A DenseNet-based diagnosis algorithm for automated diagnosis using clinical ECG data.
10.12122/j.issn.1673-4254.2019.01.11
- Author:
Jiewei LAI
1
;
Yundai CHEN
2
;
Baoshi HAN
2
;
Lei JI
2
;
Yajun SHI
2
;
Zhicong HUANG
3
;
Wei YANG
1
;
Qianjin FENG
1
Author Information
1. School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
2. Department of Cardiology, Chinese PLA General Hospital, Beijing 100853, China.
3. Cardiocloud Medical Technology (Beijing) Co., Ltd., Beijing 100094, China.
- Publication Type:Journal Article
- Keywords:
ECG data preprocessing;
densely connected convolutional network;
depth-wise separable convolutions;
signal framing
- MeSH:
Algorithms;
Arrhythmias, Cardiac;
diagnosis;
Databases as Topic;
Electrocardiography;
classification;
methods;
Humans;
Neural Networks (Computer);
Sensitivity and Specificity
- From:
Journal of Southern Medical University
2019;39(1):69-75
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE:To train convolutional networks using multi-lead ECG data and classify new data accurately to provide reliable information for clinical diagnosis.
METHODS:The data were pre-processed with a bandpass filter, and signal framing was adopted to adjust the data of different lengths to the same size to facilitate network training and prediction. The dataset was expanded by increasing the sample size to improve the detection rate of abnormal samples. A depth-wise separable convolution structure was used for more specific feature extraction for different channels of twelve-lead ECG data. We trained the two classifiers for each label using the improved DenseNet to classify different labels.
RESULTS:The propose model showed an accuracy of 80.13% for distinguishing between normal and abnormal ECG with a sensitivity of 80.38%, a specificity of 79.91% and a F1 score of 79.35%.
CONCLUSIONS:The model proposed herein can rapidly and effectively classify the ECG data. The running time of a single dataset on GPU is 33.59 ms, which allows real-time prediction to meet the clinical requirements.
