Compression of ECG Signals Using Long Short-Term Memory based Sequence-to-Sequence Autoencoder - Aydemir, Gürkan; Bekiryazıcı, Tahir; Gürkan, Hakan (Institute of Electrical and Electronics Engineers Inc., 2020)
This study proposes a novel long short-term memory based sequence-to-sequence autoencoder model to compress ECG signals. The efficiency of this new method is illustrated on MIT-BIH Arrhythmia dataset. In the conducted experiments, the proposed architecture achieves %21.14 mean-independent percentage mean square difference (MPRD) with a constant compression ratio value of 33 : 1. © 2020 IEEE.
Uzun Kisa-Sureli Bellek Tabanli Diziden Diziye Otokodlayici Kullanarak EKG Isaretlerinin Sikistirilmasi
28th Signal Processing and Communications Applications Conference, SIU 2020 -- 5 October 2020 through 7 October 2020 -- -- 166413
Keywords: data compression; deep learning; ECG signals; long short-term memory networks; sequence-tosequence autoencoders