scholarly journals ECG Beats Classification Using Mixture of Features

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Manab Kumar Das ◽  
Samit Ari
Keyword(s):  
Feature Extraction ◽  
Extraction Methods ◽  
Ectopic Beat ◽  
Extraction Techniques ◽  
Efficient System ◽  
Ecg Signals ◽  
Temporal Features ◽  
S Transform ◽  
Electrocardiogram Ecg

Classification of electrocardiogram (ECG) signals plays an important role in clinical diagnosis of heart disease. This paper proposes the design of an efficient system for classification of the normal beat (N), ventricular ectopic beat (V), supraventricular ectopic beat (S), fusion beat (F), and unknown beat (Q) using a mixture of features. In this paper, two different feature extraction methods are proposed for classification of ECG beats: (i) S-transform based features along with temporal features and (ii) mixture of ST and WT based features along with temporal features. The extracted feature set is independently classified using multilayer perceptron neural network (MLPNN). The performances are evaluated on several normal and abnormal ECG signals from 44 recordings of the MIT-BIH arrhythmia database. In this work, the performances of three feature extraction techniques with MLP-NN classifier are compared using five classes of ECG beat recommended by AAMI (Association for the Advancement of Medical Instrumentation) standards. The average sensitivity performances of the proposed feature extraction technique for N, S, F, V, and Q are 95.70%, 78.05%, 49.60%, 89.68%, and 33.89%, respectively. The experimental results demonstrate that the proposed feature extraction techniques show better performances compared to other existing features extraction techniques.

scholarly journals Detection and Classification of R-Peak Using Naïve Bayes Classifier

2018 ◽  
Vol 7 (3.27) ◽  
pp. 397 ◽  
Author(s):  
S Celin ◽  
K Vasanth
Keyword(s):  
Feature Extraction ◽  
Performance Measures ◽  
Heart Diseases ◽  
Processing Technique ◽  
Challenging Problem ◽  
Bayes Classifier ◽  
Extraction Techniques ◽  
And Performance ◽  
Electrocardiogram Ecg

Electrocardiogram (ECG) in classification of signals plays a major role in the diagnoses of heart diseases. The main challenging problem is the classification of accurate ECG. Here in this paper the ECG is classified into arrhythmia types. It is very important that detecting the heart disease and finding the treatment for the patient at the earliest must be done accurately. In the ECG classification different classifiers are available. The best accuracy value of 99.7% is produced by using the Bayes classifiers in this paper. ECG databases, classifiers, feature extraction techniques and performance measures are presented in the pre-processing technique. And also the classification of ECG, analysis of input beat selection and the output of classifiers are also discussed in this paper.  

ELECTROCARDIOGRAM BEAT CLASSIFICATION USING S-TRANSFORM BASED FEATURE SET

2014 ◽  
Vol 14 (05) ◽  
pp. 1450066 ◽  
Author(s):  
MANAB KUMAR DAS ◽  
SAMIT ARI
Keyword(s):  
Feature Extraction ◽  
Support Vector ◽  
Svm Classifier ◽  
Massachusetts Institute Of Technology ◽  
Ecg Signals ◽  
Beat Classification ◽  
Temporal Features ◽  
S Transform ◽  
Institute Of Technology ◽  
Stockwell Transform

In this paper, the conventional Stockwell transform is effectively used to classify the ECG arrhythmias. The performance of ECG classification mainly depends on feature extraction based on an efficient formation of morphological and temporal features and the design of the classifier. Feature extraction is the important component of designing the system based on pattern recognition since even the best classifier will not perform better if the good features are not selected properly. Here, the S-transform (ST) is used to extract the morphological features which is appended with temporal features. This feature set is independently classified using artificial neural network (NN) and support vector machine (SVM). In this work, five classes of ECG beats (normal, ventricular, supra ventricular, fusion and unknown beats) from Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database are classified according to AAMI EC57 1998 standard (Association for the Advancement of Medical Instrumentation). Performance is evaluated on several normal and abnormal ECG signals of MIT-BIH arrhythmias database using two classifier techniques: ST with NN classifier (ST-NN) and other proposed ST with SVM classifier (ST-SVM). The proposed method achieves accuracy of 98.47%. The performance of the proposed technique is compared with ST-NN and earlier reported technique.

ECG Processing

2011 ◽  
pp. 137-160
Author(s):  
Lenka Lhotská ◽  
Václav Chudácek ◽  
Michal Huptych
Keyword(s):  
Feature Extraction ◽  
Fuzzy Rule ◽  
Future Research ◽  
Discrete Wavelet ◽  
Channel Measurements ◽  
Ecg Signals ◽  
Body Surface Potential ◽  
Potential Mapping ◽  
Electrocardiogram Ecg

This chapter describes methods for preprocessing, analysis, feature extraction, visualization, and classification of electrocardiogram (ECG) signals. First we introduce preprocessing methods, mainly based on the discrete wavelet transform. Then classification methods such as fuzzy rule based decision trees and neural networks are presented. Two examples - visualization and feature extraction from Body Surface Potential Mapping (BSPM) signals and classification of Holter ECGs – illustrate how these methods are used. Visualization is presented in the form of BSPM maps created from multi-channel measurements on the patient’s thorax. Classification involves distinguishing between Holter recordings from premature ventricular complexes and normal ECG beats. Classification results are discussed. Finally the future research opportunities are proposed.

scholarly journals Detection and Classification of ECG Signal through Machine learning

