KR102417949B1 - Artificial Intelligence Based ECG Analysis and Application for Automated External Defibrillator - Google Patents

Abstract

The present invention relates to an artificial intelligence-based automatic defibrillator electrocardiogram analysis and application method, which is to provide a technology in which electric shock is provided only to a patient who needs the electric shock by accurately determining shock and no-shock. That is, the method of the present invention includes: a step (S1) of collecting ECG data and storing the data in an ECG data storage; a step (S2) of dividing the stored ECG data into pieces to create a data set; a secondary labeling step (S3) of first labeling each piece of the data set and recording whether there was heart shock; a step of (S4) removing noise; a step (S5) of analyzing the ECG signal and extracting features; an ECG machine learning step (S6) of performing deep learning; a step (S7) of conducting an ECG machine learning test; a step (S8) of evaluating artificial intelligence-based ECG analysis performance; and a step (S9) of equally applying the ECG analysis artificial intelligence model, digital filter, and ECG feature extraction algorithm to the automatic defibrillator.

Description

Artificial Intelligence Based ECG Analysis and Application for Automated External Defibrillator

The present invention relates to an AI-based ECG analysis and application method for an automatic defibrillator, and applies and analyzes a model for increasing the accuracy of the ECG analysis technology built into the automatic defibrillator by applying and analyzing the shock and non-shock It relates to a technology that allows the judgment of (No-Shock) to be made accurately so that only the patients who need it can receive the electric shock.

In general, an automatic defibrillator (or Automated External Defibrillator, AED) is a device that automatically removes cardiac fibrillation, which is essential for vital activity as blood flow to each tissue is interrupted due to an arrhythmia patient with an irregular heartbeat or when the heartbeat stops. It is a device that restores normal heartbeat by giving an electric shock from the outside when sudden cardiac arrest (SCA: Sudden Cardiac Arrest), which is a phenomenon in which the supply of oxygen is interrupted, occurs.

In the process of using the automatic defibrillator, the patient's electrocardiogram (ECG) is measured through a pad attached to the patient's body, and the device decides whether to select Shock or No-Shock for the patient. Thus, an electric shock is made.

Therefore, accurate judgment is required because what is judged by the AED determines whether or not an electric shock is present. There was a problem with losing.

However, the automatic defibrillators so far judged only by the electrocardiogram rule-based algorithm when analyzing the electrocardiogram, so the judgment accuracy was lowered. It is a structure that cannot be expected much.

Registered Patent No. 10-2143705 (Attachment guide system for automatic defibrillators and a guide method for attaching the same)

The present invention has been devised to solve the above problems, and the electrocardiogram analysis of the automatic defibrillator is actually applied through machine learning using deep learning, shock of the automatic defibrillator. The purpose of this study is to provide an electrocardiogram analysis and application method for an AI-based automatic defibrillator that allows an electric shock to be delivered only to patients in need of an electric shock by accurately determining and no-shock.

The present invention provides a machine learning computer device comprising an ECG data storage, a first ECG feature extracting unit, and an ECG machine learning unit, and an automatic defibrillator connected thereto and comprising an electrocardiogram measuring unit, a second ECG feature extracting unit, and an ECG artificial intelligence engine. In the AI-based electrocardiogram analysis and application method for an automatic defibrillator of Creating a step (S2), performing primary labeling in the form of recording the type of cardiac rhythm for each piece of the data set, and a second labeling step (S3) of recording whether or not a heart shock is present for each of the first labeled pieces; removing noise by applying a digital filter to the labeled data set (S4), analyzing the ECG signal from the data set to which the digital filter is applied and extracting features (S5); ECG machine learning step (S6) of performing deep learning with a supervised learning method and deep neural network (DNN) by using the extracted learning data set as an input value and setting the secondary labeled heart shock as a classification result, and the ECG Using an artificial intelligence model such as neural network structure, activation function, and weight created through machine learning, the test data set from which the ECG signal characteristics are extracted is an input value, and the secondary labeled cardiac shock is set as the classification result. Conducting an ECG machine learning test (S7), and evaluating the AI-based ECG analysis performance using the digital filter, the extraction after analysis of the ECG signal, the ECG machine learning test result, and the primary labeled heart rhythm data set (S8) and the ECG analysis artificial intelligence model including the neural network structure, activation function, weight, and shock determination threshold finally selected through the evaluated AI-based ECG analysis performance evaluation, digital filter, and ECG feature extraction algorithm automatically It is characterized in that it consists of; step (S9) of applying the same to the defibrillator.

