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 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 Wearable Measurement of ECG Signals Based on Smart Clothing

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Ming Li ◽  
Wei Xiong ◽  
Yongjian Li
Keyword(s):  
Human Body ◽  
The Body ◽  
Differential Signal ◽  
Ecg Signals ◽  
Smart Clothing ◽  
Textile Electrodes ◽  
Volume Conductor Model ◽  
Body Surface Potential ◽  
Potential Mapping ◽  
Electrocardiogram Ecg

Smart clothing that can measure electrocardiogram (ECG) signals and monitor the health status of people meets the needs of our increasingly aging society. However, the conventional measurement of ECG signals is complicated and its electrodes can cause irritation to the skin, which makes the conventional measurement method unsuitable for applications in smart clothing. In this paper, a novel wearable measurement of ECG signals is proposed. There are only three ECG textile electrodes knitted into the fabric of smart clothing. The acquired ECG signals can be transmitted to a smartphone via Bluetooth, and they can also be sent out to a PC terminal by a smartphone via WiFi or Internet. To get more significant ECG signals, the ECG differential signal between two electrodes is calculated based on a spherical volume conductor model, and the best positions on the surface of a human body for two textile electrodes to measure ECG signals are simulated by using the body-surface potential mapping (BSPM) data. The results show that position 12 in the lower right and position 11 in the upper left of the human body are the best for the two electrodes to measure ECG signals, and the presented wearable measurement can obtain good performance when one person is under the conditions of sleeping and jogging.

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 Classification & Feature extraction of Brain tumor from MRI Images using Modified ANN Approach

2021 ◽  
Vol 9 (2) ◽  
pp. 10-15
Author(s):  
Harendra Singh ◽  
Roop Singh Solanki
Keyword(s):  
Feature Extraction ◽  
Brain Tumor ◽  
Computational Time ◽  
Discrete Wavelet ◽  
Classification Feature ◽  
Proposed Model ◽  
Magnetic Resonance Imaging Mri ◽  
Artificial Neural Network Ann ◽  
High Degree

In this research paper, a new modified approach is proposed for brain tumor classification as well as feature extraction from Magnetic Resonance Imaging (MRI) after pre-processing of the images. The discrete wavelet transformation (DWT) technique is used for feature extraction from MRI images and Artificial Neural Network (ANN) is used for the classification of the type of tumor according to extracted features. Mean, Standard deviation, Variance, Entropy, Skewness, Homogeneity, Contrast, Correlation are the main features used to classify the type of tumor. The proposed model can give a better result in comparison with other available techniques in less computational time as well as a high degree of accuracy. The training and testing accuracies of the proposed model are 100% and 98.20% with a 98.70 % degree of precision respectively.

scholarly journals Classification of cardiac arrhythmias using deep learning

2018 ◽  
Vol 7 (3.3) ◽  
pp. 401
Author(s):  
Jeong Hwan Kim ◽  
Jeong Whan Lee ◽  
Kyeong Seop Kim
Keyword(s):  
Deep Learning ◽  
Normal Sinus Rhythm ◽  
Premature Ventricular Contraction ◽  
Heart Rhythm ◽  
Future Research ◽  
Normal Sinus ◽  
Input Layer ◽  
Hidden Layer ◽  
Electrocardiogram Ecg

Background/Objectives: The main objective of this research is to design Deep Learning (DL) architecture to classify an electrocardiogram (ECG) signal into normal sinus rhythm (NSR), premature ventricular contraction (PVC), atrial premature contraction (APC) or right/left bundle branch block (RBBB/LBBB) arrhythmia by empirically optimizing the numbers of hidden layers, the number of neurons in each hidden layer and the number of neurons in input layer in DL model.Methods/Statistical analysis: For our experimental simulations, PhysioBank-MIT/BIH annotated ECG database was considered to classify heart beats into abnormal rhythms (PVC, APC, RBBB, LBBB) or normal sinus. The performance of classifying ECG beats by the proposed DL architecture was evaluated by computing the overall accuracy of classifying NSR or four different arrhythmias.Findings: Base on testing MIT/BIH arrhythmia database, the proposed DL model can classify the heart rhythm into one of NSR, PVC, APC, RBBB or LBBB beat with the mean accuracy of 95.5% by implementing DL architecture with 200 neurons in input layer, 100 neurons in the first and second hidden layer, respectively and 80 neurons in the 3rd hidden layer.Improvements/Applications: Our experimental results show that the proposed DL model might not be quite accurate for detecting APC beats due to its morphological resemblance of NSR. Therefore, we might need to design more sophisticated DL architecture by including more temporal characteristics of APC to increase the classification accuracy of APC arrhythmia in the future research efforts. 

The Role of Neural Networks in Computerized Classification of the Electrocardiogram

2011 ◽  
pp. 60-80
Author(s):  
Chris D. Nugent ◽  
Dewar D. Finlay ◽  
Mark P. Donnelly ◽  
Norman D. Black
Keyword(s):  
Neural Networks ◽  
Research Effort ◽  
Body Surface Potential ◽  
Potential Mapping ◽  
Rule Set ◽  
Chaotic Nature ◽  
Definition Of ◽  
Cardiac Diagnosis ◽  
Electrocardiogram Ecg

Electrical forces generated by the heart are transmitted to the skin through the body’s tissues. These forces can be recorded on the body’s surface and are represented as an electrocardiogram (ECG). The ECG can be used to detect many cardiac abnormalities. Traditionally, ECG classification algorithms have used rule based techniques in an effort to model the thought and reasoning process of the human expert. However, the definition of an ultimate rule set for cardiac diagnosis has remained somewhat elusive, and much research effort has been directed at data driven techniques. Neural networks have emerged as a strong contender as the highly non-linear and chaotic nature of the ECG represents a well-suited application for this technique. This study presents an overview of the application of neural networks in the field of ECG classification, and, in addition, some preliminary results of adaptations of conventional neural classifiers are presented. From this work, it is possible to highlight issues that will affect the acceptance of this technique and, in addition, identify challenges faced for the future. The challenges can be found in the intelligent processing of larger amounts of ECG information which may be generated from recording techniques such as body surface potential mapping.

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.  

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