WO2017101529A1

WO2017101529A1 - Electrocardio lead intelligent selection method and system

Info

Publication number: WO2017101529A1
Application number: PCT/CN2016/098231
Authority: WO
Inventors: 赵巍
Original assignee: 广州视源电子科技股份有限公司
Priority date: 2015-12-14
Filing date: 2016-09-06
Publication date: 2017-06-22
Also published as: CN105550653A; CN105550653B

Abstract

Provided are electrocardio lead intelligent selection method and system, the method comprises the steps of: extracting characteristic signal of the obtained electrocardio lead signals in the same time period, to obtain a global characteristic value of each electrocardio lead signal (S110); classifying the corresponding electrocardio lead signals according to the maximum value of an integrated wave and local pooled characteristic value of the electrocardio lead signals to obtain signal quality levels of each electrocardio lead signal (S120); extracting the maximum value of the integrated wave and local pooled characteristic value of the electrocardio lead signalswith different signal quality levels and training, to obtain a quality classifier (S130); and screening the electrocardio lead signals according to the quality classifier, to obtain and output the optimal electrocardio lead signal (S140). The electrocardio lead intelligent selection method and system can well reflect sudden changes of signals in local time, with high accuracy, without personnel intervention throughout the whole process, saving time and human resources, and the determination of signal qualities and the selection of leads can be finished before QRS detection, thereby saving the calculation amount.

Description

ECG lead intelligent selection method and system

Technical field

The present invention relates to the field of medical monitoring technologies, and in particular, to an ECG lead intelligent selection method and system.

Background technique

The monitoring and analysis of ECG signals is an important function of instruments such as monitors and electrocardiographs. The acquisition of ECG signals is usually performed simultaneously on multiple leads. For the collected multi-lead signals, the instrument usually selects only one lead signal for subsequent analysis, such as QRS (magnetic resonance angiography) detection. Wait. If the electrode of the lead used for analysis is in poor contact with the human body, the collected ECG signal may be seriously disturbed.

The traditional ECG intelligent selection method is to calculate several kinds of feature quantities on a signal of a length, and then compare the pre-set empirical parameters to judge the quality of the signal and select a suitable lead. The feature quantity obtained from a signal of a length is difficult to reflect the sudden change of the signal in a short time. The manual setting of the parameter has a large workload and the generalization ability is not strong. In addition, QRS detection is required to complete the signal quality judgment and guidance. The choice of the union. The traditional ECG intelligent selection method has the disadvantage of low accuracy.

Summary of the invention

Based on this, it is necessary to provide a highly accurate ECG lead intelligent selection method and system for the above problems.

An intelligent selection method for ECG leads includes the following steps:

Performing feature extraction on the acquired ECG lead signals of the same time period to obtain a global feature quantity of each of the ECG lead signals, the global feature quantity including a maximum value of the integrated wave of the ECG lead signal and pooling Local feature quantity

And classifying the corresponding ECG lead signals according to the maximum value of the integrated wave of the ECG lead signal and the localized feature quantity of the pool, to obtain a signal quality level of each of the ECG lead signals;

Extracting a maximum value of the integrated wave of the ECG lead signal of different signal quality levels and a localized feature quantity of the pool to obtain a quality classifier;

Each of the ECG lead signals is filtered according to the quality classifier to obtain and output an optimal ECG lead signal.

An intelligent guiding system for ECG leads, comprising:

a feature extraction module, configured to perform feature extraction on the acquired ECG lead signals of the same time period, to obtain a global feature quantity of each of the ECG lead signals, where the global feature quantity includes an integrated wave of the ECG lead signal Maximum and pooled local feature quantities;

a signal classification module, configured to classify the corresponding ECG lead signals according to the maximum value of the integrated wave of the ECG lead signal and the localized feature quantity of the pool, to obtain a signal quality level of each of the ECG lead signals;

a model training module, configured to extract a maximum value of the integrated wave of the ECG lead signal of different signal quality levels and a localized feature quantity of the pool to obtain a quality classifier;

The signal screening module is configured to filter each of the ECG lead signals according to the quality classifier to obtain and output an optimal ECG lead signal.

