CN113576401A - Sleep apnea syndrome rapid diagnosis device based on convolutional neural network - Google Patents
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Abstract
The invention discloses a sleep apnea syndrome rapid diagnosis device based on a convolutional neural network, which comprises: the data acquisition and acquisition module is used for monitoring night sleep monitoring data of the testee of the messenger by using the polysomnography or the portable sleep monitoring equipment and acquiring a corresponding medical diagnosis report; the data preprocessing module comprises a data extracting unit, a signal down-sampling unit and a data acquiring unit, and the neural network module is used for inputting the sleep monitoring data of the person to be detected into the trained sleep apnea syndrome recognition model to predict the sleep apnea syndrome of the person to be detected and outputting the severity of the sleep apnea syndrome of the person to be detected. The invention can more efficiently and directly realize the diagnosis of the severity of the sleep apnea syndrome, assist doctors in diagnosis and improve the diagnosis efficiency.
Description
Technical Field
The application relates to the technical field of neural networks and medical treatment, in particular to a sleep apnea syndrome rapid diagnosis device based on a convolutional neural network.
Background
Sleep Apnea Syndrome (SAS) refers to a Sleep disorder disease in which Apnea (Sleep Apnea) and hypoventilation (Hypopnea) repeatedly occur during Sleep in a patient. Apnea and hypopnea repeatedly occurring at night cause chronic intermittent hypoxia, carbon dioxide retention, increase of sympathetic excitability, systemic inflammatory reaction, and oxidative stress reaction, and insufficient antioxidant capacity, thereby causing or aggravating cardiovascular and cerebrovascular diseases and metabolic disorders. SAS can seriously affect human health.
The prevalence rate of SAS in China is about 4%, and the SAS is a common disease. However, diagnosis of SAS requires a special device, Polysomnography (PSG). At present, only three-level hospitals or partial two-level hospitals in large and medium cities can carry out standard diagnosis and treatment on the disease, so that a large number of patients cannot be diagnosed and treated in time, and great harm is caused to the health of people. In addition, in the prior art, PSG is mainly used for detecting SAS, but PSG detects a lot of signals, and professional technicians are required for diagnosis, which causes a lot of labor cost and equipment cost.
The AHI index (Apnea hypnea index Apnea hypopnea index) is an important index for measuring the tested sleep Apnea syndrome. In the prior art, physiological signal data of sleep monitoring is obtained firstly, noise reduction and filtering are carried out on the signals firstly, then the signals are segmented, signal characteristics are extracted manually, a neural network is trained to predict whether the signal segment belongs to a normal segment or a sleep respiratory event occurs, an AHI value is calculated according to the sleep respiratory event, and finally the tested SAS disease degree is judged according to the AHI value. These methods require noise reduction and filtering of the signal, and are weak in signal interference resistance. In addition, since the signal is segmented, event annotation data is also required. However, in practice, event annotation data is not readily available, and this segmented approach does not allow a direct diagnosis of the severity of sleep apnea syndrome, and an AHI value is also calculated. Most importantly, these methods require manual extraction of signal features, which is time consuming and laborious. Expert domain knowledge is also required, and important information hidden in the signal cannot be represented, so that the robustness and the universality are poor.
Disclosure of Invention
In order to enable potential patients with SAS to be diagnosed and treated in time, the invention provides a sleep apnea syndrome rapid diagnosis device based on a convolutional neural network, and aims to solve the problems of low efficiency, poor universality and the like in the existing method.
In order to realize the task, the invention adopts the following technical scheme:
a sleep apnea syndrome rapid diagnosis device based on a convolutional neural network comprises:
the data acquisition and acquisition module is used for monitoring night sleep monitoring data of the testee of the messenger by using the polysomnography or the portable sleep monitoring equipment and acquiring a corresponding medical diagnosis report;
the data preprocessing module comprises a data extraction unit, a signal down-sampling unit and a data acquisition unit, wherein:
the data extraction unit is used for extracting 4 physiological signals of a blood oxygen saturation degree signal, an airflow signal, a chest respiration signal and an abdomen respiration signal from the sleep monitoring data of each testee, extracting label data from a medical diagnosis report, and forming a group of original data by each group of the physiological signals and the label data; the signal downsampling unit is used for downsampling 4 physiological signals of the blood oxygen saturation signal, the airflow signal, the chest respiration signal and the abdomen respiration signal in each set of original data; the data acquisition unit is used for extracting data positioned in the middle from the original data of each testee after the downsampling processing as training data of the testee; constructing training data of different testees into a data set;
and the neural network module is used for inputting the sleep monitoring data of the person to be tested into the trained sleep apnea syndrome recognition model to predict the sleep apnea syndrome of the person to be tested and outputting the severity of the sleep apnea syndrome of the person to be tested.
