  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].