The above-mentioned ECG intelligent selection method and system perform feature extraction on the obtained ECG lead signals in the same time period, and obtain the global feature quantity of each ECG lead signal; the maximum value and pooling of the integrated wave according to the ECG lead signal The local feature quantity classifies the corresponding ECG lead signals to obtain the signal quality level of each ECG lead signal. Extracting the maximum value of the integrated wave of the ECG lead signal of different signal quality levels and the localized feature quantity of the pooling to obtain the quality classifier; screening the ECG lead signals according to the quality classifier to obtain and output the optimal cardiac conductance Linked signal. By extracting the global feature quantity of the ECG lead signal for signal classification and modeling and screening, the localized eigenvalues of the pool are introduced to express the state of the signal, which can well reflect the sudden change of the signal at local time with high accuracy. The whole process requires no personnel intervention, saving time and human resources. Signal quality judgment and lead selection can be completed before QRS detection, saving calculations.

DRAWINGS

1 is a flow chart of an embodiment of a central electrical lead intelligent selection method;

2 is a flow chart showing feature extraction of an acquired ECG lead signal of the same time period in an embodiment to obtain a global feature quantity of each ECG lead signal;

3 is a schematic diagram of extracting local feature quantities of an ECG lead signal in an embodiment;

4 is a flow chart showing the signal quality level of each ECG lead signal by classifying the corresponding ECG lead signals according to the maximum value of the integrated wave of the ECG lead signal and the localized feature quantity of the pooling in an embodiment;

5 is a flow chart of another embodiment of a center conduction intelligent selection method;

6 is a structural diagram of an embodiment of a center conduction intelligent selection system;

7 is a structural diagram of a feature extraction module in an embodiment;

8 is a structural diagram of a signal classification module in an embodiment;

9 is a structural diagram of another embodiment of a center conduction intelligent selection system.

detailed description

An intelligent selection method for ECG leads is suitable for ECG lead screening of instruments such as monitors and electrocardiographs. As shown in Figure 1, the above steps include the following steps:

Step S110: Perform feature extraction on the acquired ECG lead signals of the same time period to obtain global feature quantities of the respective ECG lead signals.

The ECG lead signals acquired during the same time period are acquired for feature extraction for use as a signal for screening. The global feature quantity specifically includes the maximum value of the integrated wave of the ECG lead signal and the localized feature quantity of the pooling. The localized feature quantity of the pooling refers to the feature quantity obtained by calculating and pooling the local area of the signal. In one of the embodiments, as shown in FIG. 2, step S110 includes steps S112 to S116.

Step S112: respectively calculating an integrated wave of each ECG lead signal, and extracting a maximum value of the integrated wave. The ECG lead signal is processed to obtain an integrated wave, and the maximum value of the integrated wave is extracted as a dimension corresponding to the global feature quantity of the ECG lead signal.

Step S113: respectively extract a baseline signal and a high frequency noise signal of each ECG lead signal. The specific manner of extracting the baseline signal and the high-frequency noise signal of each ECG lead signal is not unique, and may be selected according to actual conditions. In this embodiment, median filtering is used to extract the midline signal, and the Butterworth filter is used to extract. High frequency noise signal. In other embodiments, the baseline signal may also be acquired by a low pass filter or other means, and the high frequency noise signal may be obtained by a Chebyshev filter or other means.

Step S114: performing slice processing on each ECG lead signal according to a preset length and a step size to obtain a plurality of signal segment slices. The electrocardiographic signal is sliced to obtain a plurality of signal segment slices. As shown in FIG. 3, a schematic diagram of the local feature quantity of the ECG lead signal is extracted. The specific values of the preset length and the step size are not unique. In this embodiment, the preset length and the step size are 0.15 s and 0.05 s, respectively.

Step S115: respectively extracting the height of the ECG lead signal on each slice segment, the height of the integrated wave, the height of the baseline signal, and the mean, variance, kurtosis and kurtosis of the high frequency noise signal as local features of each signal segment slice. the amount.

