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

2018/12/31 by Saeed Saadatnejad, Mohammadhosein Oveisi, Matin Hashemi · 1 citation
Engineering · Computer Science · #eess.SP #cs.HC #cs.NE

paper · pdf · doi:10.1109/jbhi.2019.2911367

published as IEEE Journal of Biomedical and Health Informatics (JBHI), Vol. 24, No. 2, February 2020 · Accepted for publication in IEEE Journal of Biomedical and Health Informatics (J-BHI)

arxiv created 2019/05/11 · arxiv updated 2020/01/22

Abstract

Objective: A novel 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 LSTM recurrent neural networks. 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 [1].

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