The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
LSTM-Based ECG Classification for Continuous Monitoring on Personal Wearable Devices
Open accessAbstract
OBJECTIVE: A novel electrocardiogram (ECG) classification algorithm is proposed for continuous cardiac monitoring on wearable devices with limited processing capacity. METHODS: The proposed solution employs a novel architecture consisting of wavelet transform and multiple long short-term memory (LSTM) recurrent neural networks (see Fig. 1). RESULTS: Experimental evaluations show superior ECG classification performance compared to previous works. Measurements on different hardware platforms show the proposed algorithm meets timing requirements for continuous and real-time execution on wearable devices. CONCLUSION: In contrast to many compute-intensive deep-learning based approaches, the proposed algorithm is lightweight, and therefore, brings continuous monitoring with accurate LSTM-based ECG classification to wearable devices. SIGNIFICANCE: The proposed algorithm is both accurate and lightweight. The source code is available online at http://lis.ee.sharif.edu.
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Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review
Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., & Raad, A. (2023) · Sensors · doi:10.3390/s23020828 · Cited by 120
Shumba, A.-T., Montanaro, T., Sergi, I., Bramanti, A., Ciccarelli, M., Rispoli, A., Carrizzo, A., De Vittorio, M., & Patrono, L. (2023) · Sensors · doi:10.3390/s23156896 · Cited by 74
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Lee, K.-S., Park, H.-J., Kim, J. E., Kim, H. J., Chon, S., Kim, S., Jang, J., Kim, J.-K., Jang, S., Gil, Y., et al. (2022) · Sensors · doi:10.3390/s22051776 · Cited by 36
Metadata
- Authors
- Saadatnejad, S., Oveisi, M., & Hashemi, M.
- Venue
- IEEE Journal of Biomedical and Health Informatics
- Published
- 2020-02
- Volume
- 24
- Issue
- 2
- Pages
- 515-523
- ISSN
- 2168-2194 (ISSN-L)
- 2168-2208
- OpenAlex
- W2951632519
- Cited by
- 406
- References
- 41
- FWCI
- 29.18
- Type
- journal article
- Language
- en
- Open access
- green
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