In this embodiment, the height of the ECG lead signal on each slice segment, the height of the integrated wave, the height of the baseline signal, and the mean, variance, kurtosis, and kurtosis of the high-frequency noise signal are obtained as local feature quantities of the corresponding signal segment slice. , used as a subsequent step pooling process for signal classification and screening operations. The ECG lead signal and the relevant parameters of the extracted integrated wave, baseline signal and high frequency noise signal are collected as local feature quantities to improve the response ability to short-term mutation of the signal. It can be understood that in other embodiments, other parameters of the ECG lead signal on the slice segment can also be acquired as local feature quantities.

Step S116: performing a maximum pooling process on the local feature quantities extracted by each signal segment slice to obtain a pooled local feature quantity. The local feature quantity extracted from the signal segment slice is subjected to a maximum pooling process to obtain a pooled local feature quantity corresponding to the ECG lead signal as other dimensions of the global feature quantity for subsequent signal classification and screening. The max pooling process refers to calculating the maximum value of all the pooled feature quantities in each dimension, and using this maximum value as the value of the pooled feature quantity in the dimension.

Step S120: classify the corresponding ECG lead signals according to the maximum value of the integrated wave of the ECG lead signal and the localized feature quantity of the pool, and obtain the signal quality level of each ECG lead signal. By extracting the global eigenvalues of the respective ECG lead signals, the classification of all ECG lead signals can be completed, and the corresponding signal quality level can be obtained. The specific division of the signal quality level may be determined according to actual conditions. In this embodiment, the signal quality level includes two levels of excellent and fail, and in other embodiments, the signal quality level may be classified into three or more levels.

In one of the embodiments, as shown in FIG. 4, step S120 includes steps S122 to S126.

Step S122: Training the received global feature quantity training sample set by using a K-means algorithm to obtain a plurality of data clusters, and calculating a center position and a maximum radius of each data cluster.

The maximum radius is the distance between the data in the data cluster that is furthest from the center and the center position. Similarly, the signal quality level includes two levels of excellent and failing. The global feature quantity training sample set is a sample set obtained by extracting the global feature quantity of the historical ECG lead signal whose outstanding signal quality is excellent, and passes the K-means algorithm. (K-Means) is trained to obtain multiple data clusters as a classifier. Calculate the distance between the center position of each data cluster and the current cluster data farthest from the center position. The training process of the K-means algorithm is as follows: at initialization, the number of given clusters and the position of each cluster center are randomly set. Then repeat the following two steps: 1. Assign the feature quantity to the nearest cluster center. 2. Find the centroid of the feature quantity attributed to each cluster and set it as the new cluster center. If the number of executions of the two steps reaches a predetermined threshold, or the degree of change of the cluster center position is less than a predetermined threshold, the training is stopped and the current center position of each cluster is output. The sample training is performed by the K-means algorithm, and the classification accuracy is high.

Step S124: Allocating the global feature quantity of each ECG lead signal to the data cluster closest to the center position, and calculating the distance between the global feature quantity and the center position of the nearest data cluster. The global feature quantity of each ECG lead signal is respectively introduced into the classifier to be assigned to the data cluster closest to the center position, and the distance from the center position of the data cluster is calculated.

Step S126: Obtain a signal quality level of the corresponding ECG lead signal according to the distance between the global feature quantity and the center position of the nearest data cluster. It is determined whether the distance between the global feature quantity and the center position of the nearest data cluster is smaller than the maximum radius of the data cluster, and if so, the corresponding ECG lead signal is excellent; if not, the corresponding ECG lead signal is unqualified.

It can be understood that the manner of classifying the ECG lead signals will be different depending on the division of the signal quality levels. For example, when the signal quality level is three, the ECG lead signal can be classified into excellent, normal, and unqualified according to the distance between the global feature quantity and the center position of the nearest data cluster.

In addition, after step S120, before step S130, the ECG intelligent selection method may further include the following steps:

Determine whether the signal quality level is excellent or not. If yes, the ECG lead signal with excellent signal quality level is output as the optimal ECG lead signal; if not, the step is performed. S130. After calculating the signal quality level of each ECG lead signal, if there is only one excellent signal, it can be directly used as the optimal signal output, saving the calculation amount.

