Compression of ECG Signals Using Long Short-Term Memory based Sequence-to-Sequence Autoencoder
Küçük Resim Yok
Tarih
2020
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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.
Açıklama
28th Signal Processing and Communications Applications Conference, SIU 2020 -- 5 October 2020 through 7 October 2020 -- -- 166413
Anahtar Kelimeler
data compression, deep learning, ECG signals, long short-term memory networks, sequence-tosequence autoencoders
Kaynak
2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
