finite impulse response filter;

the frequency response of the filter is:

representing the signal frequency;

after wavelet transformation and filtering processing, normalization processing is carried out on physiological signal data, and a calculation formula is as follows:

wherein the content of the first and second substances,andrespectively representing signalsMean and standard deviation at all times;

and truncating the physiological signal data by using a time window to enable the dimensionality of each training signal data matrix to be two-dimensional data, and dividing the two-dimensional data set into a training set and a test set.

5. The convolutional neural network-based nondestructive testing apparatus according to claim 1, wherein in the signal preprocessing module, the window function is selected according to the specification and parameter requirements of the FIR filter; calculating the size of the transition region according to the required passband edge frequency and stopband starting frequency, thereby calculating the length of the window function; and finally, calculating the unit impulse response of the required filter according to the window function and the unit impulse response of the ideal filter.

6. The convolutional neural network-based nondestructive testing device of claim 1, wherein in the binary prediction model training module, the binary labels corresponding to the physiological signal data in the training set and the training set are input into the convolutional neural network to train the binary prediction model, and the binary prediction model with the best prediction effect is selected through the test set to determine the final binary prediction model;

the convolutional neural network comprises an input layer, a plurality of convolutional layers, a plurality of pooling layers, a plurality of full-connection layers and an output layer which are connected in sequence;

wherein, in the input layer, the input value is the two-dimensional data generated after the data preprocessing in the step S2, and the two-dimensional data generated after the data preprocessing is transmitted to the convolutional layer, and each sample X has one dimension ofA tensor matrix of, whereinRepresents the number of channels of the electrical signal,represents a time step;

the convolution layer receives data of an input layer and uses small-batch normalization to prevent gradient disappearance and over-fitting problems, the output after being filtered by each convolution filter is a two-dimensional tensor, and the two-dimensional tensor is transmitted to the pooling layer;

the down-sampling in the Pooling layer is carried out by Max-Pooling, the purpose is to carry out dimensionality reduction on the input tensor of the convolution layer, reduce the number of parameters under the condition of not losing characteristic information so as to reduce the calculated amount, flatten the multidimensional input tensor into a one-dimensional tensor form, and transmit the tensor to the full-connection layer so as to realize the transition from the convolution layer to the full-connection layer;

the fully connected layer plays a role of a 'classifier' in the whole convolutional neural network and is used for mapping the learned 'distributed feature representation' to a sample mark space; the output of the fully-connected layer is a vectorAnd is and；for each neuron, i.e. predictive probabilityAndrespectively representing the probability of not detecting the stool signal and the probability of detecting the stool signal; therefore, the expression of the detection category of the binary signal is as follows:

wherein X represents an input sample, E represents a classifier obtained by learning, and the prediction probability of the full connection layer is used as the output of the binary prediction model.

7. The convolutional neural network-based nondestructive testing device of claim 1, wherein in the stochastic prediction model training module, stochastic labels corresponding to the physiological signal data in the training set and the training set are obtained; the stool labels are divided into two types of stool labels with stool meaning and stool meaning;

using the shit labels corresponding to physiological signal data in a training set and a training set as the input of a shit prediction model, inputting the shit labels into the constructed shit prediction model for training, using shit signal prediction of the training set as the output of the shit prediction model to obtain the trained shit prediction model, and then selecting the shit prediction model with the best prediction effect through a test set;

and (3) taking the test set as test data, taking the excrement signal prediction of the test set as the output of the test set in an excrement prediction model, comparing the output with excrement signal labels corresponding to the physiological data of the actual test set to obtain the prediction accuracy of the test set, retraining by a method of setting a threshold value if the accuracy does not exceed the threshold value until the accuracy reaches the threshold value, and determining the final excrement prediction model.

8. The convolutional neural network-based nondestructive testing device as claimed in claim 1, wherein the embeddable computer subsystem is a Jetson TX2 computer subsystem.

9. The convolutional neural network-based binary nondestructive testing device of claim 1, wherein a final binary prediction model obtained from the data filtering algorithm in the signal preprocessing module and the binary prediction model training module is transplanted into an embeddable computer subsystem to predict whether a human body generates a convenient meaning in real time, specifically as follows:

the real-time physiological signal data of the patient are acquired through the signal acquisition module, preprocessed through a data filtering algorithm and finally input into a final stool prediction model for prediction, and a stool early warning signal is obtained.

