Year: 2019
Type: article
Abstract: 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.
Authors: Saeed Saadatnejad, Mohammadhosein Oveisi, Matin Hashemi
Institution: Sharif University of Technology
Language: English
Topic: ECG Monitoring and Analysis
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: green
Funder: Iran National Science Foundation
Award: G295344715
OpenAlex ID: W2951632519