Journal ArticleDOI
Robust electrocardiogram (ECG) beat classification using discrete wavelet transform.
TLDR
This paper presents a robust technique for the classification of six types of heartbeats through an electrocardiogram (ECG) using a wavelet transform along with the instantaneous RR-interval, which offers substantial advantages over previous techniques for implementation in a practical ECG analyzer.Abstract:
This paper presents a robust technique for the classification of six types of heartbeats through an electrocardiogram (ECG). Features extracted from the QRS complex of the ECG using a wavelet transform along with the instantaneous RR-interval are used for beat classification. The wavelet transform utilized for feature extraction in this paper can also be employed for QRS delineation, leading to reduction in overall system complexity as no separate feature extraction stage would be required in the practical implementation of the system. Only 11 features are used for beat classification with the classification accuracy of approximately 99.5% through a KNN classifier. Another main advantage of this method is its robustness to noise, which is illustrated in this paper through experimental results. Furthermore, principal component analysis (PCA) has been used for feature reduction, which reduces the number of features from 11 to 6 while retaining the high beat classification accuracy. Due to reduction in computational complexity (using six features, the time required is approximately 4 ms per beat), a simple classifier and noise robustness (at 10 dB signal-to-noise ratio, accuracy is 95%), this method offers substantial advantages over previous techniques for implementation in a practical ECG analyzer.read more
Citations
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Journal ArticleDOI
ECG-based heartbeat classification for arrhythmia detection
TL;DR: This work surveys the current state-of-the-art methods of ECG-based automated abnormalities heartbeat classification by presenting the ECG signal preprocessing, the heartbeat segmentation techniques, the feature description methods and the learning algorithms used.
Journal ArticleDOI
Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals
TL;DR: This paper investigates the representation ability of linear and nonlinear features and proposes a combination of such features in order to improve the classification of ECG data and is able to classify the N, S, V, F and U arrhythmia classes with high accuracy.
Journal ArticleDOI
A deep learning approach for ECG-based heartbeat classification for arrhythmia detection
Giovanna Sannino,G. De Pietro +1 more
TL;DR: A novel deep learning approach for ECG beat classification is proposed that is not only more efficient than the state of the art in terms of accuracy, but also competitive in Terms of sensitivity and specificity.
Journal ArticleDOI
Heartbeat classification using disease-specific feature selection
TL;DR: A novel disease-specific feature selection method which consists of a one-versus-one (OvO) features ranking stage and a feature search stage wrapped in the same OvO-rule support vector machine (SVM) binary classifier.
Journal ArticleDOI
Current methods in electrocardiogram characterization
TL;DR: Nonlinear methods which can capture the small variations in the ECG signal and provide improved accuracy in the presence of noise are discussed in greater detail in this review.
References
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Book
Characterization of Signals From Multiscale Edges
Stéphane Mallat,S. Zhong +1 more
TL;DR: The authors describe an algorithm that reconstructs a close approximation of 1-D and 2-D signals from their multiscale edges and shows that the evolution of wavelet local maxima across scales characterize the local shape of irregular structures.
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Detection of ECG characteristic points using wavelet transforms
TL;DR: An algorithm based on wavelet transforms (WT's) has been developed for detecting ECG characteristic points and the relation between the characteristic points of ECG signal and those of modulus maximum pairs of its WT's is illustrated.
Journal ArticleDOI
A wavelet-based ECG delineator: evaluation on standard databases
TL;DR: A robust single-lead electrocardiogram (ECG) delineation system based on the wavelet transform (WT), outperforming the results of other well known algorithms, especially in determining the end of T wave.
Journal ArticleDOI
The principles of software QRS detection
TL;DR: The authors provide an overview of these recent developments as well as of formerly proposed algorithms for QRS detection, which reflects the electrical activity within the heart during the ventricular contraction.
Journal ArticleDOI
ECG beat recognition using fuzzy hybrid neural network
Stanislaw Osowski,T.H. Linh +1 more
TL;DR: The results of experiments of recognition of different types of beats on the basis of the ECG waveforms have confirmed good efficiency of the proposed solution and show that the method may find practical application in the recognition and classification of different type heart beats.