10. The convolutional neural network-based binary nondestructive testing device as claimed in any one of claims 1 to 9, wherein the specific testing process comprises the following steps:

s1, acquiring real-time physiological signal data and shit labels of a plurality of subjects;

s2, preprocessing the acquired physiological signal data through a data filtering algorithm, forming a two-dimensional data set by the preprocessed physiological signal data, and dividing the two-dimensional data set into a training set and a test set;

s3, inputting the training set and the shit labels corresponding to the physiological data in the training set into a convolutional neural network to train a shit prediction model, selecting the shit prediction model with the best prediction effect through the test set, and determining a final shit prediction model;

and S4, transplanting the data filtering algorithm and the final stool prediction model into an embeddable computer subsystem to predict whether the human body generates stool intentions in real time.

Technical Field

The invention relates to the field of signal detection and early warning, in particular to a portable nondestructive testing device based on a convolutional neural network.

Background

The disabled old people can have various complications after being bedridden for a long time, the life quality and the service life of the old people are seriously influenced, the intelligent excrement and urine monitoring technology and the processing function module based on individualized information are developed according to the long-term care and rehabilitation requirements of the disabled old people from clinical requirements, multi-scene application facing hospitals, nursing institutions, families and the like is established, the problem that the disabled old people are difficult to care for a long time can be solved, the social cost is saved, and the practical value and the social benefit are great.

On the other hand, the physiological signals for monitoring the excrement and urine are very weak, generally only about 50 microvolts, the amplitude range is 5 microvolts to 100 microvolts, and the frequency range is generally 0.5 Hz to 35 Hz. Because signals have characteristics of weak level, narrow band and the like, the signals are difficult to extract under strong electromyographic noise and power frequency interference, and complex processing on the signals is more difficult.

Among the urination detection and transmission devices (P2002-369810) is a urination detection and transmission device for constantly detecting the state of urine in the bladder from the inside of the bladder. The urination detecting and transmitting device is disposed inside the urinary bladder through the urethra, and if the detection result indicates that the stored urine amount reaches the urination level, the detection signal is launched to the side of the urination reporting device through the transmitting antenna. This method is invasive and highly invasive.

The existing excrement signal acquisition equipment has the following defects:

(1) various excrement signal acquisition equipment is high in price, large in size, complex to use and the like, so that the excrement signal acquisition equipment cannot be widely applied;

(2) the existing excrement and urine signal acquisition equipment adopts an invasive monitoring scheme, is difficult to correspond to various medical conditions and is difficult to observe in real time.

(3) The acquired data of the existing portable signal acquisition equipment cannot correspond to a server interface, and the electroencephalogram signals cannot be accurately acquired in real time.

Disclosure of Invention

The invention aims to overcome the defects of the prior art and provide a device for acquiring physiological signals of patients in real time, processing and analyzing the signals in real time and giving early warning to shit signals.

The purpose of the invention is realized by at least one of the following technical solutions.

A stool nondestructive testing device based on a convolutional neural network comprises a signal acquisition module, a signal preprocessing module, a stool prediction model training module and an embeddable computer subsystem;

the signal acquisition module acquires physiological signal data of a patient in real time and inputs the physiological signal data into the signal preprocessing module for preprocessing, the signal preprocessing module inputs the preprocessed physiological signal data into the binary prediction model training module, a final binary prediction model is obtained through training and is input into the embeddable computer subsystem, a data filtering algorithm and a final binary prediction model are transplanted on the embeddable computer subsystem, the physiological signal data sent by the signal acquisition module are received in real time, the physiological signal data are preprocessed through the data filtering algorithm and then input into the final binary prediction model for prediction, and a binary early warning signal is obtained.

Further, the signal acquisition module comprises:

the electronic stethoscope is used for measuring the bowel sounding signals of the patient; the electronic stethoscope comprises a stethoscope head, a sound guide tube and an ear hook;

a gastric electrical sensor for measuring a gastric electrical signal of the patient; the stomach electric sensor comprises a measuring electrode and a transmission interface;

the electrocardio sensor is used for measuring electrocardiosignals of a patient; the electrocardio sensor comprises a measuring electrode and a transmission interface;

the physiological signal data comprises intestinal tract bowel sounding signals, stomach electric signals and electrocardiosignals.

Furthermore, the real-time physiological signal data are acquired in the morning, the noon and the evening respectively in different situations, including fasting state and non-fasting state, and with and without defecation;

the real-time physiological signal data acquisition process is as follows:

intestinal borygmus signals, namely, tightly attaching an auscultation head to the skin, and respectively collecting the borygmus signals from the upper right quadrant, the upper left quadrant and the lower left quadrant of the abdomen of a subject;

the electrocardiosignal, the measuring electrode is clung to the chest part of the testee and collects the electrocardiosignal;

and (3) a stomach electric signal, namely tightly attaching a measuring electrode to the stomach of the subject and collecting the stomach electric signal.

