Short Term ECG Classification with Residual-Concatenate Network and Metric Learning
| Autor: | Yuwen Huang, Xinjing Song, Yilong Yin, Feng Yuan, Gongping Yang, Kuikui Wang |
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| Rok vydání: | 2020 |
| Předmět: |
Computer Networks and Communications
Computer science business.industry 020207 software engineering Pattern recognition 02 engineering and technology Residual Convolutional neural network Term (time) ComputingMethodologies_PATTERNRECOGNITION Hardware and Architecture Metric (mathematics) 0202 electrical engineering electronic engineering information engineering Media Technology ComputerSystemsOrganization_SPECIAL-PURPOSEANDAPPLICATION-BASEDSYSTEMS Artificial intelligence business Software |
| Zdroj: | Multimedia Tools and Applications. 79:22325-22336 |
| ISSN: | 1573-7721 1380-7501 |
| Popis: | ECG classification is important to the diagnosis of cardiovascular disease. This paper develops a robust and accurate algorithm for automatic detection of heart arrhythmias from ECG signals recorded with one lead. A novel model based on the convolutional neural network is proposed to extract low-level and high-level features of short term ECG. In addition, Information-Theoretic Metric Learning is utilized as a final classification model to boost the discrimination abilities of the network trained features. The experimental results over the MIT-BIH arrhythmia database show that the model achieves a comparable performance with most of the state-of-the-art methods and Information-Theoretic Metric Learning further improves the performance. Besides the good accuracy achieved, the proposed method balances different criteria. |
| Databáze: | OpenAIRE |
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