Further, the construction method of the sleep apnea syndrome recognition model comprises the following steps:
constructing a convolutional neural network, wherein the convolutional neural network comprises 4 one-dimensional convolutional layers, 1 batch normalization layer, 1 maximum pooling layer and 1 full-connection layer; the input of the convolutional neural network is training data, firstly, the input normalization processing is completed through a batch normalization layer, then, the training data is activated through a one-dimensional convolutional layer provided with 4 convolutional kernels with the size of 1 × 8 and a Relu function, then, the maximum pooling down-sampling is performed through a maximum pooling layer with the size of 1 × 2, then, the training data is respectively passed through a one-dimensional convolutional layer provided with 128 convolutional kernels with the size of 1 × 8, a one-dimensional convolutional layer provided with 64 convolutional kernels with the size of 1 × 8 and a one-dimensional convolutional layer provided with 16 convolutional kernels with the size of 1 × 8, and finally, the output is converted into the severity of the tested sleep apnea syndrome through a full connection layer and an output function: and 4 normal, mild, moderate and severe classification probabilities, and taking the class with the highest probability as the prediction result of the neural network.
Further, the construction method further comprises: and training the neural network by using a training set by adopting a random gradient descent algorithm, adjusting parameters of the neural network by combining a test set and a verification set, and storing the trained model as a trained sleep apnea syndrome recognition model.
Further, the night sleep monitoring data of the testee is collected for at least 7 hours; correspondingly, the data acquisition unit extracts data located in the middle 7 hours from the raw data of each subject after the down-sampling process.
Further, the 4 physiological signals are down-sampled to 1 Hz.
Further, the data preprocessing module further comprises a data set dividing unit for dividing the data set into a training set, a verification set and a test set according to a ratio of 6:2: 2.
Further, the sleep monitoring data of the person to be detected are monitoring data of 4 signals of blood oxygen saturation, nasal airflow, chest and abdomen movement in the sleep state of the person to be detected.
Compared with the prior art, the invention has the following technical characteristics:
the invention trains the convolutional neural network by using the blood oxygen saturation signal, the airflow signal, the chest breathing signal and the abdomen breathing signal at 7 hours at night, automatically extracts the signal characteristics by using 4 convolutional layers of the convolutional neural network, is basically different from the prior method, does not need to segment the signals, and does not need event marking data. Moreover, because the training signature of the present invention is the severity of sleep apnea syndrome: the convolutional neural network directly predicts the severity of the tested sleep apnea syndrome without judging whether a sleep apnea event occurs in a signal, and the method is more direct. In addition, because the noise in the physiological signal is also used as the input of the convolutional neural network, the invention has stronger anti-interference capability on the signal. In conclusion, the invention can more efficiently and directly realize the diagnosis of the severity of the sleep apnea syndrome, assist the diagnosis of doctors and improve the diagnosis efficiency.
Drawings
FIG. 1 is a schematic flow chart of the operation of the apparatus of the present invention;
FIG. 2 is a block diagram of a convolutional neural network model;
FIG. 3 shows the judgment of the SAS disease level and AHI and/or hypoxemia level.
Detailed Description
The invention provides a sleep apnea syndrome severity diagnostic device based on a convolutional neural network, which uses the existing portable sleep monitoring equipment or a polysomnograph to obtain 4 kinds of physiological signal data of a tested blood oxygen saturation signal, an airflow signal, a chest respiration signal and an abdomen respiration signal, trains a convolutional neural network according to the 4 kinds of original physiological signal data, and finally predicts the severity of the tested sleep apnea syndrome through the convolutional neural network so as to realize the rapid diagnosis of the sleep apnea syndrome; the core of the realization of the invention is the convolutional neural network structure which is reasonably designed. The flow chart is as follows
Referring to fig. 1, the fast sleep apnea syndrome diagnosing apparatus based on a convolutional neural network of the present invention includes:
1. data acquisition and acquisition module
Monitoring sleep monitoring data of a testee of the testee for at least 7 hours at night by using a polysomnography or portable sleep monitoring equipment, and acquiring a corresponding medical diagnosis report; wherein the report is self-administered by the device or assisted by a physician.
In this embodiment, the data is actual clinical data provided by a hospital in Guangzhou, which is collected from a PSG portable sleep monitoring device. The data is composed of 1755 records, and each record comprises a plurality of physiological signal data such as a blood oxygen saturation signal, an airflow signal, a chest respiration signal, an abdomen respiration signal and the like and a medical diagnosis report (containing the diagnosis result of the severity of the tested sleep apnea syndrome made by a doctor).
2. Data preprocessing module
The device comprises a data extraction unit, a signal down-sampling unit, a data acquisition unit and a data set division unit, wherein:
the data extraction unit is used for extracting 4 physiological signals of a blood oxygen saturation degree signal, an airflow signal, a chest respiration signal and an abdomen respiration signal from the sleep monitoring data of each testee, extracting label data from a medical diagnosis report, and forming a group of original data by each group of the physiological signals and the label data.
