Spectral feature extraction based on continuous wavelet transform and image segmentation for peak detection

2020 ◽  
Vol 12 (2) ◽  
pp. 169-178 ◽  
Author(s):  
Guofeng Yang ◽  
Jiacai Dai ◽  
Xiangjun Liu ◽  
Meng Chen ◽  
Xiaolong Wu
Keyword(s):  
Feature Extraction ◽  
Image Segmentation ◽  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Spectral Feature ◽  
Peak Detection ◽  
Continuous Wavelet ◽  
Crucial Step ◽  
Spectral Signal

Peak detection is a crucial step in spectral signal pre-processing.

Peak detection of TOF-SIMS using continuous wavelet transform and curve fitting

2018 ◽  
Vol 428 ◽  
pp. 43-48 ◽  
Author(s):  
Ying Zheng ◽  
Di Tian ◽  
Ke Liu ◽  
Zemin Bao ◽  
Peizhi Wang ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Curve Fitting ◽  
Continuous Wavelet Transform ◽  
Peak Detection ◽  
Continuous Wavelet ◽  
Tof Sims

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.

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