ECG beat recognition using fuzzy hybrid neural network

By Stanislaw Osowski, Tran Haoi Linh. IEEE Transactions on Biomedical Engineering, 2001

577 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cardiology and Cardiovascular Medicine, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/6894579ee2beb441b5f8ac92ab6062b94b29e6d0

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In Biomedical Engineering

In Cardiology and Cardiovascular Medicine

In Cognitive Neuroscience

Citations in the corpus

Classification of Electrocardiogram Signals With Support Vector Machines and Particle Swarm Optimization (2008, methodology): “Among the most recently published works are those presented in [1]–[10].”
Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals (2016, methodology): “For example, the use of higher order spectra(HOS) cumulants provides very good recognition and is less affected by the morphological changes of the ECG signal(Martis et al., 2014; Osowski and Linh, 2001).”
Optimization of ECG Classification by Means of Feature Selection (2011, background): “…perceptrons (MLPs) are the most popular family of ANN within ECG classiﬁcation [13]–[18], but other ANN paradigms, such as fuzzy neural networks [14], [19]–[25], radial basis networks [26], [27], support vector machines [28], [29], and self-organizing maps [13], [25], [30], [31], have been also…”
A Patient-Adapting Heartbeat Classifier Using ECG Morphology and Heartbeat Interval Features (2006, background): “Features include ECG morphology [8], [9], [13], [14], heartbeat interval features [8], [9], [11], [13], [14], frequency based features [7], higher order cumulant features [10], Karhunen-Loeve expansion of ECG morphology [11], and hermite polynomials [12].”
ECG-based heartbeat classification for arrhythmia detection: A survey (2016, methodology): “However, in [121,84,122,88], it was proposed a hybrid neuro-fuzzy network methods in order to minimize the problems of MLP, increasing its generalization and reducing its training time.”
Automatic classification of heartbeats using ECG morphology and heartbeat interval features (2004, methodology): “, [7], [9], [10]) and should be avoided.”
ECG Classification Using Wavelet Packet Entropy and Random Forests (2016, background): “Some other researchers investigated the performance of fuzzy NN [28] and combined NN [29] on ECG classiﬁcation [2].”
Support vector machine-based expert system for reliable heartbeat recognition (2004, background): “classifier, the paper [6] has reported the mean error of 3.”