In addition, the ECG data collected in the step (S1) of storing the ECG data storage is characterized in that it consists of the electronically synthesized ECG data made by synthesizing the electronic signal with the actual ECG data obtained from the patient.

In addition, in the step (S1) of storing the ECG data storage, the ECG data is changed to the same sampling frequency as the electrocardiogram measuring unit of the automatic defibrillator and stored.

In addition, in the step (S2) of creating a data set by dividing it into pieces, the ECG artificial intelligence engine of the AED divides the data into pieces that can be analyzed.

In addition, in the step (S2) of creating a data set by dividing the pieces into pieces, the data set is a supervised learning method, characterized in that it consists of a learning data set for deep learning and a test data set for a machine learning test.

In addition, in the labeling step (S3), the primary labeling includes a total of 5 types of heart rhythm: VF: ventricular fibrillation, VT: ventricular tachycardia, NSR: normal heartbeat, ASYS: asystole, OTHER: all heartbeats that cannot be shocked. It is characterized in that it is classified and labeled according to the corresponding heart rhythm.

In addition, the secondary labeling in the labeling step (S3) is characterized by labeling whether shock is shock or NO-SHOCK: non-shock according to the heart rhythm type and combination of the primary labeling.

In addition, after inputting the test data set in the step (S7) of conducting the ECG machine learning test, if the AI-VALUE, the output value of the ECG artificial intelligence model, is greater than or equal to the shock judgment threshold, AI-SHOCK determines the shock and If it is less than the shock determination threshold, AI-NO-SHOCK is characterized in that the artificial intelligence determines the non-shock.

In addition, in the step (S8) of evaluating the AI-based ECG analysis performance, C-DF is the number of combinations of digital filters, C-FE is the number of combinations of extracted ECG features, and C-ML is the number of combinations of ECG AI models. When the total number of machine learning combinations to be performed according to the judgment threshold is the number of C-DF × C-FE × C-ML ECG machine learning tests, the optimal It is characterized in that the combination is selected.

In addition, in the step (S8) of evaluating the AI-based ECG analysis performance, according to the matching combination of the shock label and the ECG AI model result, true positive (TP), true negative (TN), false positive ( False Positive. FP) and false negative (False Negative. FN) are characterized.

In addition, in the step (S8) of evaluating the AI-based ECG analysis performance, if the shock label is SHOCK and the ECG AI model result is AI-SHOCK, it is true positive (TP), and the shock label is SHOCK, and the ECG AI model result is If the AI-NO-SHOCK is false negative (FN), if the shock label is NO-SHOCK and the ECG AI model result is AI-NO-SHOCK, it is true negative (TN), and the shock label is NO-SHOCK, but the ECG AI model result If is AI-SHOCK, it is characterized in that it is determined as false positive (FP).

The present invention extracts cardiac rhythm characteristics through ECG data and performs supervised machine learning using the extracted ECG signal characteristics and data sets, so that the actual applied shock and ratio of the automatic defibrillator It has the effect of ensuring that the shock (No-Shock) is accurately judged and that only the patient who needs the electric shock is given an electric shock.

1 is a diagram showing the connection configuration of a computer device for machine learning and an automatic defibrillator in the method for analyzing and applying an electrocardiogram for an artificial intelligence-based automatic defibrillator according to the present invention;
2 is a view showing an electrocardiogram analysis and application method for an artificial intelligence-based automatic defibrillator of the present invention;
3 is a flowchart showing the process of an electrocardiogram analysis and application method for an artificial intelligence-based automatic defibrillator according to the present invention;
4 is a flowchart showing a deep learning method in the present invention

Hereinafter, preferred embodiments of the present invention will be described in detail. In the description of the present invention, if it is determined that a detailed description of a related known function or configuration may unnecessarily obscure the gist of the present invention, the detailed description thereof will be omitted.

The present invention provides a computer device 100 for machine learning comprising an ECG data storage 110, a first ECG feature extraction unit 120, and an ECG machine learning unit 130, and an electrocardiogram measuring unit 210 connected thereto, and a second ECG feature This is a technology related to an AI-based ECG analysis and application method for an AI-based automatic defibrillator in a system consisting of an automatic defibrillator 200 consisting of an extraction unit 220 and an ECG artificial intelligence engine 230 .

