The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Cardiac arrhythmia detection using deep learning
Open accessAbstract
An electrocardiogram (ECG) is an important diagnostic tool for the assessment of cardiac arrhythmias in clinical routine. In this study, a deep learning framework previously trained on a general image data set is transferred to carry out automatic ECG arrhythmia diagnostics by classifying patient ECG’s into corresponding cardiac conditions. Transferred deep convolutional neural network (namely AlexNet) is used as a feature extractor and the extracted features are fed into a simple back propagation neural network to carry out the final classification. Three different conditions of ECG waveform are selected from MIT-BIH arrhythmia database to evaluate the proposed framework. Main focus of this study is to implement a simple, reliable and easily applicable deep learning technique for the classification of the selected three different cardiac conditions. Obtained results demonstrated that the transferred deep learning feature extractor cascaded with a conventional back propagation neural network were able to obtain very high performance rates. Highest obtained correct recognition rate is 98.51% while obtaining testing accuracy around 92%. Based on these results, transferred deep learning proved to be an efficient automatic cardiac arrhythmia detection method while eliminating the burden of training a deep convolutional neural network from scratch providing an easily applicable technique.
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Metadata
- Authors
- Isin, A., & Ozdalili, S.
- Venue
- Procedia Computer Science
- Publisher
- Elsevier BV
- Published
- 2017
- Volume
- 120
- Pages
- 268-275
- ISSN
- 1877-0509 (ISSN-L)
- OpenAlex
- W2771170013
- Cited by
- 267
- References
- 10
- FWCI
- 34.78
- Type
- conference paper
- Language
- en
- Open access
- diamond
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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