Further, in the signal preprocessing module, the physiological signal data acquired by the signal acquisition module is preprocessed through a data filtering algorithm, including noise reduction, filtering and normalization processing, specifically as follows:

adopting wavelet to reduce noise of physiological signal data, selecting wavelet function to decompose physiological signal data, and setting the original signal expression of physiological signal data as:

wherein the content of the first and second substances,representing signalsThe time in (1) is (a),by a set of functionsBy stretching, it is meant that all discontinuities are only integersA space formed by concentrated piecewise constant functions;is a non-negative integer representing spaceThe highest resolution of (c);is a set of integersElement(s) in (1), representing translation parameters;is a signalDecomposed in spaceTo (1) aWavelet packet coefficients;representing signalsIn spaceTo (3) is performed.

Is a scale function, defined as

Is thatCan be decomposed into:then there are:

thenCan be expressed as:

wherein the content of the first and second substances,is a signalDecomposed in space2 ndWavelet packet coefficients;is a signalDecomposed in spaceTo (1) aWavelet packet coefficients;is formed byStretching the linear combination of (1);representing signalsIs/are as followsA component;representing signalsIs/are as followsA component;

is a wavelet function expressed as:

the decomposition process being followed bySubstitutionContinuing handleIs decomposed intoObtaining:

method for constructing FIR filter by adopting window function design, L-order FIR filter inputAndthe output relational expression is as follows:

wherein the content of the first and second substances,respectively representing an input signal time and an output signal time,is the unit sample response of the finite impulse response filter;

the frequency response of the filter is:

representing the signal frequency;

after wavelet transformation and filtering processing, normalization processing is carried out on physiological signal data, and a calculation formula is as follows:

wherein the content of the first and second substances,andrespectively representing signalsMean and standard deviation at all times;

and truncating the physiological signal data by using a time window to enable the dimensionality of each training signal data matrix to be two-dimensional data, and dividing the two-dimensional data set into a training set and a test set.

Furthermore, in the signal preprocessing module, a window function is selected according to the specification and parameter requirements of the FIR filter; calculating the size of the transition region according to the required passband edge frequency and stopband starting frequency, thereby calculating the length of the window function; and finally, calculating the unit impulse response of the required filter according to the window function and the unit impulse response of the ideal filter.

Further, in the stool prediction model training module, the training set and the stool labels corresponding to the physiological signal data in the training set are input into the convolutional neural network to train the stool prediction model, and the stool prediction model with the best prediction effect is selected through the test set to determine the final stool prediction model;

the convolutional neural network comprises an input layer, a plurality of convolutional layers, a plurality of pooling layers, a plurality of full-connection layers and an output layer which are connected in sequence;

wherein, in the input layer, the input value is the two-dimensional data generated after the data preprocessing in the step S2, and the two-dimensional data generated after the data preprocessing is transmitted to the convolutional layer, and each sample X has one dimension ofA tensor matrix of, whereinRepresents the number of channels of the electrical signal,represents a time step;

the convolution layer receives data of an input layer and uses small-batch normalization to prevent gradient disappearance and over-fitting problems, the output after being filtered by each convolution filter is a two-dimensional tensor, and the two-dimensional tensor is transmitted to the pooling layer;

the down-sampling in the Pooling layer is carried out by Max-Pooling, the purpose is to carry out dimensionality reduction on the input tensor of the convolution layer, reduce the number of parameters under the condition of not losing characteristic information so as to reduce the calculated amount, flatten the multidimensional input tensor into a one-dimensional tensor form, and transmit the tensor to the full-connection layer so as to realize the transition from the convolution layer to the full-connection layer;

the fully connected layer plays a role of a 'classifier' in the whole convolutional neural network and is used for mapping the learned 'distributed feature representation' to a sample mark space; the output of the fully-connected layer is a vectorAnd is and；for each neuron, i.e. predictive probabilityAndrespectively representing the probability of not detecting the stool signal and the probability of detecting the stool signal; therefore, the expression of the detection category of the binary signal is as follows:

wherein X represents an input sample, E represents a classifier obtained by learning, and the prediction probability of the full connection layer is used as the output of the binary prediction model.