The computer device for machine learning 100 includes an ECG data storage 110 in which various ECG data is stored, a first ECG feature extraction unit 120 that extracts ECG features through a specific algorithm, and deep using data for learning and testing. It consists of an ECG machine learning unit 130 that performs running.

In addition, the automatic defibrillator 200 includes an electrocardiogram measuring unit 210 that measures an actual patient's electrocardiogram, a second ECG feature extracting unit 220 that extracts ECG characteristics through an already input algorithm, and ECG machine learning generated through It is composed of an ECG artificial intelligence engine 230 that analyzes ECG characteristics based on the ECG artificial intelligence model and determines whether or not the heart is in shock.

The present invention is a technology that performs machine learning in a computer device for machine learning using ECG data stored in ECG data storage, and then applies an algorithm and ECG analysis artificial intelligence model, which are the final result, to an automatic defibrillator. The process takes place in a computer device for machine learning. The computer device for machine learning may include a mechanical device capable of performing an automatic calculation function, such as a notebook computer, a desktop computer, a supercomputer, a server, and a personal terminal.

In addition, the computer device for machine learning consists of a single configuration so that all processes can be performed, but since it is configured in plurality, different computer devices for machine learning can process each step.

As shown in FIG. 2, the specific method of the present invention includes the steps of collecting ECG data and storing it in an ECG data storage of a computer device for machine learning (S1), and creating a data set by dividing the stored ECG data into pieces (S2) and a secondary labeling step (S3) of recording the type of cardiac rhythm for each piece of the data set, and recording whether or not the heart is in shock for each of the first labeled pieces (S3), and the labeling removing noise by applying a digital filter to the data set (S4), extracting features by analyzing the ECG signal from the data set to which the digital filter is applied (S5), and extracting the features of the ECG signal ECG machine learning step (S6) of performing deep learning with a supervised learning method and deep neural network (DNN) by using a training data set as an input value and setting the secondary labeled heart shock as a classification result, and the ECG machine learning Using an artificial intelligence model such as neural network structure, activation function, and weight created through Conducting a learning test (S7) and evaluating the AI-based ECG analysis performance using the digital filter, the extraction after analysis of the ECG signal, the ECG machine learning test result, and the primary labeled heart rhythm data set (S8) ) and an ECG analysis AI model including the neural network structure, activation function, weight, and shock determination threshold finally selected through the evaluated AI-based ECG analysis performance evaluation, digital filter, and ECG feature extraction algorithm to the automatic defibrillator The same applies to (S9); characterized in that it consists of.

The ECG data collected in the step (S1) of storing the ECG data storage consists of electronically synthesized ECG data made by synthesizing the actual ECG data and electronic signals obtained from the patient as shown in FIG. 3 .

Here, the actual ECG data can be applied to the ECG data collected from real patients in a hospital, etc. and the ECG data collected through preclinical trials. Exclude ECG data for which it is impossible to determine whether an impact is present due to excessive noise or data with saturated ECG signals because they cannot be used for machine learning.

Since the ECG data collected in this way has different sampling frequency, data format, and signal unit, it must be converted to the same standard as the ECG measurement unit of the AED to be applied. That is, it is converted to the same sampling frequency as the analog-digital converter of the automatic defibrillator to which the machine learning model is to be applied, and the ECG signal is converted into nV unit integer.

As described above, the ECG data collected from the patient is the same as the ECG signal that is actually input when the AED analyzes the heart rhythm, so it is suitable as learning data and test data. Shock) is insufficient to learn the boundaries. Therefore, in order to learn the boundary between shock and no-shock, the ECG signal is created and synthesized to further create the synthesized electronic ECG data.

The electronically synthesized ECG data synthesizes signals by variously combining a waveform of a heart rhythm, a heart rate (beats per minute, BPM), and an amplitude.

The waveform of the heart rhythm is Coarse VFib, Fine VFib, Poly VTach, Mono VTach, Normal Sinus, Asystole, Multifocal PVCs, PVC1 LV R On T, PVC1 LV Early, PVC1 Left Vent, PVC2 RV R On T, PVC2 RV Early, It is created by combining the waveform characteristics of PVC2 Right Vent, Nodal PNC, AFib, SupraVTach, Nodal Rhythm, Paroxys-mal ATach, ATach, Missed Beat, Sinus Ar-rhythmia, and Atrial Flutter.

