scholarly journals A novel Discrete Wavelet-Concatenated Mesh Tree and ternary chess pattern based ECG signal recognition method

2022 ◽  
Vol 72 ◽  
pp. 103331
Author(s):  
Turker Tuncer ◽  
Sengul Dogan ◽  
Pawel Plawiak ◽  
Abdulhamit Subasi
Keyword(s):  
Discrete Wavelet ◽  
Ecg Signal ◽  
Signal Recognition ◽  
Recognition Method ◽  
Mesh Tree

Walking Intrusion Signal Recognition Method for Fiber Fence System

2009 ◽  
Author(s):  
Nian Fang ◽  
Lutang Wang ◽  
Dongjian Jia ◽  
Chao Shan ◽  
Zhaoming Huang
Keyword(s):  
Signal Recognition ◽  
Recognition Method

scholarly journals Advanced Signal Recognition Method for Path using FPGA

2017 ◽  
Vol 4 (3) ◽  
pp. 26-30
Author(s):  
Belly Ballot R ◽  
Anisley T ◽  
Addison N
Keyword(s):  
Signal Recognition ◽  
Recognition Method

Deep Learning Based Adaptive Recurrent Neural Network for Detection of Myocardial Infarction

2021 ◽  
Vol 11 (12) ◽  
pp. 3044-3053
Author(s):  
Rakesh Kumar Mahendran ◽  
V. Prabhu ◽  
V. Parthasarathy ◽  
A. Mary Judith
Keyword(s):  
Neural Network ◽  
Myocardial Infarction ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Classification Accuracy ◽  
Learning Algorithm ◽  
Training Data ◽  
Discrete Wavelet ◽  
Ecg Signal ◽  
Detection Techniques

Myocardial infarction (MI) may precipitate severe health damage and lead to irreversible death of the heart muscle, the result of prolonged lack of oxygen if it is not treated in a timely manner. Lack of accurate and early detection techniques for this heart disease has reduced the efficiency of MI diagnosis. In this paper, the design, and implementation of an efficient deep learning algorithm called Adaptive Recurrent neural network (ARNN) is proposed for the MI detection. The main objective of the proposed work is the accurate identification of MI disease using ECG signals. ECG signal denoising has been performed using the Multi-Notch filter, which removes the specified noise frequency range. Discrete wavelet transform (DWT) is utilized for performing the feature extraction that decomposes the ECG signal into varied scales with waveletfiltering bank. After the extraction of specific QRS features, classification of the defected and normal ECG arrhythmic beat has been performed using the deep learning-based ARNN classifier. The MIT-BIH database has been used for testing and training data. The performance of the proposed algorithm is evaluated based on classification accuracy. Results that are attained include the classification accuracy of about 99.21%, 99% of sensitivity and 99.4% of specificity with PPV and NPV of about 99.4 and 99.01 values indicate the enhanced performance of our proposed work compared with the conventional LSTM-CAE and LSTM-CNN techniques.

Optical fiber intrusion signal recognition method based on TSVD-SCN

2019 ◽  
Vol 48 ◽  
pp. 270-277 ◽  
Author(s):  
Zhiyong Sheng ◽  
Zhiqiang Zeng ◽  
Hongquan Qu ◽  
Yuan Zhang
Keyword(s):  
Optical Fiber ◽  
Signal Recognition ◽  
Recognition Method

A Novel Fiber Intrusion Signal Recognition Method for OFPS Based on SCN With Dropout

2019 ◽  
Vol 37 (20) ◽  
pp. 5221-5230 ◽  
Author(s):  
Wei Li ◽  
Zhiqiang Zeng ◽  
Hongquan Qu ◽  
Chengbin Sun
Keyword(s):  
Signal Recognition ◽  
Recognition Method

Walking intrusion signal recognition method for fiber fence system

2009 ◽  
Author(s):  
Nian Fang ◽  
Lutang Wang ◽  
Dongjian Jia ◽  
Chao Shan ◽  
Zhaoming Huang
Keyword(s):  
Signal Recognition ◽  
Recognition Method
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