Further, in a stool prediction model training module, obtaining a training set and stool labels corresponding to physiological signal data in the training set; the stool labels are divided into two types of stool labels with stool meaning and stool meaning;

using the shit labels corresponding to physiological signal data in a training set and a training set as the input of a shit prediction model, inputting the shit labels into the constructed shit prediction model for training, using shit signal prediction of the training set as the output of the shit prediction model to obtain the trained shit prediction model, and then selecting the shit prediction model with the best prediction effect through a test set;

and (3) taking the test set as test data, taking the excrement signal prediction of the test set as the output of the test set in an excrement prediction model, comparing the output with excrement signal labels corresponding to the physiological data of the actual test set to obtain the prediction accuracy of the test set, retraining by a method of setting a threshold value if the accuracy does not exceed the threshold value until the accuracy reaches the threshold value, and determining the final excrement prediction model.

Further, the embeddable computer subsystem adopts a Jetson TX2 computer subsystem.

Further, the final prediction model of the stool and urine obtained from the data filtering algorithm in the signal preprocessing module and the training module of the stool and urine prediction model is transplanted into the embeddable computer subsystem to predict whether the human body generates stool and urine in real time, which is specifically as follows:

the real-time physiological signal data of the patient are acquired through the signal acquisition module, preprocessed through a data filtering algorithm and finally input into a final stool prediction model for prediction, and a stool early warning signal is obtained.

Further, the detection process of the convolutional neural network-based two-dimensional nondestructive detection device comprises the following steps:

s1, acquiring real-time physiological signal data and shit labels of a plurality of subjects;

s2, preprocessing the acquired physiological signal data through a data filtering algorithm, forming a two-dimensional data set by the preprocessed physiological signal data, and dividing the two-dimensional data set into a training set and a test set;

s3, inputting the training set and the shit labels corresponding to the physiological data in the training set into a convolutional neural network to train a shit prediction model, selecting the shit prediction model with the best prediction effect through the test set, and determining a final shit prediction model;

and S4, transplanting the data filtering algorithm and the final stool prediction model into an embeddable computer subsystem to predict whether the human body generates stool intentions in real time.

Compared with the prior art, the invention has the advantages that:

the invention provides a measuring method for acquiring physiological signals of a patient in real time, and can process and analyze the signals in real time and obtain stool and urine signal early warning.

Drawings

Fig. 1 is a schematic diagram of a system structure of a convolutional neural network-based nondestructive testing apparatus according to an embodiment of the present invention.

FIG. 2a is a schematic diagram of an electronic stethoscope; figure 2b is a schematic diagram of an electrocardiograph sensor and a pyroelectric sensor.

Fig. 3 is a schematic diagram of a network structure of a convolutional neural network according to an embodiment of the present invention.

FIG. 4 is a diagram illustrating a data structure of a convolution binary signal according to an embodiment of the present invention.

Fig. 5 is a schematic diagram of CNN training data allocation in the embodiment of the present invention.

FIG. 6 is a schematic block diagram of a portable data filtering algorithm and a binary prediction model according to an embodiment of the present invention.

Detailed Description

The present invention will be described in further detail with reference to examples and drawings, but the present invention is not limited thereto.

Example (b):

a stool nondestructive testing device based on a convolutional neural network is shown in figure 1 and comprises a signal acquisition module, a signal preprocessing module, a stool prediction model training module and an embeddable computer subsystem;

the signal acquisition module acquires physiological signal data of a patient in real time and inputs the physiological signal data into the signal preprocessing module for preprocessing, the signal preprocessing module inputs the preprocessed physiological signal data into the binary prediction model training module, a final binary prediction model is obtained through training and is input into the embeddable computer subsystem, a data filtering algorithm and a final binary prediction model are transplanted on the embeddable computer subsystem, the physiological signal data sent by the signal acquisition module are received in real time, the physiological signal data are preprocessed through the data filtering algorithm and then input into the final binary prediction model for prediction, and a binary early warning signal is obtained.

