期刊
2017 COMPUTING IN CARDIOLOGY (CINC)
卷 44, 期 -, 页码 -出版社
IEEE COMPUTER SOC
DOI: 10.22489/CinC.2017.350-114
关键词
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Classification of Atrial Fibrillation from diverse electro-cardiographic (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.
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