Step S130: extracting the maximum value of the integrated wave of the ECG lead signal of different signal quality levels and the localized feature quantity of the pooling to perform the training to obtain the quality classifier. Similarly, taking the signal quality level including excellent and unqualified as an example, after calculating the signal quality level of each ECG lead signal, the global feature quantity of some excellent and unqualified ECG lead signals is extracted and trained to obtain a quality classifier.

In the embodiment, step S130 is specifically: inputting a global feature quantity of the ECG lead signal of different signal quality levels to a support vector machine (SVM) for training, and obtaining a hyperplane as a quality classifier. The training process of the support vector machine is as follows: For the feature quantity of the ECG lead signal of different signal quality levels, the feature quantity located in the boundary area is regarded as the support vector of the signal of the type. After the support vector is mapped to the high-dimensional feature space by the kernel function, an optimal hyperplane between different kinds of support vectors is calculated, that is, the hyperplane with the largest sum of the spacings of different kinds of support vectors, the hyperplane Used as a quality classifier. The support vector machine algorithm is used to train the quality classifier, which can solve the machine learning problem in the small sample case and improve the generalization performance. The feature quantity can be mapped to the high-latitude kernel space to improve the classification accuracy. It can be understood that in other embodiments, other machine algorithms can also be sampled for training to obtain quality classifiers, such as decision trees, random forests, neural networks, and the like.

A machine learning algorithm is utilized to train the classifier. After setting the training parameters, the machine learning algorithm can automatically train the classifier on a large amount of data. The entire training process does not require user intervention, saving time and human resources.

Step S140: screening each ECG lead signal according to the quality classifier to obtain and output an optimal ECG lead signal. According to the trained quality classifier, all ECG signals are screened to identify a ECG lead signal with relatively better signal quality. The output of the optimal ECG lead signal may be sent to the display for display for observation, or may be sent to the memory for storage.

In one embodiment, step S140 specifically includes step 142 and step 144.

Step 142: Calculate the distance between the global feature quantity of the ECG lead signal and the hyperplane. Calculate the distance between the global feature quantity and the hyperplane from different ECG lead signals for the same time period.

Step 144: Filter the ECG lead signal according to the distance between the global feature quantity and the hyperplane. The optimal ECG lead signal is output and output. The specific method for screening the ECG lead signal according to the distance between the global feature quantity and the hyperplane is not unique, and may be sorted according to the distance from the largest to the smallest, and the ECG lead signal corresponding to the preset number of global feature quantities is extracted. As the optimal ECG lead signal, the ECG lead signal corresponding to the global feature quantity whose distance from the hyperplane is greater than the preset distance value may be directly extracted as the optimal ECG lead signal. In this embodiment, the ECG lead signal corresponding to the global feature quantity farthest from the hyperplane is used as the optimal ECG lead signal to ensure signal screening reliability.

In one embodiment, as shown in FIG. 5, after step S140, the ECG intelligent selection method further includes step S150.

Step S150: The gold standard is established by the method based on QRS detection, and the optimal ECG signal is tested.

When evaluating the screening effect, a gold standard of data is established to compare with the screening result obtained in step S140. In this embodiment, a gold standard is established based on the QRS detection method. The gold standard refers to the most reliable, accurate, and best diagnostic method for diagnosing diseases recognized by the current clinical medical community. First, the QRS detection algorithm is used to perform QRS detection on the ECG signals of each lead, and the position of the R wave is marked. Then, compared with the gold standard of QRS detection, the F1 value (F1score) of the QRS detection is calculated. If the F1 value is equal to 1 (the result of the detection is exactly the same as the gold standard), the signal quality of the lead is marked as excellent, and vice versa. When the ECG lead signal is selected, the lead with the highest F1 value is set as the ECG lead signal with the best signal quality.

It is checked whether the ECG lead signal obtained by the gold standard is the same as the optimal ECG lead signal obtained in step S140 to detect whether the signal screening is accurate. Since QRS detection is the basis for ECG signal analysis. The correctness of the QRS detection is directly related to the accuracy of the subsequent analysis. The QRS detection algorithm is robust to noise, and even if there is noise, it does not necessarily affect the detection effect. The gold standard established by the results of QRS detection can ensure the consistency of ECG signal quality and detection results. Establishing a data gold standard to evaluate the screening effect can not only improve the accuracy of classification, but also reduce the workload.