The signal down-sampling unit is used for down-sampling 4 physiological signals of the blood oxygen saturation signal, the airflow signal, the chest respiration signal and the abdomen respiration signal in each set of original data to 1 Hz; this is because the sampling frequency of the monitored data to be tested acquired by the sleep monitoring device is not necessarily the same, and the downsampling process is performed to reduce the data dimension and unify the sampling frequency.
A data acquisition unit, which is used for extracting data (including 4 kinds of physiological signals and corresponding label data) positioned in the middle 7 hours from the original data of each testee after the down-sampling processing as training data of the testee; the training data of different subjects constitutes a data set. The design purpose of the unit is to extract the sleep monitoring data of the middle 7 hours of the testee, because the monitoring time of the tested monitoring data collected by using the sleep monitoring equipment is not always the same, and the traditional medicine diagnoses the sleep apnea syndrome according to the sleep monitoring data of at least 7 hours tested.
The data set dividing unit is used for dividing the data set into a training set, a verification set and a test set according to the proportion of 6:2:2 so as to scientifically train and test the model; in this embodiment, the training set has a total of 1051 records. The verification set has a total of 352 records and the test set has a total of 352 records.
3. Neural network module
The method is used for inputting the sleep monitoring data of the testee into a trained sleep apnea syndrome recognition model so as to predict the sleep apnea syndrome of the testee.
The sleep monitoring data of the person to be detected are monitoring data of 4 signals of blood oxygen saturation, nasal airflow, chest and abdomen movement of the person to be detected in a sleep state. Then, the monitoring data of the 4 signals are directly input into a trained sleep apnea syndrome recognition model, whether the tested person suffers from the sleep apnea syndrome can be directly predicted through the trained model, and if the tested person suffers from the sleep apnea syndrome, the model can recognize the severity of the tested sleep apnea syndrome.
The construction method of the sleep apnea syndrome recognition model comprises the following steps:
constructing a convolutional neural network, wherein the convolutional neural network comprises 4 one-dimensional convolutional layers, 1 batch normalization layer, 1 maximum pooling layer and 1 full-connection layer; the model structure diagram is shown in fig. 3. Since 4 kinds of physiological signals of the blood oxygen saturation signal, the airflow signal, the chest respiration signal, and the abdominal respiration signal extracted for 7 hours are time-series data in nature, one-dimensional convolution is used.
The input of the convolutional neural network is training data, namely 4 time sequences of the blood oxygen saturation signal with the sampling frequency of 1 in the time length of 7 hours, the airflow signal, the chest respiration signal and the abdomen respiration signal, so the input dimension of the convolutional neural network is 25200 x 4; firstly, input normalization processing is completed through a batch normalization layer, then, the normalization processing is carried out through a one-dimensional convolution layer provided with 4 convolution kernels with the size of 1 x 8 and activated through a Relu function, then, maximum pooling down-sampling is carried out through a maximum pooling layer with the size of 1 x 2, then, the normalization processing is carried out through one-dimensional convolution layers provided with 128 convolution kernels with the size of 1 x 8, one-dimensional convolution layers provided with 64 convolution kernels with the size of 1 x 8 and one-dimensional convolution layers provided with 16 convolution kernels with the size of 1 x 8, and finally, the output is converted into the severity of the tested sleep apnea syndrome through a full connection layer and an output function softmax (x): and 4 normal, mild, moderate and severe classification probabilities, and taking the class with the highest probability as the prediction result of the model.
Defining a convolution operation:
y(l)represents the output of the first convolutional layer;
α(l)an activation function representing the first convolutional layer;
ω(l)representing the convolution kernel weight of the convolution layer of the first layer;
X(l)representing the input of the first layer convolution layer;
b(l)indicating the bias of the first layer convolution layer;
the batch normalization layer is used for converting data into data with a mean value of 0 and a standard deviation of 1, and the conversion formula is as follows:
where μ is the mean of all training samples and σ is the standard deviation of all training samples. The role of the batch normalization layer is to normalize and normalize the training data.
The convolutional neural network provided by the invention uses a maximum pooling method to reduce the dimension of data and reduce the calculation amount. As shown, the output result of the first convolutional layer is reduced in dimension through the maximum pooling layer.
The output of the 4 convolutional layers was converted to the severity of the test sleep apnea syndrome by the fully-connected layer and the output function softmax (x): and 4 normal, mild, moderate and severe classification probabilities, and taking the class with the highest probability as the prediction result of the model. Output function of convolutional neural network softmax (x):
the convolutional neural network proposed by the present invention uses an Adam optimizer and selects a cross entropy function as the loss function of the training, the cross entropy loss function L (y, f (x)) is as follows:
wherein y represents a real label, f (x) represents a prediction function of the model, p, q represent a real distribution of the data label and a distribution given by the model prediction, respectively, and p (y)i|xi) Represents a sample xiThe true distribution of the labels is such that,
representing a given sample xiThe model predicts the probability distribution over the various classes.
