Does an LSTM network using QRS complex features accurately classify atrial fibrillation from ECG signals?
A lightweight LSTM network using QRS complex features provides an accurate solution for classifying atrial fibrillation from ECG signals.
Classification of Atrial Fibrillation from diverse electrocardiographic (ECG) signals is the challenging objective of the 2017 Physionet Challenge. We suggest a Long Short Term Memory (LSTM) network, which learns patterns directly from pre-computed QRS complex features that classifies ECG signals. Although our architecture is considered deep, it only consists of 1791 parameters. The result is an accurate, lightweight solution that classifies ECG records as Normal, Atrial fibrillation, Other or Too noisy with final challenge score of 0.78.
Maknickas et al. (Thu,) studied this question.
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