The above-mentioned ECG intelligent selection method is used to verify the data of a certain database. A total of 46 cases of ECG data, each paragraph has 2 leads, the length is 30 minutes. During the test, starting from the 0th second, the lead is selected every 5 seconds, and the data length selected each time is 5 seconds. Finally, QRS detection is performed on the selected lead data. The overall accuracy of lead selection is above 95%, and the accuracy of QRS detection The sensitivity rate is above 99.5%.

The above-mentioned ECG intelligent selection method extracts the global feature quantity of the ECG lead signal for signal classification and modeling and screening, and introduces the localized feature value of the pool to express the state of the signal, which can well reflect the signal at local time. The mutation is highly accurate. The whole process requires no personnel intervention, saving time and human resources. Signal quality judgment and lead selection can be completed before QRS detection, saving calculations.

The invention also provides an intelligent guiding system for ECG lead, which is suitable for screening ECG leads of instruments such as monitors and electrocardiographs. As shown in FIG. 6, the above system includes a feature extraction module 110, a signal classification module 120, a model training module 130, and a signal screening module 140.

The feature extraction module 110 is configured to perform feature extraction on the acquired ECG lead signals of the same time period to obtain global feature quantities of the respective ECG lead signals.

The ECG lead signals acquired during the same time period are acquired for feature extraction for use as a signal for screening. The global feature quantity includes the maximum value of the integrated wave of the ECG lead signal and the localized feature quantity of the pooling. The localized feature quantity of the pooling refers to the feature quantity obtained by calculating and pooling the local area of the signal. In one of the embodiments, as shown in FIG. 7, the feature extraction module 110 includes a first extraction unit 112, a second extraction unit 113, a first processing unit 114, a third extraction unit 115, and a second processing unit 116.

The first extracting unit 112 is configured to separately calculate an integrated wave of each ECG lead signal and extract a maximum value of the integrated wave. The ECG lead signal is processed to obtain an integrated wave, and the maximum value of the integrated wave is extracted as a dimension corresponding to the global feature quantity of the ECG lead signal.

The second extracting unit 113 is configured to separately extract a baseline signal and a high frequency noise signal of each ECG lead signal. The specific manner of extracting the baseline signal and the high frequency noise signal of each ECG lead signal is not unique, and may be selected according to actual conditions. In this embodiment, median filtering is used to extract the midline signal, and the Butterworth filter is used to extract the high frequency noise signal. . In other embodiments, the baseline signal may also be acquired by a low pass filter or other means, and the high frequency noise signal may be obtained by a Chebyshev filter or other means.

The first processing unit 114 is configured to perform slice processing on each ECG lead signal according to a preset length and a step size to obtain a plurality of signal segment slices. Slicing each ECG lead signal to obtain multiple signal segments sheet. The specific values of the preset length and the step size are not unique. In this embodiment, the preset length and the step size are 0.15 s and 0.05 s, respectively.

The third extracting unit 115 is configured to separately extract the height of the ECG lead signal on each slice segment, the height of the integrated wave, the height of the baseline signal, and the mean, variance, kurtosis and kurtosis of the high frequency noise signal as the signal segments. The local feature quantity of the slice.

The second processing unit 116 is configured to perform a maximum pooling process on the local feature quantities extracted by each signal segment slice to obtain a pooled local feature quantity. The local feature quantity extracted from the signal segment slice is subjected to a maximum pooling process to obtain a pooled local feature quantity corresponding to the ECG lead signal as other dimensions of the global feature quantity for subsequent signal classification and screening.

The signal classification module 120 is configured to classify the corresponding ECG lead signals according to the maximum value of the integrated wave of the ECG lead signal and the localized feature quantity of the pool, to obtain the signal quality level of each ECG lead signal. By extracting the global eigenvalues of the respective ECG lead signals, the classification of all ECG lead signals can be completed, and the corresponding signal quality level can be obtained. The specific division of the signal quality level may be determined according to actual conditions. In this embodiment, the signal quality level includes two levels of excellent and fail, and in other embodiments, the signal quality level may be classified into three or more levels.

