Arrhythmia ECG Beats Classification Using Wavelet-Based Features and Support Vector Machine Classifier

2019 ◽  
pp. 74-88 ◽  
Author(s):  
Chandan Kumar Jha ◽  
Maheshkumar H. Kolekar
Keyword(s):  
Support Vector Machine ◽  
Premature Ventricular Contraction ◽  
Abnormal Behavior ◽  
Support Vector ◽  
Bundle Branch Block ◽  
Learning Classifier ◽  
Beat Classification ◽  
Multi Resolution Analysis ◽  
Resolution Analysis ◽  
Electrocardiogram Ecg

Abnormal behavior of heart muscles generates irregular heartbeats which are collectively known as arrhythmia. Classification of arrhythmia beats plays a prominent role in electrocardiogram (ECG) analysis. It is widely used in online and long-term patient monitoring systems. This chapter reports a classification technique to recognize normal (N) and five arrhythmia beats (i.e., left bundle branch block [LBBB], right bundle branch block [RBBB], premature ventricular contraction [V], paced [P], and atrial premature contraction [A]). The technique utilizes features of heartbeats extracted by the wavelet multi-resolution analysis. The feature vectors are used to train and test the classifier based on the support vector machine which has been emerged as a benchmark in machine learning classifier. It accomplishes the beat classification very efficiently. ECG records of the MIT-BIH arrhythmia database are utilized to acquire the different types of heartbeats. Performance of the proposed classifier outperforms the contemporary arrhythmia beats classification techniques.

Electrocardiogram Beat Classification Using BAT-Optimized Fuzzy KNN Classifier

2019 ◽  
pp. 132-141
Author(s):  
Atul Kumar Verma ◽  
Indu Saini ◽  
Barjinder Singh Saini
Keyword(s):  
Nearest Neighbor ◽  
Premature Ventricular Contraction ◽  
Bat Algorithm ◽  
Ventricular Contraction ◽  
K Nearest Neighbor ◽  
Beat Classification ◽  
Knn Classifier ◽  
Electrocardiogram Ecg

In this chapter, the BAT-optimized fuzzy k-nearest neighbor (FKNN-BAT) algorithm is proposed for discrimination of the electrocardiogram (ECG) beats. The five types of beats (i.e., normal [N], right bundle block branch [RBBB], left bundle block branch [LBBB], atrial premature contraction [APC], and premature ventricular contraction [PVC]) are taken from MIT-BIH arrhythmia database for the experimentation. Thereafter, the features are extracted from five type of beats and fed to the proposed BAT-tuned fuzzy KNN classifier. The proposed classifier achieves the overall accuracy of 99.88%.

Multi-Resolution Analysis of Edge-Texture Features for Mammographic Mass Classification

2019 ◽  
Vol 29 (10) ◽  
pp. 2050156
Author(s):  
Rinku Rabidas ◽  
Abhishek Midya ◽  
Jayasree Chakraborty ◽  
Wasim Arif
Keyword(s):  
Local Binary Pattern ◽  
Texture Features ◽  
Support Vector ◽  
Stepwise Logistic Regression ◽  
Local Ternary Pattern ◽  
Multi Resolution Analysis ◽  
Logistic Regression Method ◽  
Resolution Analysis ◽  
Optimal Set

In this paper, multi-resolution analysis of two edge-texture based descriptors, Discriminative Robust Local Binary Pattern (DRlbp) and Discriminative Robust Local Ternary Pattern (DRltp), are proposed for the determination of mammographic masses as benign or malignant. As an extension of Local Binary Pattern (LBP) and Local Ternary Pattern (LTP), DRlbp and LTP-based features overcome the drawbacks of these features preserving the edge information along with texture. With the hypothesis that multi-resolution analysis of these features for different regions related to mammaographic masses with wavelet transform will capture more discriminating patterns and thus can help in characterizing masses. In order to evaluate the efficiency of the proposed approach, several experiments are carried out using the mini-MIAS database where a 5-fold cross validation technique is incorporated with Support Vector Machine (SVM) on the optimal set of features obtained via stepwise logistic regression method. An area under the receiver operating characteristic (ROC) curve ([Formula: see text] value) of 0.96 is achieved with DRlbp attributes as the best performance. The superiority of the proposed scheme is established by comparing the obtained results with recently developed other competing schemes.

Learning of Whole Arm Manipulation with Constraint of Contact Mode Maintaining

2010 ◽  
Vol 22 (4) ◽  
pp. 542-550 ◽  
Author(s):  
Nobuyuki Kawarai ◽  
◽  
Yuichi Kobayashi
Keyword(s):  
Support Vector Machine ◽  
Reinforcement Learning ◽  
Support Vector ◽  
Svm Classifier ◽  
Control Input ◽  
Contact Mode ◽  
Learning Classifier ◽  
Input Torque

This paper proposes the learning of whole arm manipulation with a two-link manipulator. Our proposal combines a controller obtained by reinforcement learning (actor-critic) and a learning classifier realized by a Support Vector Machine (SVM). The classifier learns the boundary between slip and stick modes in torque space. Using the result of classification, the robot learns to move the object toward desired position while keeping the desired contact modes. Control input (torque) is first specified by the actor. The SVM classifier judges whether torque can maintain the desired slip or stick mode and, if not, it modifies the torque so that the desired mode is maintained. It was verified in the simulation that our proposed learning realized accelerating of the object and decelerating it while keeping the desired mode, i.e., avoiding undesired slipping of the object.

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