And the amplitude range is 0.1~0.5 mV (△0.05 mV), 1~3.5 mV (△0.5 mV), and the heart rate range is 120~300 BPM (△5 BPM) for Mono VTach, and 10~360 for Normal Sinus. It consists of BPM (Δ5 BPM). (where △ means an increase value)

In the step (S2) of creating a data set by dividing the fragments into pieces, the actual ECG data that has undergone the conversion process and the newly created electronically synthesized ECG data are divided into small units that can be analyzed by the ECG artificial intelligence engine of the AED. will make

Since the machine learning of the present invention is applied as a supervised learning type, in the step (S2) of creating a data set by dividing it into pieces, the data set is a learning data set required for deep learning and a test for machine learning test separated into data sets.

The training data set uses all of the electronically synthesized ECG data and a part of the collected ECG data. At this time, when composing the training data set, ECG data with an ECG signal amplitude of less than 0.1 mV is excluded.

And the test data set is used in the step (S7) of performing the ECG machine learning test.

The labeling step (S3) of the present invention consists of primary labeling for recording the type of heart rhythm and secondary labeling for recording whether or not the heart is in shock. The primary and secondary labeling is to label each ECG fragment of each data set, and primary and secondary labeling is performed to test the performance of machine learning and cardiac rhythm analysis of the training data set.

In the labeling step (S3), the primary labeling corresponding to the heart rhythm is set through annotation recorded in the ECG database and analysis by an expert. In other words, the primary labeling is VF: ventricular fibrillation, VT: ventricular tachycardia, NSR: normal heartbeat, ASYS: asystole, OTHER: all non-shockable heartbeats. Select and label.

The OTHER items are: Premature ventricular contractions, Supraventricular tachycardia, sinus bradycardia, Atrial fibrillation/atrial flutter, 2nd/3rd degree atrioventricular block ( Second or third-degree heart block), Accelerated Junctional Rhythm, and Pacemaker Rhythm.

An ECG fragment with an ECG signal amplitude of less than 0.1 mV in the data set is labeled ASYS. And among the ECG pieces labeled with VF and VT, if the heart rate is slow and non-shock, it is labeled as OTHER.

And in the labeling step (S3), the secondary labeling is to label the shock with SHOCK or NO-SHOCK according to the heart rhythm type and combination of the primary labeling. That is, in the cardiac rhythm of the ECG fragment, if shock is required, it is labeled as shock (SHOCK), and if shock is not required, it is labeled as non-shock (NO-SHOCK). For example, VF, VT: SHOCK is labeled, or NSR, ASYS, OTHER: NO-SHOCK is labeled.

A digital filter is applied to the labeled data set through the step (S4) of removing the noise. The digital filter is a filter that removes noise from the ECG signal and allows the signal to be smoothly analyzed. The application sequence of the digital filter is as follows.

First, it converts an nV-class integer ECG signal into an mV-class floating-point in the data set. Second, a band-pass filter is applied to remove baseline drift noise and power-line interference, and to easily analyze ECG signals. Third, if necessary, a moving average filter may be applied.

The result of machine learning varies according to the digital filter combination according to the frequency of the data set, the filter order, and the type of digital filter to be applied. Therefore, it is necessary to select the optimal digital filter combination through machine learning evaluation, and to obtain the same performance, it is applied to the AED in the same way as the selected digital filter combination.

In the step (S5) of analyzing the ECG signal and extracting the features, it is a process of analyzing the ECG signal from the data set to which the digital filter is applied to perform feature extraction. In order to extract the features, heart rate, conduction, stability of the rhythm, and amplitude of the ECG signal may be used as features. Features can be extracted by analyzing the ECG signal using Fast Fourier Transform (FFT) or Wavelet Transform. In addition, it can be extracted by statistical methods such as mean, standard deviation, and kurtosis of ECG signal characteristics.

In addition, the analysis method and algorithm combination used for feature extraction are equally applied to the AED.

Then, it goes through the ECG machine learning step (S6) of performing deep learning with the supervised learning method and deep neural network (DNN). In general, supervised learning uses learning data unlike unsupervised learning. It is a deep learning method that, for example, informs (or learns) the meaning of each data in advance and finds out the meaning of newly input data.

In deep learning, ECG feature extraction is input in the step (S5) of extracting features by analyzing the ECG signal, and SHOCK and NO-SHOCK, which are shock labels, are set as classification results.

