ECG Compression method based on convolutional autoencoder and discrete wavelet transform
Küçük Resim Yok
Institute of Electrical and Electronics Engineers Inc.
In this work, a compression method based on one dimensional convolutional autocoder architecture and wavelet transform is proposed for the compression of ECG signals. The proposed method is tested on the MIT-BIH Arrhythmia Database and its performance is evaluated with respect to compression ratio (CR) and mean-independent percentage mean square difference (MPRD). Experimental results showed that the proposed method achieves an average CR value of 32.27:1 with an averages MPRD of %18.91. © 2020 IEEE.
28th Signal Processing and Communications Applications Conference, SIU 2020 -- 5 October 2020 through 7 October 2020 -- -- 166413
convolutional auto-encoder, data compression, discrete wavelet transform, ECG signals
2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings