Compression of ECG Signals Using Long Short-Term Memory based Sequence-to-Sequence Autoencoder

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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

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