PMID- 35893004
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20220824
IS  - 1099-4300 (Electronic)
IS  - 1099-4300 (Linking)
VI  - 24
IP  - 8
DP  - 2022 Jul 25
TI  - Deep Compressive Sensing on ECG Signals with Modified Inception Block and LSTM.
LID - 10.3390/e24081024 [doi]
LID - 1024
AB  - In practical electrocardiogram (ECG) monitoring, there are some challenges in 
      reducing the data burden and energy costs. Therefore, compressed sensing (CS) 
      which can conduct under-sampling and reconstruction at the same time is adopted 
      in the ECG monitoring application. Recently, deep learning used in CS methods 
      improves the reconstruction performance significantly and can removes of some of 
      the constraints in traditional CS. In this paper, we propose a deep 
      compressive-sensing scheme for ECG signals, based on modified-Inception block and 
      long short-term memory (LSTM). The framework is comprised of four modules: 
      preprocessing; compression; initial; and final reconstruction. We adaptively 
      compressed the normalized ECG signals, sequentially using three convolutional 
      layers, and reconstructed the signals with a modified Inception block and LSTM. 
      We conducted our experiments on the MIT-BIH Arrhythmia Database and Non-Invasive 
      Fetal ECG Arrhythmia Database to validate the robustness of our model, adopting 
      Signal-to-Noise Ratio (SNR) and percentage Root-mean-square Difference (PRD) as 
      the evaluation metrics. The PRD of our scheme was the lowest and the SNR was the 
      highest at all of the sensing rates in our experiments on both of the databases, 
      and when the sensing rate was higher than 0.5, the PRD was lower than 2%, showing 
      significant improvement in reconstruction performance compared to the comparative 
      methods. Our method also showed good recovering quality in the noisy data.
FAU - Hua, Jing
AU  - Hua J
AD  - School of Software, Jiangxi Agricultural University, Nanchang 330045, China.
FAU - Rao, Jue
AU  - Rao J
AD  - School of Computer and Information Engineering, Jiangxi Agricultural University, 
      Nanchang 330045, China.
FAU - Peng, Yingqiong
AU  - Peng Y
AD  - School of Software, Jiangxi Agricultural University, Nanchang 330045, China.
FAU - Liu, Jizhong
AU  - Liu J
AD  - Nanchang Key Laboratory of Medical and Technology Research, Nanchang University, 
      Nanchang 330031, China.
FAU - Tang, Jianjun
AU  - Tang J
AD  - School of Computer and Information Engineering, Jiangxi Agricultural University, 
      Nanchang 330045, China.
LA  - eng
GR  - 61861021/National Natural Science Foundation of China/
GR  - 61863027/National Natural Science Foundation of China/
GR  - GJJ190194/Science and Technology Project of Education Department of Jiangxi 
      Province/
GR  - GJJ200426/Science and Technology Project of Education Department of Jiangxi 
      Province/
PT  - Journal Article
DEP - 20220725
PL  - Switzerland
TA  - Entropy (Basel)
JT  - Entropy (Basel, Switzerland)
JID - 101243874
PMC - PMC9394370
OTO - NOTNLM
OT  - ECG signal
OT  - Inception block
OT  - LSTM
OT  - compressed sensing
OT  - deep learning
COIS- The authors declare no conflict of interest.
EDAT- 2022/07/28 06:00
MHDA- 2022/07/28 06:01
PMCR- 2022/07/25
CRDT- 2022/07/27 04:57
PHST- 2022/05/31 00:00 [received]
PHST- 2022/07/20 00:00 [revised]
PHST- 2022/07/23 00:00 [accepted]
PHST- 2022/07/27 04:57 [entrez]
PHST- 2022/07/28 06:00 [pubmed]
PHST- 2022/07/28 06:01 [medline]
PHST- 2022/07/25 00:00 [pmc-release]
AID - e24081024 [pii]
AID - entropy-24-01024 [pii]
AID - 10.3390/e24081024 [doi]
PST - epublish
SO  - Entropy (Basel). 2022 Jul 25;24(8):1024. doi: 10.3390/e24081024.