In one of the embodiments, as shown in FIG. 8, the signal classification module 120 includes a first classification unit 122, a second classification unit 124, and a third classification unit 126.

The first classifying unit 122 is configured to train the received global feature quantity training sample set by using a K-means algorithm to obtain a plurality of data clusters, and calculate a center position and a maximum radius of each data cluster.

The maximum radius is the distance between the data in the data cluster that is furthest from the center and the center position. For example, the signal quality level includes two levels of excellent and failing. The global feature quantity training sample set is a sample set obtained by extracting the global feature quantity of the historical ECG lead signal with the confirmed signal quality as excellent. The K-means algorithm is trained to obtain multiple data clusters as a classifier. Calculate the distance between the center position of each data cluster and the current cluster data farthest from the center position. The sample training is performed by the K-means algorithm, and the classification accuracy is high.

The second classification unit 124 is configured to allocate the global feature quantity of each ECG lead signal to the data cluster closest to the center position, and calculate the distance between the global feature quantity and the center position of the nearest data cluster. The global feature quantity of each ECG lead signal is respectively introduced into the classifier to be assigned to the data cluster closest to the center position, and the distance from the center position of the data cluster is calculated.

The third classifying unit 126 is configured to obtain a signal quality level of the corresponding ECG lead signal according to the distance between the global feature quantity and the center position of the nearest data cluster. It is determined whether the distance between the global feature quantity and the center position of the nearest data cluster is smaller than the maximum radius of the data cluster, and if so, the corresponding ECG lead signal is excellent; if not, the corresponding ECG lead signal is unqualified.

The model training module 130 is configured to extract the maximum value of the integrated wave of the ECG lead signal of different signal quality levels and the localized feature quantity of the pool to perform the training to obtain the quality classifier. Similarly, taking the signal quality level including excellent and unqualified as an example, after calculating the signal quality level of each ECG lead signal, the global feature quantity of some excellent and unqualified ECG lead signals is extracted and trained to obtain a quality classifier.

In this embodiment, the model training module 130 extracts the maximum value of the integrated wave of the ECG lead signal of different signal quality levels and the localized feature quantity of the pool to obtain the quality classifier, specifically, the ECG lead signal of different signal quality levels. The global feature quantity is input to the support vector machine algorithm for training, and the hyperplane is obtained as the quality classifier.

The support vector machine algorithm is used to train the quality classifier, which can solve the machine learning problem in the small sample case and improve the generalization performance. The feature quantity can be mapped to the high-latitude kernel space to improve the classification accuracy.

In addition, after obtaining the signal quality level of each ECG lead signal, the signal classification module 120 can also be used to determine whether the ECG lead signal with excellent signal quality level is unique. If yes, the ECG lead signal with excellent signal quality level is output as the optimal ECG lead signal; if not, the control model training The training module 130 extracts the maximum value of the integrated wave of the ECG lead signal of different signal quality levels and the localized feature quantity of the pool to train the quality classifier. After calculating the signal quality level of each ECG lead signal, if there is only one excellent signal, it can be directly used as the optimal signal output, saving the calculation amount.

The signal screening module 140 is configured to filter each ECG lead signal according to the quality classifier, and obtain and output an optimal ECG lead signal. According to the trained quality classifier, all ECG signals are screened to identify a ECG lead signal with relatively better signal quality. The output of the optimal ECG lead signal may be sent to the display for display for observation, or may be sent to the memory for storage.

In one embodiment, the signal screening module 140 specifically includes a first screening unit and a second screening unit.

The first screening unit is configured to calculate the distance between the global feature quantity of the ECG lead signal and the hyperplane. Calculate the distance between the global feature quantity and the hyperplane from different ECG lead signals for the same time period.

The second screening unit is configured to filter the ECG lead signal according to the distance between the global feature quantity and the hyperplane to obtain an optimal ECG lead signal and output. The specific method for screening the ECG lead signal according to the distance between the global feature quantity and the hyperplane is not unique, and may be sorted according to the distance from the largest to the smallest, and the ECG lead signal corresponding to the preset number of global feature quantities is extracted. As the optimal ECG lead signal, the ECG lead signal corresponding to the global feature quantity whose distance from the hyperplane is greater than the preset distance value may be directly extracted as the optimal ECG lead signal. In this embodiment, the ECG lead signal corresponding to the global feature quantity farthest from the hyperplane is used as the optimal ECG lead signal to ensure signal screening reliability.