And training the neural network by using a training set by adopting a random gradient descent algorithm, adjusting parameters of the neural network by combining a test set and a verification set, and storing the trained model as a trained sleep apnea syndrome recognition model.
The above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it should 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 technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (7)
1. A sleep apnea syndrome rapid diagnosis device based on a convolutional neural network is characterized by comprising the following components:
the data acquisition and acquisition module is used for monitoring night sleep monitoring data of the testee of the messenger by using the polysomnography or the portable sleep monitoring equipment and acquiring a corresponding medical diagnosis report;
the data preprocessing module comprises a data extraction unit, a signal down-sampling unit and a data acquisition unit, wherein:
the data extraction unit is used for extracting 4 physiological signals of a blood oxygen saturation degree signal, an airflow signal, a chest respiration signal and an abdomen respiration signal from the sleep monitoring data of each testee, extracting label data from a medical diagnosis report, and forming a group of original data by each group of the physiological signals and the label data; the signal downsampling unit is used for downsampling 4 physiological signals of the blood oxygen saturation signal, the airflow signal, the chest respiration signal and the abdomen respiration signal in each set of original data; the data acquisition unit is used for extracting data positioned in the middle from the original data of each testee after the downsampling processing as training data of the testee; constructing training data of different testees into a data set;
and the neural network module is used for inputting the sleep monitoring data of the person to be tested into the trained sleep apnea syndrome recognition model to predict the sleep apnea syndrome of the person to be tested and outputting the severity of the sleep apnea syndrome of the person to be tested.
2. The convolutional neural network-based sleep apnea syndrome rapid diagnosis device of claim 1, wherein the sleep apnea syndrome identification model is constructed by the following method:
constructing a convolutional neural network, wherein the convolutional neural network comprises 4 one-dimensional convolutional layers, 1 batch normalization layer, 1 maximum pooling layer and 1 full-connection layer; the input of the convolutional neural network is training data, firstly, the input normalization processing is completed through a batch normalization layer, then, the training data is activated through a one-dimensional convolutional layer provided with 4 convolutional kernels with the size of 1 × 8 and a Relu function, then, the maximum pooling down-sampling is performed through a maximum pooling layer with the size of 1 × 2, then, the training data is respectively passed through a one-dimensional convolutional layer provided with 128 convolutional kernels with the size of 1 × 8, a one-dimensional convolutional layer provided with 64 convolutional kernels with the size of 1 × 8 and a one-dimensional convolutional layer provided with 16 convolutional kernels with the size of 1 × 8, and finally, the output is converted into the severity of the tested sleep apnea syndrome through a full connection layer and an output function: and 4 normal, mild, moderate and severe classification probabilities, and taking the class with the highest probability as the prediction result of the neural network.
3. The convolutional neural network-based sleep apnea syndrome rapid diagnosis device of claim 1, wherein the construction method further comprises: and training the neural network by using a training set by adopting a random gradient descent algorithm, adjusting parameters of the neural network by combining a test set and a verification set, and storing the trained model as a trained sleep apnea syndrome recognition model.
4. The convolutional neural network-based sleep apnea syndrome rapid diagnosis device as recited in claim 1, wherein the night sleep monitoring data of the subject is collected for at least 7 hours; correspondingly, the data acquisition unit extracts data located in the middle 7 hours from the raw data of each subject after the down-sampling process.
5. The convolutional neural network-based sleep apnea syndrome rapid diagnosis device of claim 1, wherein the 4 physiological signals are down-sampled to 1 Hz.
6. The convolutional neural network-based sleep apnea syndrome rapid diagnosis device as recited in claim 1, wherein the data preprocessing module further comprises a data set partitioning unit for partitioning the data set into a training set, a validation set and a test set in a ratio of 6:2: 2.
7. The convolutional neural network-based sleep apnea syndrome rapid diagnosis apparatus as recited in claim 1, wherein the sleep monitoring data of the subject is monitoring data of 4 signals of blood oxygen saturation, nasal airflow, chest and abdomen movement in a sleep state of the subject.
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| CN114869241B (en) * | 2022-07-11 | 2022-09-23 | 西南交通大学 | Sleep respiratory event prediction method, device, equipment and readable storage medium |
| CN117064333A (en) * | 2023-08-01 | 2023-11-17 | 大连市中心医院 | Primary screening device for obstructive sleep apnea hypopnea syndrome |
| CN117064333B (en) * | 2023-08-01 | 2024-03-26 | 大连市中心医院 | Primary screening device for obstructive sleep apnea hypopnea syndrome |
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