US-20260123874-A1 - ARRHYTHMIA CLASSIFICATION METHOD USING ECG SIGNALS, APPARATUS THEREOF, DEVICE AND MEDIUM

US20260123874A1US 20260123874 A1US20260123874 A1US 20260123874A1US-20260123874-A1

Abstract

Provided is an arrhythmia classification method using ECG signals, an apparatus thereof, a device and a medium. The method includes: acquiring ECG data and general patient data; constructing the first and second BERT models; inputting the ECG data and the general patient data into the first and second BERT models to obtain the first and second semantic information vector; constructing the first and second tower layers; inputting the first and second semantic information vectors into the first and second tower layers respectively, to obtain the first and second feature vectors; calculating a correlation coefficient based on the first and second feature vectors; carrying out feature combination on the first and second feature vectors and the correlation coefficient to obtain a combined feature; constructing a classification model; and inputting the combined feature into the classification model to obtain the category of arrhythmia classification.

Inventors

Meihua Piao
Hongzhen Cui
Yunfeng Peng
HAOMING MA
Aoqi WANG
Xingyi TANG
Sijia Li
Longhao Zhang
Hefei HAO

Assignees

PEKING UNION MEDICAL COLLEGE
UNIVERSITY OF SCIENCE AND TECHNOLOGY BEIJING

Dates

Publication Date: 20260507
Application Date: 20250116
Priority Date: 20241101

Claims (9)

1 . An arrhythmia classification method using ECG signals, wherein the arrhythmia classification method using the ECG signals comprises: acquiring Electrocardiograph (ECG) data and general patient data; constructing a first Bidirectional Encoder Representations from Transformers (BERT) model and a second BERT model; inputting the ECG data and the general patient data into the first BERT model and the second BERT model to obtain a first semantic information vector and a second semantic information vector; constructing a first tower layer and a second tower layer; inputting the first semantic information vector and the second semantic information vector into the first tower layer and the second tower layer, respectively, to obtain a first feature vector and a second feature vector; calculating a correlation coefficient based on the first feature vector and the second feature vector; carrying out feature combination on the first feature vector, the second feature vector and the correlation coefficient to obtain a combined feature; constructing a classification model; and inputting the combined feature into the classification model to obtain a category of arrhythmia classification.
2 . The arrhythmia classification method using the ECG signals according to claim 1 , wherein the general patient data comprises an age, a sex, a height and a weight; the ECG data comprises general ECG data and lead data distribution, the general ECG data comprises QRS duration, P-R interval, P-T interval, T interval, P interval, QRS wave, T wave, P wave, QRST wave and J point; and the lead data distribution comprises a wave width and an amplitude.
3 . The arrhythmia classification method using the ECG signals according to claim 1 , wherein the first tower layer is any one of Long Short-Term Memory (LSTM), Gate Recurrent Unit (GRU), Bidirectional Long Short-Term Memory (BiLSTM), Bidirectional Gate Recurrent Unit (BiGRU), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Transformer and Attention; the second tower layer is any one of LSTM, GRU, BILSTM, BIGRU, CNN, RNN, Transformer and Attention.
4 . The arrhythmia classification method using the ECG signals according to claim 1 , wherein the correlation coefficient is calculated based on the first feature vector and the second feature vector using any one of methods comprising cosine similarity, Euclidean distance, Jaccard similarity coefficient, Pearson correlation coefficient, Manhattan distance, Spearman's rank correlation coefficient, angular similarity, cosine distance and Chebyshev distance.
5 . The arrhythmia classification method using the ECG signals according to claim 1 , wherein the correlation coefficient is calculated based on the first feature vector and the second feature vector using a following formula: cos ⁢ θ = ∑ i = 1 n ( v i × μ i ) ∑ i = 1 n ( v i ) 2 × ∑ i = 1 n ( μ i ) 2 ⁢ or ⁢ cos ⁢ θ = V 1 ⁢  ⁢ V 2  V 1  ×  V 2  K = cos ⁢ θ wherein V 1 ={v 1 , v 2 , . . . , v n } represents the first feature vector, and V 2 ={μ 1 , μ 2 , . . . , μ n } represents the second feature vector.
6 . The arrhythmia classification method using the ECG signals according to claim 1 , wherein an expression of the classification model is: f ⁡ ( x ) = ω T ⁢ φ ⁡ ( x ) ? b = sign ⁡ ( ? K ⁡ ( x i , y j ) + b ) ? indicates text missing or illegible when filed where ω represents a parameter matrix, i.e. a weight vector, T represents transposition calculation, φ(x) represents a nonlinear mapping function that maps input features into a high-dimensional space, b represents a bias term, n represents a number of patients, i represents an i-th patient, y i represents a classification category of the i-th patient with arrhythmia, y j represents a classification category of a j-th patient with arrhythmia, α i represents a Lagrange multiplier corresponding to an i-th training sample, and K(x i , y j ) represents a kernel function, which is used to calculate similarity between input features.
7 . An arrhythmia classification apparatus using ECG signals, wherein the arrhythmia classification apparatus using the ECG signals comprises: a data acquiring module, configured to acquire Electrocardiograph (ECG) data and general patient data; a BERT model constructing module, configured to construct a first BERT model and a second BERT model; a semantic information vector determining module, configured to input the ECG data and the general patient data into the first BERT model and the second BERT model to obtain a first semantic information vector and a second semantic information vector; a tower layer constructing module, configured to construct a first tower layer and a second tower layer; a feature vector determining module, configured to input the first semantic information vector and the second semantic information vector into the first tower layer and the second tower layer, respectively, to obtain a first feature vector and a second feature vector; a correlation coefficient calculating module, configured to calculate a correlation coefficient based on the first feature vector and the second feature vector; a feature combining module, configured to carry out feature combination on the first feature vector, the second feature vector and the correlation coefficient to obtain a combined feature; a classification model constructing module, configured to construct a classification model; and a classifying module, configured to input the combined feature into the classification model to obtain a category of arrhythmia classification.
8 . A computer device, comprising: a memory, a processor and a computer program which is stored in the memory and is executable on the processor, wherein the processor executes the computer program to implement steps of the arrhythmia classification method using the ECG signals according to any one of claim 1 .
9 . A non-transitory computer-readable storage medium on which a computer program is stored, wherein the computer program, when executed by a processor, implements steps of the arrhythmia classification method using the ECG signals according to any one of claim 1 .