Further, the signal acquisition module comprises:

the electronic stethoscope is used for measuring the bowel sounding signals of the patient; as shown in fig. 2a, the electronic stethoscope comprises a stethoscope head 1, a sound guide tube 2 and an ear hook 3; the intestinal bowel sounding signal is a gas-water passing sound generated between gas and liquid in an intestinal tube during intestinal peristalsis and can be used as an important index for detecting intestinal diseases; bowel sounds of different characteristics or different types, representing bowel movements in different pathological states;

a gastric electrical sensor for measuring a gastric electrical signal of the patient; as shown in fig. 2b, the gastric electrical sensor comprises a measuring electrode 4 and a transmission interface 5; the stomach electric signals are electric signals generated during gastrointestinal peristalsis, and the stomach electric signals with different characteristics reflect the movement state of the stomach and the intestine;

the electrocardio sensor is used for measuring electrocardiosignals of a patient; as shown in fig. 2b, the electrocardiograph sensor comprises a measuring electrode 4 and a transmission interface 5;

in this embodiment, the signal acquisition module comprises a stomach electric sensor, an electrocardio sensor and a 3M series Littmann3200 type electronic stethoscope configured by a Biosignals series 4-channel physiological recording monitor

The physiological signal data comprises intestinal tract bowel sounding signals, stomach electric signals and electrocardiosignals.

Furthermore, the real-time physiological signal data are acquired in the morning, the noon and the evening respectively in different situations, including fasting state and non-fasting state, and with and without defecation;

the subject needs to be a person with no history of intestinal tract diseases in nearly three months and normal gastrointestinal functions; then, arranging the tested volunteer to wear the gastric electricity sensor, the electrocardio sensor and the electronic stethoscope, and collecting data in a lying posture;

the real-time physiological signal data acquisition process is as follows:

intestinal bowel sounding signals are acquired from the right upper quadrant (ascending colon), the left upper quadrant (descending colon) and the left lower quadrant (sigmoid colon) of the abdomen of a subject by closely attaching an auscultatory head to the skin;

the electrocardiosignal, the measuring electrode is clung to the chest part of the testee and collects the electrocardiosignal;

and (3) a stomach electric signal, namely tightly attaching a measuring electrode to the stomach of the subject and collecting the stomach electric signal.

Further, in the signal preprocessing module, the physiological signal data acquired by the signal acquisition module is preprocessed through a data filtering algorithm, including noise reduction, filtering and normalization processing, specifically as follows:

adopting wavelet to reduce noise of physiological signal data, selecting wavelet function to decompose physiological signal data, and setting the original signal expression of physiological signal data as:

wherein the content of the first and second substances,which represents the time in the signal or signals,by a set of functionsExpanding, namely representing a space formed by the piecewise constant functions of which all the discontinuous points are only in the integer set;is a non-negative integer representing spaceThe highest resolution of (c);is a set of integersElement(s) in (1), representing translation parameters;is a signalDecomposed in spaceTo (1) aWavelet packet coefficients;representing signalsIn spaceTo (3) is performed.

Is a scale function, defined as

Is thatCan be decomposed into:then, there are:

thenCan be expressed as:

wherein the content of the first and second substances,is a signalDecomposed in space2 ndWavelet packet coefficients;is a signalDecomposed in spaceTo (1) aWavelet packet coefficients;is formed byStretching the linear combination of (1);representing signalsIs/are as followsA component;representing signalsIs/are as followsA component;

is a wavelet function expressed as:

the decomposition process being followed bySubstitutionContinuing handleIs decomposed intoObtaining:

；

method for constructing FIR filter by adopting window function design, L-order FIR filter inputAndthe output relational expression is as follows:

wherein the content of the first and second substances,respectively representing an input signal time and an output signal time,is the unit sample response of the finite impulse response filter;

the frequency response of the filter is:

representing the signal frequency.

After wavelet transformation and filtering processing, normalization processing is carried out on physiological signal data, and a calculation formula is as follows:

wherein the content of the first and second substances,andrespectively representing signalsMean and standard deviation at all times;

and truncating the physiological signal data by using a time window to enable the dimensionality of each training signal data matrix to be two-dimensional data, and dividing the two-dimensional data set into a training set and a test set.

In this embodiment, the physiological signal data is truncated with a time window of 0-600ms, such that each training signal data matrix has dimensions of. Since the ratio of positive and negative samples of the training data set is 1:5, data imbalance is easily caused, and therefore all positive samples are copied by 4 times to achieve positive and negative sample balance.

In the signal preprocessing module, a window function is selected according to the specification and parameter requirements of the FIR filter; calculating the size of the transition region according to the required passband edge frequency and stopband starting frequency, thereby calculating the length of the window function; and finally, calculating the unit impulse response of the required filter according to the window function and the unit impulse response of the ideal filter.

