Name
Title | ||
|---|---|---|
Automatic Detection of ECG Abnormalities by Using an Ensemble of Deep Residual Networks with Attention. |
Abstract | ||
|---|---|---|
Heart disease is one of the most common diseases causing morbidity and mortality. Electrocardiogram (ECG) has been widely used for diagnosing heart diseases for its simplicity and non-invasive property. Automatic ECG analyzing technologies are expected to reduce human working load and increase diagnostic efficacy. However, there are still some challenges to be addressed for achieving this goal. In this study, we develop an algorithm to identify multiple abnormalities from 12-lead ECG recordings. In the algorithm pipeline, several preprocessing methods are firstly applied on the ECG data for denoising, augmentation and balancing recording numbers of variant classes. In consideration of efficiency and consistency of data length, the recordings are padded or truncated into a medium length, where the padding/truncating time windows are selected randomly to suppress overfitting. Then, the ECGs are used to train deep neural network (DNN) models with a novel structure that combines a deep residual network with an attention mechanism. Finally, an ensemble model is built based on these trained models to make predictions on the test data set. Our method is evaluated based on the test set of the First China ECG Intelligent Competition dataset by using the F1 metric that is regarded as the harmonic mean between the precision and recall. The resultant overall F1 score of the algorithm is 0.875, showing a promising performance and potential for practical use. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-33327-0_11 | MLMECH/CVII-STENT@MICCAI |
Field | DocType | Citations |
Residual,F1 score,Pattern recognition,Computer science,Precision and recall,Preprocessor,Artificial intelligence,Test data,Overfitting,Artificial neural network,Test set | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yang Liu | 1 | 194 | 17.42 |
| Runnan He | 2 | 7 | 4.84 |
| Kuanquan Wang | 3 | 1617 | 141.11 |
| Qince Li | 4 | 7 | 9.91 |
| Qiang Sun | 5 | 0 | 0.34 |
| Na Zhao | 6 | 37 | 16.03 |
| Henggui Zhang | 7 | 105 | 51.88 |