Description

CROSS-REFERENCE TO RELATED APPLICATION This patent application claims the benefit and priority of Chinese Patent Application No. 2024115540862 filed with the China National Intellectual Property Administration on Nov. 1, 2024, the disclosure of which is incorporated by reference herein in its entirety as part of the present disclosure. TECHNICAL FIELD The present disclosure relates to the field of arrhythmia classification, in particular to an arrhythmia classification method using Electrocardiograph (ECG) signals, an apparatus thereof, a device and a medium. BACKGROUND With the introduction and development of computer neural networks and large models, various cardiovascular diseases are constantly being judged by the application of models such as deep learning, and certain research results have been achieved. However, it is difficult to classify and diagnose individual differences resulted from specific types of arrhythmia diseases. It is difficult to make regular judgments on the potential features of data through human beings, which is limited by deep learning and the generalization ability and accuracy of existing models in arrhythmia diseases. The existing main techniques of diagnosing arrhythmia using ECG signals include traditional methods and machine learning-based methods. The traditional methods include manual analysis, rule or threshold definition, etc. Manual analysis needs manual interpretation from a cardiologist. By observing the shapes, the intervals and the amplitudes of P wave, QRS complex and T wave, screening and diagnosis are carried out according to experience. Manual analysis is time-consuming and labor-consuming, and relies on expert experience. There may be subjective errors or misjudgments. Manual analysis is not suitable for large-scale screening or diagnosis. When judging R-R interval, the method based on rules and thresholds automatically judges whether arrhythmia exists in the P-R interval. Obviously, in the case of individual differences, the thresholds or rules are inaccurate and lack of adaptability, which cannot ensure the processing of complex arrhythmia and cannot ensure the accuracy and robustness. According to the machine learning-based methods, features such as heart rate variability, waveform morphological features, waveform variation features, etc. are first extracted from ECG signals, and then the arrhythmia is classified and diagnosed by combining with classification algorithms, such as a support vector machine, a decision tree, a random forest, etc. The most serious defect of the process is that the process relies on experts in the field of cardiology, the sensitivity of feature selection and model performance to data distribution needs a lot of labeled data to be trained, and there is weak robustness to the diagnosis and judgment of abnormal unlabeled data. Therefore, the present disclosure provides an arrhythmia classification method using ECG signals, an apparatus thereof, a device, a medium and a product. SUMMARY The purpose of the present disclosure is to provide an arrhythmia classification method using ECG signals, an apparatus thereof, a device, a medium and a product, which can improve the accuracy of arrhythmia classification. In order to achieve the above purposes, the present disclosure provides the following solution. In a first aspect, the present disclosure provides an arrhythmia classification method using ECG signals, including: acquiring Electrocardiograph (ECG) data and general patient data;constructing a first Bidirectional Encoder Representations from Transformers (BERT) model and a second BERT model;inputting the ECG data and the general patient data into the first BERT model and the second BERT model to obtain a first semantic information vector and a second semantic information vector;constructing a first tower layer and a second tower layer;inputting the first semantic information vector and the second semantic information vector into the first tower layer and the second tower layer, respectively, to obtain a first feature vector and a second feature vector;calculating a correlation coefficient based on the first feature vector and the second feature vector;carrying out feature combination on the first feature vector, the second feature vector and the correlation coefficient to obtain a combined feature;constructing a classification model; andinputting the combined feature into the classification model to obtain a category of arrhythmia classification. Preferably, the general patient data includes an age, a sex, a height and a weight; the ECG data includes general ECG data and lead data distribution, the general ECG data includes QRS duration, P-R interval, P-T interval, T interval, P interval, QRS wave, T wave, P wave, QRST wave and J point; and the lead data distribution includes a wave width and an amplitude. Preferably, the first tower layer is any one of Long Short-Term Memory (LSTM), Gate Recurrent Unit (GRU), Bidirectional Long Shor