Research On Arrhythmia Diagnosis Method Based On Multimodal Learning

The high incidence and mortality rate of cardiovascular diseases have attracted public attention.Among them,cardiac arrhythmia as a common symptom of cardiovascular disease,accurate diagnosis is very important,which can effectively prevent the occurrence of cardiovascular diseases.However,manual diagnosis of cardiac arrhythmia has problems such as missed diagnosis,misdiagnosis,and low efficiency.Therefore,developing an intelligent cardiac arrhythmia diagnostic algorithm has important research value and social significance.Currently existing intelligent diagnostic algorithms for cardiac arrhythmia mainly rely on analysis of electrocardiogram(ECG)data.However,the information included in ECG data offers a limited perspective on a patient’s health status,which may have some limitations in practical applications compared to the complete diagnostic process performed by medical professionals.In this study,Multimodal Learning technology was used to comprehensively analyze multiple examination data of patients,including electrocardiogram,biochemical examination,personal signs information and echocardiogram data,in order to achieve a more accurate diagnosis of patients with arrhythmia.The main work of this thesis can be divided into the following aspects.To address the issue of the lack of open-source multimodal datasets for cardiac arrhythmia,a retrospective study was conducted to create a multi-modal dataset of cardiac arrhythmia using de-identified electronic health records from hospitals.This dataset includes electrocardiograms,echocardiogram reports,biochemical examination data,and physical sign information.In the context of constructing a multimodal dataset for arrhythmia,there exists a high noise problem with respect to electrocardiogram(ECG)data.To address this issue,we propose a waveform extraction method based on generative adversarial networks,where the generator network extracts ECG waveforms directly from raw ECG data in an end-to-end manner.The machine learning method based on supervised learning needs to label the data set.It is a timeconsuming and laborious work to label the ECG waveform in the ECG data,and the precision of data annotation will affect the training effect of the subsequent model.To reduce reliance on ECG data annotation,we employ simulation data generation to generate synthetic data for model training.Compared to the baseline algorithm,our proposed method improves the Dice similarity coefficient and the Jaccard index from 0.851 and 0.741 to 0.879 and 0.786,respectively,achieving the average level of human annotation.Additionally,the proposed method does not require preprocessing of the ECG data and leads to significant improvements in ECG waveform extraction speed relative to the baseline approach.To address the challenge of diagnosing cardiac arrhythmias,we propose a cardiac arrhythmia diagnosis model based on multimodal learning.Deep learning models are used to extract deep features from different types of examination data for patients.Through feature fusion,the extracted features are combined in a multimodal way to achieve automated diagnosis of arrhythmia.Compared with the existing studies,the proposed model can synthesize more patient information and achieve more accurate diagnosis effect.Through experimental validation and analysis,our proposed multimodal diagnosis model demonstrates significant improvements in F1 score,precision,and recall on the test set.Furthermore,an ablation study is conducted to verify the effectiveness of multimodal learning in arrhythmia diagnosis.