Deep learning constructs a deep neural network (DNN) as a sequential model. The input layer takes the data of ECG feature extraction as input. The hidden layer uses a hyperbolic tangent function to solve nonlinear data as an activation function, and a softmax function that transforms a vector into a probability distribution is used. The output layer is an activation function and uses a sigmoid function that converts a value between 0 and 1.

The input layer of the deep neural network model consists of the same nodes as the ECG feature extraction, and the output layer consists of one node. ECG feature extraction data is [FE(1), FE(2), … , FE(n)], the input node is [L1(1), L1(2), ... , L1(n)]. Here, n means the total number of ECG feature extraction data. The hidden layer consists of three layers, L2, L3, and L4, and each layer consists of n2, n3, and n4 nodes. And we use hyperbolic tangent and soft max as activation functions. Hidden layers can be added to improve ECG analysis performance. The output layer consists of one node and uses a sigmoid function.

If the ECG machine learning model is constructed using ECG feature extraction in this way, the number of nodes and hidden layers of the deep neural network model decreases, and the ECG artificial intelligence engine of the AED can be made with fewer resources.

When machine learning is performed in this way, the ECG machine learning test is performed (S7). Test data from which the ECG signal features are extracted using the ECG artificial intelligence model such as the neural network structure, activation function, and weight created through machine learning. ECG machine learning is tested by taking the set as an input and setting the secondary labeled cardiac shock as the classification result.

The ECG machine learning test first inputs the ECG feature values of the test data set into the ECG artificial intelligence model. The input ECG feature value passes through the neural network node according to the learned deep neural network structure, activation function, and weight, and then goes through the sigmoid function of the output layer to output an AI-VALUE value between 0 and 1. As the AI-VALUE value approaches 1, it approaches impact, and as it approaches 0, it approaches non-impact.

Finally, impact and non-impact are determined by the AI-VALUE threshold. If the AI-VALUE exceeds the threshold, the AI-SHOCK AI judges it as a shock, and if the AI-VALUE does not exceed the threshold, the AI-NO-SHOCK AI judges it as a non-shock.

In this way, since the determination of whether or not an impact is impacted depends on the threshold, the ECG machine learning test should be performed according to the combination of the thresholds. Therefore, in the step (S7) of performing the ECG machine learning test, C-DF is the number of combinations of digital filters, C-FE is the number of combinations of extracted ECG features, and C-ML is the impact in the ECG artificial intelligence model. When it is the total number of machine learning combinations to be performed according to the threshold value, ECG machine learning tests are performed as many as C-DF × C-FE × C-ML. And among the results, the optimal combination that can be applied to the AED is to be selected.

Then, the AI-based ECG analysis performance is evaluated (S8). The above step is a combination of an ECG artificial intelligence model such as a neural network structure, activation function, weight, etc. created through algorithms such as digital filter and ECG feature extraction and ECG machine learning and a threshold value that is a criterion for determining an impact by artificial intelligence. In order to select an ECG analysis AI model for

In the step of evaluating the AI-based ECG analysis performance (S8), the evaluation of algorithms such as digital filters and ECG feature extraction algorithms using the ECG machine learning test results and the primary labeled heart rhythm data set, the neural network structure, and the activation function It evaluates the performance of the ECG analysis AI model such as , weight, and shock judgment threshold. True positive (TP) and True Negative (True Negative) according to the matching combination of the shock label and the ECG AI model result. TN), false positive (FP), and false negative (FN).

The true positive (TP) is a case in which an impulse heart rhythm is accurately determined, and a true negative (TN) is a case in which a non-impulsive heart rhythm is accurately determined. In addition, false positive (FP) is a case in which a shock heart rhythm is incorrectly determined in a situation where no shock is required, and a false negative (FN) is a case in which a non-shock cardiac rhythm is incorrectly determined in a situation in which shock is required.

That is, the step (S8) of evaluating the AI-based ECG analysis performance is to determine the matching combination of the shock label and the machine learning model result in four forms. Specifically, the shock label is SHOCK and the ECG AI model result is AI-SHOCK, true positive (TP), the shock label is SHOCK, and the ECG artificial intelligence model result is AI-NO-SHOCK, false negative (FN), the shock label is NO-SHOCK, and the ECG artificial intelligence model result is AI- If NO-SHOCK is true negative (TN), if the shock label is NO-SHOCK, but the ECG AI model result is AI-SHOCK, it is judged as false positive (FP).

