Aydemir, GürkanBekiryazıcı, TahirGürkan, Hakan2021-03-202021-03-2020209781728172064http://doi.org/10.1109/SIU49456.2020.9302503https://hdl.handle.net/20.500.12885/128428th Signal Processing and Communications Applications Conference, SIU 2020 -- 5 October 2020 through 7 October 2020 -- -- 166413This 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.trinfo:eu-repo/semantics/closedAccessdata compressiondeep learningECG signalslong short-term memory networkssequence-tosequence autoencodersConference Object10.1109/SIU49456.2020.93025032-s2.0-85100318674N/AN/A