LSTM-Based ECG Classification for Continuous Monitoring on Personal Wearable Devices

<italic>Objective</italic>: A novel electrocardiogram (ECG) classification algorithm is proposed for continuous cardiac monitoring on wearable devices with limited processing capacity. <italic>Methods</italic>: The proposed solution employs a novel architecture consisting of wavelet transform and multiple long short-term memory (LSTM) recurrent neural networks (see <xref ref-type="fig" rid="fig1">Fig. 1</xref>). <italic>Results</italic>: 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. <italic>Conclusion</italic>: 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. <italic>Significance</italic>: The proposed algorithm is both accurate and lightweight. The source code is available online at <uri>http://lis.ee.sharif.edu</uri>.

• Publication year

  2018

• Venue

  IEEE journal of biomedical and health informatics

• Publication date

  2018-12-12

• Fields of study

  Medicine, Computer Science, Engineering

• Identifiers
  DOI 10.1109/JBHI.2019.2911367arXiv 1812.04818PMID 30990452
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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