ECG Beats Classification Using Mixture of Features

Das, Manab Kumar; Ari, Samit

doi:10.1155/2014/178436

Cited by 78 publications

(38 citation statements)

References 20 publications

(33 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using MLPNN, ECG signals are recognized and classified more accurately [3,8,9,12,28]. Accuracy of MLPNN increases with number of hidden neurons [25,40]. MLPNN performs static mapping, there are no internal dynamics.…”

Section: Classification Of Ecg Using Neural Networkmentioning

confidence: 99%

“…70% beats for training, 30% beats for testing B RBBB, LBBB, APC, PVC, Paced, APB, Ventricular flutter wave, Fusion of ventricular and normal, Blocked AP beat, Nodal escape beat, Fusion of paced and normal, Ventricular escape beat, Nodal premature beat, Atrial escape, Unclassified.Total 1252 beats for training Normal, LBBB, RBBB, Negative: Ventricular ectopic beats, PVC, Fusion of ventricular and normal. 150 beats from first 5 min for training and other 25 min 60, 30), LBBB(28,14), PVC (45, 25), Atrial fibrillation(30,25), Ventricular fibrillation(28,21), Complete heart block-CHB(28,21), Ischaemic cardiomyopathy(30,18), Sick sinus syndrome(28,14), LBBB (60, 30), PVC (45, 25), Atrial fibrillation(30,20), Ventricular fibrillation(28,21), CHB(28,21), Ischaemic cardiomyopathy(30,18), Sick sinus syndrome (Abnormal: RBBB, LBBB, Paced, PVC, Life threatening: Sick sinus syndrome, Ischemic heart diseases, Ventricular vibrillation beat. 600 beats for training and 400 beats for testing S V. ANALYSIS OF THE SURVEY…”

mentioning

confidence: 99%

See 1 more Smart Citation

Classification of ECG signals using machine learning techniques: A survey

Jambukia

Dabhi

Prajapati

2015

2015 International Conference on Advances in Computer Engineering and Applications

186

View full text Add to dashboard Cite

Classification of electrocardiogram (ECG) signals plays an important role in diagnoses of heart diseases. An accurate ECG classification is a challenging problem. This paper presents a survey of ECG classification into arrhythmia types. Early and accurate detection of arrhythmia types is important in detecting heart diseases and choosing appropriate treatment for a patient. Different classifiers are available for ECG classification. Amongst all classifiers, artificial neural networks (ANNs) have become very popular and most widely used for ECG classification. This paper discusses the issues involved in ECG classification and presents a detailed survey of preprocessing techniques, ECG databases, feature extraction techniques, ANN based classifiers, and performance measures to address the mentioned issues. Furthermore, for each surveyed paper, our paper also presents detailed analysis of input beat selection and output of the classifiers.

show abstract

Section: Classification Of Ecg Using Neural Networkmentioning

confidence: 99%

mentioning

confidence: 99%

Classification of ECG signals using machine learning techniques: A survey

Jambukia

Dabhi

Prajapati

2015

2015 International Conference on Advances in Computer Engineering and Applications

186

View full text Add to dashboard Cite

show abstract

“…In this work, the raw ECG signals are decomposed into approximation and detail sub bands up to level 9 using Daubechies ("db6") wavelet basis function [29]. The detail coefficients contain most of the noise information.…”

Section: Data Preprocessingmentioning

confidence: 99%

Critical Evaluation of Linear Dimensionality Reduction Techniques for Cardiac Arrhythmia Classification

Rajagopal¹,

Vidhyapriya²

2016

View full text Add to dashboard Cite

Embedding the original high dimensional data in a low dimensional space helps to overcome the curse of dimensionality and removes noise. The aim of this work is to evaluate the performance of three different linear dimensionality reduction techniques (DR) techniques namely principal component analysis (PCA), multi dimensional scaling (MDS) and linear discriminant analysis (LDA) on classification of cardiac arrhythmias using probabilistic neural network classifier (PNN). The design phase of classification model comprises of the following stages: preprocessing of the cardiac signal by eliminating detail coefficients that contain noise, feature extraction through daubechies wavelet transform, dimensionality reduction through linear DR techniques specified, and arrhythmia classification using PNN. Linear dimensionality reduction techniques have simple geometric representations and simple computational properties. Entire MIT-BIH arrhythmia database is used for experimentation. The experimental results demonstrates that combination of PNN classifier (spread parameter, σ = 0.08) and PCA DR technique exhibits highest sensitivity and F score of 78.84% and 78.82% respectively with a minimum of 8 dimensions.R. Rajagopal, V. Ranganathan 2604 need to develop automation strategies for the identification of cardiac abnormalities and manage sudden cardiac death [1].Abnormality in the normal rhythm of heartbeat causes arrhythmia. Heart beats can be classified to five classes namely class N (beats originating in the sinus node), class S (Supra ventricular ectopic beats), class V (Ventricular ectopic beats), class F (Fusion beats) and class Q (unknown beats including paced beats). The important requirements of an automated system are reduced complexity, fast decision making and less memory. This can be accomplished with the usage of dimensionality reduction techniques. Dimensionality reduction is the mapping of data in higher dimensional space to meaningful representation in lower dimensional space such that dimensionality of data is reduced to manageable size. Dimensionality reduction is very important in a classification system in order to mitigate the curse of dimensionality, computational complexity and memory requirement. Curse of dimensionality means that for a given sample size, there is a maximum number of features above which the performance of a classifier gets degraded. The objective of this work is to evaluate the performance of three different linear dimensionality reduction techniques in classification of ECG arrhythmias.In general, the algorithm used for automated classification includes (1) preprocessing, (2) feature extraction, (3) dimensionality reduction and (4) classification. The preprocessing of recorded ECG signals is done in order to eliminate the important noises that degrade the classifier performance, such as baseline wandering, motion artifact, power line interference and high frequency noise. Currently, researchers use many filtering techniques like morphological filtering [2], integral coefficient band...

show abstract

“…5,6,[9][10][11][12][13][14][15][16]21,[24][25][26] Parameters such as accuracy, sensitivity, and specificity are used in the literature for evaluating the performance of a classifier. Most of the research works reported more than 90% average accuracy, average sensitivity, and average specificity taken over all 5 classes.…”

Section: Introductionmentioning

confidence: 99%