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CHU JINGHUI, WANG HONG, LU WEI. A NOVEL TWO-LEAD ARRHYTHMIA CLASSIFICATION SYSTEM BASED ON CNN AND LSTM. J MECH MED BIOL 2019. [DOI: 10.1142/s0219519419500040] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Arrhythmia classification is useful during heart disease diagnosis. Although well-established for intra-patient diagnoses, inter-patient arrhythmia classification remains difficult. Most previous work has focused on the intra-patient condition and has not followed the Association for the Advancement of Medical Instrumentation (AAMI) standards. Here, we propose a novel system for arrhythmia classification based on multi-lead electrocardiogram (ECG) signals. The core of the design is that we fuse two types of deep learning features with some common traditional features and select discriminating features using a binary particle swarm optimization algorithm (BPSO). Then, the feature vector is classified using a weighted support vector machine (SVM) classifier. For a better generalization of the model and to draw fair comparisons, we carried out inter-patient experiments and followed the AAMI standards. We found that, when using common metrics aimed at multi-classification either macro- or micro-averaging, our system outperforms most other state-of-the-art methods.
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Affiliation(s)
- JINGHUI CHU
- Electrical Automation and Information Institute, Tianjin University, Tianjin 300072/Zone, P. R. China
| | - HONG WANG
- Electrical Automation and Information Institute, Tianjin University, Tianjin 300072/Zone, P. R. China
| | - WEI LU
- Electrical Automation and Information Institute, Tianjin University, Tianjin 300072/Zone, P. R. China
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