For the AED, the specificity of the rhythm that does not require a shock is more important than the sensitivity of the rhythm that requires a shock. Sensitivity and specificity are calculated as follows using the determination result. Preferably, the sensitivity (Sensitivity) is calculated by the TP / (TP + FN) equation, and the specificity (Specificity) is calculated by the TN / (FP + TN) equation.

In international standards, the sensitivity and specificity of the AED is required as follows. Sensitivity of ventricular fibrillation (VF) is greater than 90%, sensitivity of ventricular tachycardia (VT) is greater than 75%, specificity of normal heartbeat (NSR) is greater than 99%, and asystole (Asys) ) Specificity is required to be 95% or more, and the specificity of all heartbeats that cannot give a shock is 95%.

Then, the ECG analysis artificial intelligence model combination to be applied to the AED is selected using the heart rhythm criterion according to the heart rhythm label. That is, for example, if the heart rhythm label is VF, the shock label is SHOCK, and the ECG analysis AI model result is AI-SHOCK, then it is judged as TP-VF. If the model result is AI-SHOCK, it is determined as TP-VT, and if the shock label is NO-SHOCK among the heart rhythm label NSR and the ECG analysis AI model result is AI-NO-SHOCK, it is determined as TN-NSR, and cardiac rhythm If the shock label is NO-SHOCK in ASYS and the ECG analysis AI model result is AI-NO-SHOCK, it is determined as TN-ASYS, and the heart rhythm label is NO-SHOCK in OTHER, and the shock label is NO-SHOCK, ECG analysis AI If the model result is AI-NO-SHOCK, it is judged as TN-OTHER.

In the above judgment, TP-VF and TP-VT are the same as Sensitivity, and TN-NSR, TN-ASYS, TN-OTHER are the same as Specificity

Then, a combination of ECG analysis AI models that satisfy the following criteria is selected from among the heart rhythm labels of the electronically synthesized ECG data set.

- TP-VF = 1

- TP-VT = 1

- TN-NSR = 1

- TN-ASYS = 1

- TN-OTHER > 0.95

Among the selected ECG analysis AI model combinations, a combination of ECG analysis AI models satisfying the following criteria is selected from among the heart rhythm labels of the test data set.

- TP-VF > 0.95

- TP-VT > 0.95

- TN-NSR > 0.99

- TN-ASYS > 0.99

- TN-OTHER > 0.95

Then, from among the selected ECG analysis artificial intelligence model combinations, a combination applicable to the AED is selected. If the desired performance is not obtained, performance improvement is required, or the label is modified, data set creation is performed again.

Finally, the present invention automatically automates the ECG analysis artificial intelligence model, digital filter, and ECG feature extraction algorithm including the neural network structure, activation function, weight, and shock determination threshold finally selected through the evaluated AI-based ECG analysis performance evaluation. A step of applying the same to the defibrillator (S9); is passed.

That is, the neural network structure, activation function, weight, and shock determination threshold of the ECG analysis artificial intelligence model finally selected by the ECG machine learning unit are provided to the ECG artificial intelligence engine of the AED, and the digital filter of the first ECG feature extraction unit The ECG feature extraction algorithm provides the digital filter conditions and ECG feature extraction algorithm as the second ECG feature extraction unit of the AED. The judgment of No-Shock is made accurately.

Although the present invention has been described above with reference to the above embodiments, it goes without saying that various modifications are possible within the scope of the technical spirit of the present invention.

S1 ~ S9: ECG analysis and application method for artificial intelligence-based automatic defibrillator
100: computer device for machine learning 110: ECG data storage
120: first ECG feature extraction unit 130: ECG machine learning unit
200: automatic defibrillator 210: electrocardiogram measurement unit
220: second ECG feature extraction unit 230: ECG artificial intelligence engine

Claims (11)

