Automated detection of arrhythmia from electrocardiogram signal based on new convolutional encoded features with bidirectional long short-term memory network classifier

2021 ◽  
Author(s):  
Saroj Kumar Pandey ◽  
Rekh Ram Janghel
Keyword(s):  
Short Term Memory ◽  
Automated Detection ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Electrocardiogram Signal ◽  
Long Short Term Memory

scholarly journals Automated detection of atrial fibrillation using long short-term memory network with RR interval signals

2018 ◽  
Vol 102 ◽  
pp. 327-335 ◽  
Author(s):  
Oliver Faust ◽  
Alex Shenfield ◽  
Murtadha Kareem ◽  
Tan Ru San ◽  
Hamido Fujita ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Short Term Memory ◽  
Automated Detection ◽  
Short Term ◽  
Term Memory ◽  
Rr Interval ◽  
Memory Network ◽  
Long Short Term Memory

Automated Detection and Classification of Arrhythmia From ECG Signals Using Feature-Induced Long Short-Term Memory Network

2020 ◽  
Vol 4 (8) ◽  
pp. 1-4
Author(s):  
Biswarup Ganguly ◽  
Avishek Ghosal ◽  
Anirbed Das ◽  
Debanjan Das ◽  
Debanjan Chatterjee ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Automated Detection ◽  
Short Term ◽  
Term Memory ◽  
Ecg Signals ◽  
Memory Network ◽  
Long Short Term Memory

scholarly journals Small Floating Target Detection Method Based on Chaotic Long Short-Term Memory Network

2021 ◽  
Vol 9 (6) ◽  
pp. 651
Author(s):  
Yan Yan ◽  
Hongyan Xing
Keyword(s):  
High Frequency ◽  
Short Term Memory ◽  
Detection Method ◽  
Low Frequency ◽  
Sea Clutter ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Small Targets

In order for the detection ability of floating small targets in sea clutter to be improved, on the basis of the complete ensemble empirical mode decomposition (CEEMD) algorithm, the high-frequency parts and low-frequency parts are determined by the energy proportion of the intrinsic mode function (IMF); the high-frequency part is denoised by wavelet packet transform (WPT), whereas the denoised high-frequency IMFs and low-frequency IMFs reconstruct the pure sea clutter signal together. According to the chaotic characteristics of sea clutter, we proposed an adaptive training timesteps strategy. The training timesteps of network were determined by the width of embedded window, and the chaotic long short-term memory network detection was designed. The sea clutter signals after denoising were predicted by chaotic long short-term memory (LSTM) network, and small target signals were detected from the prediction errors. The experimental results showed that the CEEMD-WPT algorithm was consistent with the target distribution characteristics of sea clutter, and the denoising performance was improved by 33.6% on average. The proposed chaotic long- and short-term memory network, which determines the training step length according to the width of embedded window, is a new detection method that can accurately detect small targets submerged in the background of sea clutter.

Intelligent tool wear monitoring based on parallel residual and stacked bidirectional long short-term memory network

2021 ◽  
Vol 60 ◽  
pp. 608-619
Author(s):  
Xianli Liu ◽  
Shaoyang Liu ◽  
Xuebing Li ◽  
Bowen Zhang ◽  
Caixu Yue ◽  
...  
Keyword(s):  
Tool Wear ◽  
Short Term Memory ◽  
Tool Wear Monitoring ◽  
Short Term ◽  
Term Memory ◽  
Wear Monitoring ◽  
Memory Network ◽  
Long Short Term Memory
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