In one of the embodiments, as shown in FIG. 9, the ECG intelligent selection system can further include a signal verification module 150.

The signal checking module 150 is configured to filter the ECG lead signals according to the quality classifier after the signal screening module 140 obtains and outputs the optimal ECG lead signal, and then establishes a gold standard based on the QRS detection method to optimize the cardiac conductance. The joint signal is tested.

In the embodiment, the method for establishing a gold standard based on the QRS detection method and comparing the screening results is explained in detail in the above-mentioned ECG intelligent selection method, and details are not described herein again. Since QRS detection is the basis for ECG signal analysis. The correctness of the QRS detection is directly related to the accuracy of the subsequent analysis. The QRS detection algorithm is robust to noise even if noise is present It does not necessarily affect the effect of detection. The gold standard established by the results of QRS detection can ensure the consistency of ECG signal quality and detection results. Establishing a data gold standard to evaluate the screening effect can not only improve the accuracy of classification, but also reduce the workload.

The above-mentioned ECG intelligent selection system performs signal classification and modeling screening by extracting the global feature quantity of the ECG lead signal, and introduces the localized feature value of the pool to express the state of the signal, which can well reflect the signal at local time. The mutation is highly accurate. The whole process requires no personnel intervention, saving time and human resources. Signal quality judgment and lead selection can be completed before QRS detection, saving calculations.

The technical features of the above-described embodiments may be arbitrarily combined. For the sake of brevity of description, all possible combinations of the technical features in the above embodiments are not described. However, as long as there is no contradiction between the combinations of these technical features, All should be considered as the scope of this manual.

The above-described embodiments are merely illustrative of several embodiments of the present invention, and the description thereof is more specific and detailed, but is not to be construed as limiting the scope of the invention. It should be noted that a number of variations and modifications may be made by those skilled in the art without departing from the spirit and scope of the invention. Therefore, the scope of the invention should be determined by the appended claims.

Claims

An intelligent guiding method for ECG leads, characterized in that the method comprises the following steps:

Performing feature extraction on the acquired ECG lead signals of the same time period to obtain a global feature quantity of each of the ECG lead signals, the global feature quantity including a maximum value of the integrated wave of the ECG lead signal and pooling Local feature quantity

And classifying the corresponding ECG lead signals according to the maximum value of the integrated wave of the ECG lead signal and the localized feature quantity of the pool, to obtain a signal quality level of each of the ECG lead signals;

Extracting a maximum value of the integrated wave of the ECG lead signal of different signal quality levels and a localized feature quantity of the pool to obtain a quality classifier;

Each of the ECG lead signals is filtered according to the quality classifier to obtain and output an optimal ECG lead signal.
The ECG lead intelligent selection method according to claim 1, wherein the step of extracting the acquired ECG lead signals of the same time period to obtain the global feature quantity of each of the ECG lead signals is performed. Includes the following steps:

Calculating an integrated wave of each of the ECG lead signals separately, and extracting a maximum value of the integrated wave;

Extracting a baseline signal and a high frequency noise signal of each of the ECG lead signals;

And slicing each of the ECG lead signals according to a preset length and a step size to obtain a plurality of signal segment slices;

Extracting, respectively, a height of the ECG lead signal on each of the signal segment slices, a height of the integrated wave, a height of the baseline signal, and a mean value, a variance, a kurtosis, and a kurtosis of the high frequency noise signal as a local feature quantity of each of the signal segment slices;

The local feature quantity extracted by each of the signal segment slices is subjected to a maximum pooling process to obtain the pooled local feature quantity.
The ECG lead intelligent selection method according to claim 1, wherein the classification of the corresponding ECG lead signals is performed according to the maximum value of the integrated wave of the ECG lead signal and the localized feature quantity of the pooled The step of signal quality level of each of the ECG lead signals includes the following steps:

The received global feature quantity training sample set is trained by the K-means algorithm to obtain a plurality of data clusters, and the central position and the maximum radius of each of the data clusters are calculated, where the maximum radius refers to the farthest from the central position in the data cluster. The distance of the data from the central location;

