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

Saeed Saadatnejad; Mohammadhosein Oveisi; Matin Hashemi

doi:10.1109/jbhi.2019.2911367

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arXiv · 1812.04818

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

Saeed Saadatnejad·Mohammadhosein Oveisi·Matin Hashemi

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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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BibTeX RIS

Saeed Saadatnejad, Mohammadhosein Oveisi, Matin Hashemi. 2018-12-12. LSTM-Based ECG Classification for Continuous Monitoring on Personal Wearable Devices. https://doi.org/10.1109/jbhi.2019.2911367

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