A low complexity wavelet transform with point-symmetric extension at tile boundaries

2002 ◽  
Author(s):  
I. Kharitonenko ◽  
X. Zhang
Keyword(s):  
Wavelet Transform ◽  
Low Complexity ◽  
Symmetric Extension

Circulant Matrix-Based Continuous Wavelet Transform for Achieving Low Complexity Electrocardiogram Feature Extraction in Health Monitoring Applications

2020 ◽  
Vol 17 (1) ◽  
pp. 254-259
Author(s):  
Harikrishna Ponnam ◽  
Jakeer Hussain Shaik
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Computational Complexity ◽  
Error Rate ◽  
Continuous Wavelet Transform ◽  
Low Complexity ◽  
Circulant Matrix ◽  
Continuous Wavelet ◽  
Ecg Signals ◽  
Gradient Based

In the application of remote cardiovascular monitoring, the computational complexity and power consumption need to be maintained in a considerable level in order to prevent the limitations introduced by the computationally constrained equipment’s that perform the process of continuous monitoring and analysis. In this paper, a Circulant Matrix-based Continuous Wavelet Transform (CM-CWT)-based feature extraction mechanism is contributed to minimizing the computational complexity incurred during the process of feature extraction from the input ECG signals. This proposed CM-CWT mechanism derives the advantages of the Circulant Matrix-based Continuous Wavelet Transform and Gradient-based filtering design for achieving excellent feature extraction from ECG signals with low computational complexity. The experimental investigation of the proposed CM-CWT mechanism is conducted using the factors of computational complexity, sensitivity, prediction accuracy and error rate for estimating its predominance over the compared DWT-HAAR and HIFEA approaches used for ECG feature extraction. The experiments of the proposed CM-CWT mechanism on an average is estimated to reduce the error rate to the maximum of 21% compared to the existing DWT-HAAR and HIFEA approaches used for ECG feature extraction.

scholarly journals Low Complexity Automatic Stationary Wavelet Transform for Elimination of Eye Blinks from EEG

2019 ◽  
Vol 9 (12) ◽  
pp. 352
Author(s):  
Mohammad Shahbakhti ◽  
Maxime Maugeon ◽  
Matin Beiramvand ◽  
Vaidotas Marozas
Keyword(s):  
Wavelet Transform ◽  
Correlation Coefficient ◽  
Signal To Noise Ratio ◽  
Low Complexity ◽  
Stationary Wavelet Transform ◽  
Eeg Signals ◽  
Real Time Processing ◽  
Eye Blink ◽  
Eye Blinks ◽  
Components Analysis

The electroencephalogram signal (EEG) often suffers from various artifacts and noises that have physiological and non-physiological origins. Among these artifacts, eye blink, due to its amplitude is considered to have the most influence on EEG analysis. In this paper, a low complexity approach based on Stationary Wavelet Transform (SWT) and skewness is proposed to remove eye blink artifacts from EEG signals. The proposed method is compared against Automatic Wavelet Independent Components Analysis (AWICA) and Enhanced AWICA. Normalized Root Mean Square Error (NRMSE), Peak Signal-to-Noise Ratio (PSNR), and correlation coefficient ( ρ ) between filtered and pure EEG signals are utilized to quantify artifact removal performance. The proposed approach shows smaller NRMSE, larger PSNR, and larger correlation coefficient values compared to the other methods. Furthermore, the speed of execution of the proposed method is considerably faster than other methods, which makes it more suitable for real-time processing.

A wavelet transform with point-symmetric extension at tile boundaries

2002 ◽  
Vol 11 (12) ◽  
pp. 1357-1364 ◽  
Author(s):  
I. Kharitonenko ◽  
Xing Zhang ◽  
S. Twelves
Keyword(s):  
Wavelet Transform ◽  
Symmetric Extension
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