Automatic Identification of Abnormalities in 12-lead ECGs Using Expert Features and Convolutional Neural Networks

被引：23
|
作者：
Liu, Zhongdi [1 ]
Meng, Xiang'ao [1 ]
Cui, Jiajia [1 ]
Huang, Zhipei [1 ]
Wu, Jiankang [1 ]
机构：
[1] Univ Chinese Acad Sci, Sch Elect Elect & Commun Engn, Beijing, Peoples R China
来源：
2018 INTERNATIONAL CONFERENCE ON SENSOR NETWORKS AND SIGNAL PROCESSING (SNSP 2018) | 2018年
关键词：
component; ECG classification; cardiac abnormalities; deep learning; Convolutional Neural Network;
D O I：
10.1109/SNSP.2018.00038
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Automatic identification of the rhythm/morphology abnormalities in ECGs has gained growing attention in various areas and remains a challenge. We propose an algorithm to classify 12-lead ECGs into 9 categories. We extracted expert features including generic features and specific features with statistics and physiology significance. Then a 17-layer Convolutional Neural Network (CNN) was proposed to detect deep features in ECGs. With these features we trained ensemble classifiers to predict labels. Experiment on the training set (5-fold cross-validation) reports 0.81 accuracy score.
引用
收藏
页码：163 / 167
页数：5
相关论文
共 50 条
  • [41] A novel method based on convolutional neural networks for deriving standard 12-lead ECG from serial 3-lead ECG
    Lu-di WANG
    Wei ZHOU
    Ying XING
    Na LIU
    Mahmood MOVAHEDIPOUR
    Xiao-guang ZHOU
    FrontiersofInformationTechnology&ElectronicEngineering, 2019, 20 (03) : 405 - 413
  • [42] A novel method based on convolutional neural networks for deriving standard 12-lead ECG from serial 3-lead ECG
    Wang, Lu-di
    Zhou, Wei
    Xing, Ying
    Liu, Na
    Movahedipour, Mahmood
    Zhou, Xiao-guang
    FRONTIERS OF INFORMATION TECHNOLOGY & ELECTRONIC ENGINEERING, 2019, 20 (03) : 405 - 413
  • [43] A novel method based on convolutional neural networks for deriving standard 12-lead ECG from serial 3-lead ECG
    Lu-di Wang
    Wei Zhou
    Ying Xing
    Na Liu
    Mahmood Movahedipour
    Xiao-guang Zhou
    Frontiers of Information Technology & Electronic Engineering, 2019, 20 : 405 - 413
  • [44] Detecting acute myocardial infarction in the 12-lead ECG using Hermite expansions and neural networks
    Haraldsson, H
    Edenbrandt, L
    Ohlsson, M
    ARTIFICIAL INTELLIGENCE IN MEDICINE, 2004, 32 (02) : 127 - 136
  • [45] Automatic Attention Learning Using Neural Architecture Search for Detection of Cardiac Abnormality in 12-Lead ECG
    Liu, Zuhao
    Wang, Huan
    Gao, Yibo
    Shi, Shunchen
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2021, 70
  • [46] Convolutional Recurrent Neural Network and LightGBM Ensemble Model for 12-lead ECG Classification
    Zisou, Charilaos
    Sochopoulos, Andreas
    Kitsios, Konstantinos
    2020 COMPUTING IN CARDIOLOGY, 2020,
  • [47] Classifying Different Dimensional ECGs Using Deep Residual Convolutional Neural Networks
    Cai, Wenjie
    Liu, Fanli
    Wang, Xuan
    Xu, Bolin
    Wang, Yaohui
    2021 COMPUTING IN CARDIOLOGY (CINC), 2021,
  • [48] Detection of cardiac abnormalities from 12-lead ecg using complex wavelet sub-band features
    Mondal, Sourav
    Choudhary, Prakash
    Rathee, Priyanka
    BIOMEDICAL PHYSICS & ENGINEERING EXPRESS, 2024, 10 (03)
  • [49] Automatic Schizophrenia Identification Using Multi-Domain Features With Convolutional Neural Network
    Cai, Xiyu
    Pan, Huang
    Liu, Xiaobo
    Lai, Yongxiu
    INTERNATIONAL JOURNAL OF PSYCHOPHYSIOLOGY, 2021, 168 : S129 - S130
  • [50] 12-lead ECG Arrythmia Classification Using Convolutional Neural Network for Mutually Non-Exclusive Classes
    Solinski, Mateusz
    Lepek, Michal
    Pater, Antonina
    Muter, Katarzyna
    Wiszniewski, Przemyslaw
    Kokosinska, Dorota
    Salamon, Judyta
    Puzio, Zuzanna
    2020 COMPUTING IN CARDIOLOGY, 2020,
12345