컨볼루션 뉴럴 네트워크 기반 부정맥 분류 네트워크 설계
Cardiac Arrhythmia Classification Network using Convolutional Neural Network
- 주제(키워드) 12-lead Electrocardiogram (ECG) , multi-label arrhythmia classes , deep learning , convolutional neural network (CNN) , Arrhythmias classification
- 주제(DDC) 621.381
- 발행기관 아주대학교 일반대학원
- 지도교수 이정원
- 발행년도 2024
- 학위수여년월 2024. 8
- 학위명 박사
- 학과 및 전공 일반대학원 전자공학과
- 실제URI http://www.dcollection.net/handler/ajou/000000033865
- 본문언어 한국어
- 저작권 아주대학교 논문은 저작권에 의해 보호받습니다.
초록/요약
An electrocardiogram (ECG) is a non-invasive, inexpensive, and widely used diagnostic tool for arrhythmia diagnosis in clinics. Deep learning techniques have shown great promise in ECG signal analysis, enabling automatic and accurate detection of various cardiac arrhythmia. This paper proposes an automated multi-label cardiac arrhythmia classification network based on a convolutional neural network (CNN). The network aims to detect and classify 45 cardiac arrhythmia classes using 12-lead ECG data. Unlike previous studies, our approach incorporates the residual structure and channel attention mechanism. Thus, we developed two key schemes to improve classification performance: the Global Channel Attention Block (GCAB) and the Short Residual Block (SRB). The GCAB incorporates dilated convolutions to preserve overall features. It focuses on the important characteristics of each arrhythmia class from the original electrocardiogram data during the training process. The SRB employs a residual structure to enhance classification accuracy. The network’s performance is evaluated using a large-scale 12-lead electrocardiogram database for arrhythmia study on PhysioNet and the 2018 China Physiological Signal Challenge (CPSC) dataset. In particular, the proposed classification network shows the highest scores in average precision, recall, F1 score, area under the receiver operating characteristic, and accuracy compared to existing CNN-based arrhythmia classification networks in a large-scale 12-lead electrocardiogram database for arrhythmia study on PhysioNet. Finally, to evaluate the performance of the proposed classification network, we compared our proposed network with widely known classification networks such as VGGNet, ResNet, SENet, MobileNet, and EfficientNet. The proposed network demonstrates superior performance compared to other well-known classification networks. We validate the proposed arrhythmia classification network through confusion matrix and AUROC curve.
more목차
I. Introduction 1
II. Overview of Convolutional Neural Networks 7
A. LeNet 7
B. AlexNet 10
C. GoogleNet 13
D. VGGNet 17
E. ResNet 20
F. SENet 24
G. UNet 26
H. MobileNet 29
I. EfficientNet 32
III. Related Works 35
A. Arrhythmia Classification using ML and Classifier 35
B. Arrhythmia Classification using RNN 35
C. CNN-based Arrhythmia Classification using 12-lead ECG data 37
IV. Proposed Method 45
A. The Proposed Classification Network Architecture to Classify 45 Cardiac Arrhythmia Classes 45
B. Global Channel Attention Block (GCAB) 51
C. Short Residual Block (SRB) 54
V. Experimental Results and Comparisons 57
A. Datasets Description 57
B. Data Preprocessing Method 63
C. Implementation Details 66
D. Evaluation Criteria 68
E. Implementation Results using PhysioNet Dataset 69
F. Implementation Results using CPSC 2018 Dataset 79
G. Implementation Results on Classification Networks using PhysioNet Dataset 85
VI. Discussion 88
VII. Conclusion 90
Bibliography 91