Allocating a global feature quantity of each of the ECG lead signals to a data cluster closest to the central position, and calculating a distance between the global feature quantity and a center position of the nearest data cluster;

And obtaining a signal quality level of the corresponding ECG lead signal according to the distance between the global feature quantity and the center position of the nearest data cluster.
The ECG lead intelligent selection method according to claim 1, wherein the maximum value of the integrated wave of the ECG lead signal and the localized feature quantity of the pool are extracted for different signal quality levels, and the quality classifier is specifically obtained. In order to input the global feature quantity of the ECG lead signal of different signal quality levels to the support vector machine algorithm for training, a hyperplane is obtained as the quality classifier.
The ECG lead intelligent selection method according to claim 4, wherein the step of filtering the respective ECG lead signals according to the quality classifier to obtain and output an optimal ECG lead signal comprises The following steps:

Calculating a distance between the global feature quantity of the ECG lead signal and the hyperplane;

The ECG lead signal is filtered according to the distance between the global feature quantity and the hyperplane, and the optimal ECG lead signal is obtained and output.
The ECG lead intelligent selection method according to claim 1, wherein after the step of filtering the respective ECG lead signals according to the quality classifier to obtain and output an optimal ECG lead signal, It also includes the following steps:

The optimal ECG signal is tested by establishing a gold standard based on a QRS detection method.
An ECG lead intelligent selection system, comprising:

a feature extraction module, configured to perform feature extraction on the acquired ECG lead signals of the same time period, to obtain a global feature quantity of each of the ECG lead signals, where the global feature quantity includes an integrated wave of the ECG lead signal Maximum and pooled local feature quantities;

a signal classification module for using a maximum value of the integrated wave of the ECG lead signal and a pooled bureau The feature quantity is used to classify the corresponding ECG lead signals to obtain a signal quality level of each of the ECG lead signals;

a model training module, configured to extract a maximum value of the integrated wave of the ECG lead signal of different signal quality levels and a localized feature quantity of the pool to obtain a quality classifier;

The signal screening module is configured to filter each of the ECG lead signals according to the quality classifier to obtain and output an optimal ECG lead signal.
The ECG lead intelligent selection system according to claim 7, wherein the feature extraction module comprises:

a first extracting unit, configured to separately calculate an integrated wave of each of the ECG lead signals, and extract a maximum value of the integrated wave;

a second extracting unit, configured to respectively extract a baseline signal and a high frequency noise signal of each of the ECG lead signals;

a first processing unit, configured to perform slice processing on each of the ECG lead signals according to a preset length and a step size to obtain a plurality of signal segment slices;

a third extracting unit, configured to respectively extract a height of the ECG lead signal on each of the signal segment slices, a height of the integrated wave, a height of the baseline signal, and an average value and a variance of the high frequency noise signal, Kurtosis and kurtosis, as local feature quantities of each of the signal segment slices;

And a second processing unit, configured to perform a maximum pooling process on the local feature quantities extracted by each of the signal segment slices to obtain the pooled local feature quantity.
The ECG lead intelligent selection system according to claim 7, wherein the signal classification module comprises:

a first classifying unit, configured to perform training on the received global feature quantity training sample set by using a K-means algorithm to obtain a plurality of data clusters, and calculate a center position and a maximum radius of each of the data clusters, where the maximum radius refers to a data cluster The distance of the data furthest from the central location from the central location;

a second classifying unit, configured to allocate a global feature quantity of each of the ECG lead signals to a data cluster that is closest to the center position, and calculate a distance between the global feature quantity and a center position of the nearest data cluster;

a third classifying unit, configured to: according to the distance between the global feature quantity and the center position of the nearest data cluster A signal quality level corresponding to the ECG lead signal is obtained.
The ECG intelligent selection system according to claim 7, further comprising a signal verification module, wherein the signal verification module is configured to: in the signal screening module, each of the ECG leads according to the quality classifier After the signal is filtered, and the optimal ECG lead signal is obtained and output, the gold standard is established by the QRS detection method, and the optimal ECG signal is tested.