Classification of G proteins and prediction of GPCRs-G proteins coupling specificity using continuous wavelet transform and information theory

Amino Acids ◽  
2011 ◽  
Vol 43 (2) ◽  
pp. 793-804 ◽  
Author(s):  
Zhanchao Li ◽  
Xuan Zhou ◽  
Zong Dai ◽  
Xiaoyong Zou
Keyword(s):  
Information Theory ◽  
Wavelet Transform ◽  
G Proteins ◽  
Continuous Wavelet Transform ◽  
Continuous Wavelet ◽  
Coupling Specificity

scholarly journals A Study On Ventricular Fibrillation And Ventricular Tachyarrythmia Classification Methods Using Continuous Wavelet Transform

2021 ◽  
Author(s):  
Elnaz Afatmirni
Keyword(s):  
Wavelet Transform ◽  
Ventricular Fibrillation ◽  
Continuous Wavelet Transform ◽  
Principal Component ◽  
Continuous Wavelet ◽  
Classification Methods ◽  
Eigen Values ◽  
Leave One Out ◽  
Case Feature

Ventricular Tachycardia (VT) and Ventricular Fibrillation (VF) are fatal cardiac diseases associated with cardiac arrest. It is difficult to manually classify VT and VF signals. However, precise classification of VT and VF signals can assist cardiologists to identify and ultimately prevent onset of VF or VT. In this thesis, some of the underlying features which characterize VF and VT are extracted and are used to efficiently classifying these signals. The features are acquired from energy coefficients matrices using Continuous Wavelet Transform (CWT) through application of Principal Component Analysis (PCA). The features are the vector containing newly generated energy projection coefficients and the vector containing the number of the top 99% principal components (Eigen-Values) for each case. Feature vectors are then passed through Fast Forward Neural Network (FFNN) and Leave One Out Method (LOOM) classifiers for discrimination. The results are then compared for the highest classification results for VF and VT signals.

scholarly journals A Study On Ventricular Fibrillation And Ventricular Tachyarrythmia Classification Methods Using Continuous Wavelet Transform

2021 ◽  
Author(s):  
Elnaz Afatmirni
Keyword(s):  
Wavelet Transform ◽  
Ventricular Fibrillation ◽  
Continuous Wavelet Transform ◽  
Principal Component ◽  
Continuous Wavelet ◽  
Classification Methods ◽  
Eigen Values ◽  
Leave One Out ◽  
Case Feature

Ventricular Tachycardia (VT) and Ventricular Fibrillation (VF) are fatal cardiac diseases associated with cardiac arrest. It is difficult to manually classify VT and VF signals. However, precise classification of VT and VF signals can assist cardiologists to identify and ultimately prevent onset of VF or VT. In this thesis, some of the underlying features which characterize VF and VT are extracted and are used to efficiently classifying these signals. The features are acquired from energy coefficients matrices using Continuous Wavelet Transform (CWT) through application of Principal Component Analysis (PCA). The features are the vector containing newly generated energy projection coefficients and the vector containing the number of the top 99% principal components (Eigen-Values) for each case. Feature vectors are then passed through Fast Forward Neural Network (FFNN) and Leave One Out Method (LOOM) classifiers for discrimination. The results are then compared for the highest classification results for VF and VT signals.

Classification of ischemic and non-ischemic cardiac events in Holter recordings based on the continuous wavelet transform

2020 ◽  
Vol 58 (5) ◽  
pp. 1069-1078
Author(s):  
Carolina Fernández Biscay ◽  
Pedro David Arini ◽  
Anderson Iván Rincón Soler ◽  
María Paula Bonomini
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Continuous Wavelet ◽  
Cardiac Events ◽  
Holter Recordings
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