In the stool prediction model training module, a training set and stool labels corresponding to physiological signal data in the training set are input into a convolutional neural network to train a stool prediction model, the stool prediction model with the best prediction effect is selected through a test set, and a final stool prediction model is determined;

as shown in fig. 3, in the present embodiment, an 11-layer convolutional neural network is adopted, which includes an input layer, a convolutional layer, a BN layer, a max pooling layer, a full link layer, and an output layer;

in this embodiment, as shown in fig. 4, the constructed convolutional neural network structure sequentially includes:

in the input layer, the input value is two-dimensional data generated by the physiological signal acquisition system after the data preprocessing in step S2, and each sample X has one dimension ofThe tensor matrix of (a), in this embodiment,3, the number of the electric signal channels is shown,at 651, a step of time is indicated,

and (3) constructing the 11-layer convolutional neural network model by using a machine learning library, then taking a two-dimensional tensor matrix corresponding to the bowel sound, the gastric electricity and the electrocardio time sequence physiological data input by the physiological signal acquisition system as the input of a shit prediction model, and transmitting the input to a final full-connection layer to output a result to show whether the result is convenient or not.

The fully-connected layer acts as a 'classifier' in the overall convolutional neural network for mapping the learned 'distributed feature representation' to the sample label space. The output of the fully-connected layer is a vectorAnd is and；for each neuron, i.e. predictive probabilityAndrespectively representing the probability of not detecting the stool signal and the probability of detecting the stool signal; therefore, the expression of the detection category of the binary signal is as follows:

wherein X represents an input sample, E represents a classifier obtained by learning, and the prediction probability of the full connection layer is used as the output of the binary prediction model.

In a stool prediction model training module, acquiring a training set and stool labels corresponding to physiological signal data in the training set; the stool labels are divided into two types of stool labels with stool meaning and stool meaning;

using the shit labels corresponding to physiological signal data in a training set and a training set as the input of a shit prediction model, inputting the shit labels into the constructed shit prediction model for training, using shit signal prediction of the training set as the output of the shit prediction model to obtain the trained shit prediction model, and then selecting the shit prediction model with the best prediction effect through a test set;

and taking the test set as test data, taking the shit signal prediction of the test set as the output of the test set in a shit prediction model, comparing the output with shit signal labels corresponding to the physiological data of the actual test set to obtain the prediction accuracy of the test set, and re-training until the accuracy reaches a threshold value by setting the threshold value to be 95% if the accuracy does not exceed the threshold value, so as to determine the final shit prediction model.

In this example, as shown in fig. 5, a total of 60 training and testing rounds were performed, and the total ecg beats for training and testing purposes were assigned as follows: early warning training data on stool and urineIn use thereinTo verify the algorithm.

In this embodiment, the embeddable computer subsystem adopts a Jetson TX2 computer subsystem, and the processor of the Nvidia Jetson TX2 computer subsystem takes 256 Nvidia cuda and 64-bit cpu as cores and is equipped with a data processing platform to process the acquired physiological electrical signals in real time. The processor functions include:

displaying: NVIDIA Jetson TX2 connects to the display through an HDMI interface.

Controlling: NVIDIA Jetson TX2 communicates with the peripheral subsystem through a uart serial port, usb interface.

Power supply: NVIDIA Jetson TX2 is connected to a power supply via a three-hole plug.

Starting up: the NVIDIA Jetson TX2 has four red buttons, wherein the lower face of the POWER Button is marked with POWER BTN font, and the following POWER Button are respectively a Force Recovery Button, a User Defined Button and a Reset Button.

As shown in fig. 6, the final stool prediction model obtained from the data filtering algorithm in the signal preprocessing module and the stool prediction model training module is transplanted into the embeddable computer subsystem to predict whether the human body generates stool intentions in real time, which is specifically as follows:

the real-time physiological signal data of the patient are acquired through the signal acquisition module, preprocessed through a data filtering algorithm and finally input into a final stool prediction model for prediction, and a stool early warning signal is obtained.

The convolutional neural network-based two-dimensional nondestructive testing process of the embodiment comprises the following steps:

s1, acquiring real-time physiological signal data and shit labels of a plurality of subjects;

s2, preprocessing the acquired physiological signal data through a data filtering algorithm, forming a two-dimensional data set by the preprocessed physiological signal data, and dividing the two-dimensional data set into a training set and a test set;

s3, inputting the training set and the shit labels corresponding to the physiological data in the training set into a convolutional neural network to train a shit prediction model, selecting the shit prediction model with the best prediction effect through the test set, and determining a final shit prediction model;

and S4, transplanting the data filtering algorithm and the final stool prediction model into an embeddable computer subsystem to predict whether the human body generates stool intentions in real time.

The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.