Contents & References of Improvement of electrocardiogram (ECG) signal classification with support vector machine and particle swarm optimization (PSO-SVM)
List:
Chapter One Introduction. 1
1-1- Introduction. 2
1-2- Definition of the problem. 2
1-3- Necessity and importance of research. 3
1-4- Research method. 3
1-5- Definition of concepts. 4
The electrical signal of the heart: 4
The action potential of the heart muscle. 5
Rest stage: 5
Depolarization stage: 5
Repolarization stage: 5
P wave: 6
QRS curve: 6
T wave: 6
ST piece: 6
QT interval: 6
Heart beat diseases: 6
The second chapter Research background. 2
2-1- Introduction. 10
Introducing the database: 10
2-2- ECG signal classification using wavelet and neural network. 10
2-3- ECG signal classification using wavelet and morphological properties and neural network. 11
2-4- ECG signal classification using wavelet transform and fuzzy neural network. 11
2-5- ECG signal classification using violet transform and artificial neural network and bird algorithm. 12
2-6- Classification of cardiac arrhythmias using SVM. 12
2-7- Classification of atrioventricular arrhythmia. 12
2-8- Electrocardiogram signal classification with support vector machine classifier and PSO algorithm. 13
2-9- Classification of cardiac arrhythmias using PSO. 13
2-10- Combined approach in cancer classification. 14
2-11- Classification of cardiac arrhythmias based on wavelet transform and SVM. 14
2-12- ECG signal classification using morphological properties. 14
2-13- feature selection using binary cuckoo algorithm. 14
2-14- feature selection using the cuckoo algorithm. 15
Chapter Three Introduction of ECG signal processing algorithms and methods. 10
3-1- Introduction. 17
3-2- Wavelet analysis. 17
3-2-1- Continuous wave transformation (CWT) 18
3-2-2- Discrete wavelet transformation. 18
3-3-2-2- multi-level decomposition. 18
3-2-4- Selecting the mother wavelet. 19
3-2-4- Features extracted from Violet. 21
3-3- time characteristic. 21
3-4- Feature extraction with autoregression model (AR) 22
3-5- Feature selection strategy. 22
3-6-Principal component analysis (PCA) 23
3-7- Maximum dependence and minimum redundancy (mRMR) method 24
3-8- COA cuckoo algorithm. 26
3-8-2- Details of the cuckoo optimization algorithm. 27
3-8-2-1- Producing the primary habitats of cuckoos (initial population of candidate answers) 29
3-8-2-2- Method of cuckoos for laying eggs. 30
3-8-2-3- Migration of cuckoos 30
3-8-2-4- Destroying cuckoos located in inappropriate areas. 32
3-8-2-5- algorithm convergence. 32
3-9- Binary discretization of the cuckoo algorithm. 33
3-10- Support vector machine (SVM) 33
3-11- Particle optimization algorithm (PSO) 35
3-11-1- Inertia weight. 36
3-12- Overview of ECG signal classification system. 38
Chapter IV Proposed ECG signal classification method. 17
4-1- Introduction. 40
4-2- ECG signal preprocessing. 41
4-2-1- Signal shift to background deviation. 42
4-2-2- Removing the average value of the signal. 42
4-2-3- Removing noise caused by city electricity. 43
4-2-4- Signal smoothing. 43
4-2-5- signal windowing. 43
4-2-6- Correlation test and removal of uncorrelated beats. 44
4-2-7- Selection of training and test data. 44
4-3- signal characteristics. 47
4-3-1- feature extraction. 47
4-3-1-1- Time feature. 47
4-3-1-2- wavelet feature. 47
4-3-1-3- AR feature. 47
4-3-1-4- Identification of important signal points using PCA. 48
4-3-2-combination and integration of features 48
4-3-2-1- feature selection with PCA. 48
4-3-2-2- feature selection with mRMR. 49
4-3-2-3- feature selection using the cuckoo algorithm. 49
4-4- Classification using SVM. 51
Chapter Five Conclusion. 55
5-1- Introduction. 56
5-2- Comparison and conclusion. 56
5-4- Submitting an offer. 57
Resources: 58
Source:
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Sources: 58
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