A Novel Wearable Electrocardiogram Classification System Using Convolutional Neural Networks and Active Learning
| Autor: | Yaoqin Xie, Yufa Xia |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
General Computer Science
Heartbeat Active learning (machine learning) Computer science Feature extraction Wearable computer 02 engineering and technology Convolutional neural network active learning convolutional neural networks 0202 electrical engineering electronic engineering information engineering medicine General Materials Science cardiovascular diseases medicine.diagnostic_test business.industry 020208 electrical & electronic engineering General Engineering Pattern recognition Electrocardiogram Support vector machine cardiovascular system 020201 artificial intelligence & image processing Artificial intelligence lcsh:Electrical engineering. Electronics. Nuclear engineering business Electrocardiography lcsh:TK1-9971 |
| Zdroj: | IEEE Access, Vol 7, Pp 7989-8001 (2019) |
| ISSN: | 2169-3536 |
| Popis: | Arrhythmias reflect electrical abnormalities of the heart, and they can lead to severe harm to the heart. An electrocardiogram (ECG) is a useful tool to manifest arrhythmias. In this paper, we present an automatic system using a convolutional neural network and active learning to classify ECG signals. To improve the model performance, breaking-ties (BT) and modified BT algorithms are utilized in the active learning. We classify ECG signals in five heartbeat types, i.e., normal (N), ventricular (V), supraventricular (S), fusion of normal and ventricular (F), and unknown heartbeats (Q), using the Association for the Advancement of Medical Instrumentation standard. Our experiments are performed on the MIT-BIH arrhythmia database. To further verify the generalization capability of the system, the ECG data that acquired from our wearable device are also used to conduct in the experiments. Compared with most of the state-of-the-art methods, the obtained results demonstrate that the presented method promotes the classification performance remarkably. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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