Machine learning computer device consisting of ECG data storage, first ECG feature extracting unit, and ECG machine learning unit, and artificial intelligence of a system consisting of an electrocardiogram measuring unit, second ECG feature extracting unit, and ECG artificial intelligence engine connected thereto. In the electrocardiogram analysis and application method for the based automatic defibrillator,
Collecting ECG data and storing it in the ECG data storage of the computer device for machine learning (S1);
Creating a data set by dividing the stored ECG data into pieces (S2);
A secondary labeling step (S3) of performing primary labeling in the form of recording the type of heart rhythm for each piece of the data set, and recording whether or not a heart shock is present for each of the first labeled pieces;
removing noise by applying a digital filter to the labeled data set (S4);
extracting features by analyzing the ECG signal from the data set to which the digital filter is applied (S5);
ECG machine learning step of performing deep learning with a supervised learning method and deep neural network (DNN) by using the learning data set from which the characteristics of the ECG signal are extracted as an input value, and setting the secondary labeled heart shock as the classification result ( S6) and
Using an artificial intelligence model such as neural network structure, activation function, and weight created through the ECG machine learning, the test data set from which the characteristics of the ECG signal are extracted is an input value, and the secondary labeled cardiac shock is classified as the result. Setting and performing ECG machine learning test (S7),
Evaluating the AI-based ECG analysis performance using the digital filter, the extraction after analyzing the ECG signal, the ECG machine learning test result, and the primary labeled heart rhythm data set (S8);
The ECG analysis AI model including the neural network structure, activation function, weight, and shock determination threshold finally selected through the evaluated AI-based ECG analysis performance evaluation, digital filter, and ECG feature extraction algorithm are applied equally to the AED. applying step (S9); ECG analysis and application method for artificial intelligence-based automatic defibrillator
The method of claim 1,
ECG data collected in the step (S1) of storing in the ECG data storage consists of electronically synthesized ECG data made by synthesizing actual ECG data and electronic signals obtained from the patient. How to apply
The method of claim 1,
In the step (S1) of storing the ECG data in the storage, the ECG data is changed to the same sampling frequency as the ECG measuring unit of the AED and stored.
The method of claim 1,
In the step (S2) of creating a data set by dividing the data into pieces, the ECG AI engine of the automatic defibrillator divides the data into pieces that can be analyzed.
The method of claim 1,
In the step (S2) of creating a data set by dividing the pieces into pieces, the data set is an artificial intelligence-based automatic heart shock, characterized in that it consists of a learning data set for deep learning and a test data set for machine learning test by a supervised learning method. Electrocardiogram analysis and application method
The method of claim 1,
In the labeling step (S3), the primary labeling is performed on a total of five types of heart rhythm, such as VF: ventricular fibrillation, VT: ventricular tachycardia, NSR: normal heartbeat, ASYS: asystole, OTHER: all heartbeats that cannot be shocked. ECG analysis and application method for artificial intelligence-based automatic defibrillator, characterized in that classification, selection and labeling according to the corresponding cardiac rhythm
7. The method of claim 6,
In the labeling step (S3), the secondary labeling is an artificial intelligence-based electrocardiogram for an automatic defibrillator, characterized in that the shock is labeled as SHOCK: shock or NO-SHOCK: non-shock according to the cardiac rhythm type and combination of the primary labeling. Analysis and application methods
The method of claim 1,
After inputting the test data set in the step (S7) of performing the ECG machine learning test, if AI-VALUE, the output value of the ECG artificial intelligence model, is greater than or equal to the shock determination threshold, AI-SHOCK determines that it is a shock and shock Method for analyzing and applying an electrocardiogram for an AI-based automatic defibrillator, characterized in that if it is less than the judgment threshold, AI-NO-SHOCK determines that it is non-shock
The method of claim 1,
In the step (S8) of evaluating the AI-based ECG analysis performance, C-DF is the number of combinations of digital filters, C-FE is the number of combinations of extracted ECG features, and C-ML is determined whether the ECG AI model is impacted. When the number of machine learning combinations to be performed is the total number of machine learning combinations to be performed according to the threshold to be ECG analysis and application method for artificial intelligence-based automatic defibrillator, characterized in that selecting
The method of claim 1,
In the step (S8) of evaluating the AI-based ECG analysis performance, according to the matching combination of the shock label and the ECG AI model result, true positive (TP), true negative (TN), false positive (False) FP) and False Negative (False Negative. FN)
11. The method of claim 10,
In the step (S8) of evaluating the AI-based ECG analysis performance, if the shock label is SHOCK and the ECG AI model result is AI-SHOCK, it is true positive (TP), and the shock label is SHOCK, and the ECG AI model result is AI -NO-SHOCK is false negative (FN), if the shock label is NO-SHOCK, if the ECG AI model result is AI-NO-SHOCK, it is true negative (TN) ECG analysis and application method for artificial intelligence-based automatic defibrillator, characterized in that if it is AI-SHOCK, it is determined as false positive (FP)

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