2019 ◽  
Vol 8 (10) ◽  
pp. 3221-3227
Keyword(s):  
The Body ◽  
Wave Form ◽  
Ecg Signal ◽  
Software Application ◽  
Ecg Signals ◽  
Temporal Features ◽  
Associate Anomaly ◽  
The Individual ◽  
Electrocardiogram Ecg

The electrical activity which might be acquired by inserting the probes on the body exterior that is originated within the individual muscle cells of the heart and is summed to indicate an indication wave form referred to as the EKG (ECG). Cardiac Arrhythmia is an associate anomaly within the heart which may be diagnosed with the usage of signals generated by Electrocardiogram (ECG). For the classification of ECG signals a software application model was developed and has been investigated with the usage of the MIT-BIH database. The version is based on some existing algorithms from literature, entails the extraction of a few temporal features of an ECG signal and simulating it with a trained FFNN. The software version may be employed for the detection of coronary heart illnesses in patients. The neural network’s structure and weights are optimized using Particle Swarm Optimization (PSO). The FFNN trained with set of rules by PSO increase its accuracy. The overall accuracy and sensitivity of the algorithm is about 93.687 % and 92%.

Feature extraction and classification of electrocardiogram (ECG) signals related to hypoglycaemia

2003 ◽  
Author(s):  
C. Alexakis ◽  
H.O. Nyongesa ◽  
R. Saatchi ◽  
N.D. Harris ◽  
C. Davies ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Ecg Signals ◽  
Electrocardiogram Ecg

Effects of the Window Size and Feature Extraction Approach for Arrhythmia Classification

2017 ◽  
Vol 30 ◽  
pp. 1-11 ◽  
Author(s):  
Khudhur A. Alfarhan ◽  
Mohd Yusoff Mashor ◽  
Abdul Rahman Mohd Saad ◽  
Hayder A. Azeez ◽  
Mustafa M. Sabry
Keyword(s):  
Feature Extraction ◽  
Autoregressive Model ◽  
Research Work ◽  
Window Size ◽  
Extraction Methods ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Ecg Signals ◽  
Library Support ◽  
Electrocardiogram Ecg

Arrhythmia, a common form of heart disease, can be detected from an electrocardiogram (ECG) signal. This research work presents a comparative study between five feature extraction methods applied separately on two window sizes for detecting three ECG pulse types, namely normal and two arrhythmia variations. The library support vector machine (LIBSVM) was used to classify the three classes of the ECG pulses. The ECG signals were obtained from MIT-BIH database. The ECG dataset was normalized and filtered to remove any noise and after that the signals were windowed into two window sizes (long window and short window). Five approaches were used to extract the features from the ECG signals. These approaches are scalar Autoregressive model coefficients, Haar discrete wavelet transform (DWT), Daubechies (db) DWT, Biorthogonal (bior) DWT, and principal components analysis (PCA). Each approach was applied separately on the two window sizes. The results of the classification show that scalar Autoregressive model coefficients, Haar, db, and bior are better approaches to catch the ECG features for short window than the long window. However, PCA gave the closest and highest results for the two window sizes than other approaches. That mean the PCA is the better feature extraction approach for both window sizes.

scholarly journals Feature Extraction and Classification of Citrus Juice by Using an Enhanced L-KSVD on Data Obtained from Electronic Nose

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 916 ◽  
Author(s):  
Wen Cao ◽  
Chunmei Liu ◽  
Pengfei Jia
Keyword(s):  
Feature Extraction ◽  
Kernel Function ◽  
Electronic Nose ◽  
Classification Accuracy ◽  
Extraction Methods ◽  
Object Function ◽  
Optimal Value ◽  
Processed Products

Aroma plays a significant role in the quality of citrus fruits and processed products. The detection and analysis of citrus volatiles can be measured by an electronic nose (E-nose); in this paper, an E-nose is employed to classify the juice which is stored for different days. Feature extraction and classification are two important requirements for an E-nose. During the training process, a classifier can optimize its own parameters to achieve a better classification accuracy but cannot decide its input data which is treated by feature extraction methods, so the classification result is not always ideal. Label consistent KSVD (L-KSVD) is a novel technique which can extract the feature and classify the data at the same time, and such an operation can improve the classification accuracy. We propose an enhanced L-KSVD called E-LCKSVD for E-nose in this paper. During E-LCKSVD, we introduce a kernel function to the traditional L-KSVD and present a new initialization technique of its dictionary; finally, the weighted coefficients of different parts of its object function is studied, and enhanced quantum-behaved particle swarm optimization (EQPSO) is employed to optimize these coefficients. During the experimental section, we firstly find the classification accuracy of KSVD, and L-KSVD is improved with the help of the kernel function; this can prove that their ability of dealing nonlinear data is improved. Then, we compare the results of different dictionary initialization techniques and prove our proposed method is better. Finally, we find the optimal value of the weighted coefficients of the object function of E-LCKSVD that can make E-nose reach a better performance.

scholarly journals Machine learning, waveform preprocessing and feature extraction methods for classification of acoustic startle waveforms

MethodsX ◽  
2021 ◽  
Vol 8 ◽  
pp. 101166
Author(s):  
Timothy J. Fawcett ◽  
Chad S. Cooper ◽  
Ryan J. Longenecker ◽  
Joseph P. Walton
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Acoustic Startle ◽  
Extraction Methods

Comparison of feature extraction techniques for classification of hardwood species

2018 ◽  
Vol 4 (2/3) ◽  
pp. 106
Author(s):  
Arvind R. Yadav ◽  
R.S. Anand ◽  
M.L. Dewal ◽  
Sangeeta Gupta ◽  
Jayendra Kumar
Keyword(s):  
Feature Extraction ◽  
Extraction Techniques ◽  
Hardwood Species
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