Support vector machine-based expert system for reliable heartbeat recognition

By Stanislaw Osowski, Tran Haoi Linh, Tomasz Markiewicz. IEEE Transactions on Biomedical Engineering, 2004

480 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/3d1cbdaaee0d364dae6817cb7187ed5fb9fa23fa

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In Biomedical Engineering

In Cardiology and Cardiovascular Medicine

In Cognitive Neuroscience

Citations in the corpus

ECG Classification Using Wavelet Packet Entropy and Random Forests (2016, methodology): “Osowski et al. [17] presented a new approach for ECG classiﬁcation by combining SVM with the features extracted by two preprocessing methods, and the results on recognizing the 13 heart rhythm types showed that the proposed method was reliable and advantageous.”
Classification of Electrocardiogram Signals With Support Vector Machines and Particle Swarm Optimization (2008, methodology): “... in Section IV, the proposed PSO‐SVM classification system aims at enhancing the SVM classification process from two different viewpoints: 1) by automatically detecting a feature subspace of higher generalization capability in order to deal in a more effective way with the curse of dimensionality”
Block-Based Neural Networks for Personalized ECG Signal Classification (2007, methodology): “Classification techniques for ECG patterns include linear discriminant analysis [5], support vector machines [8], artificial neural networks (NNs) [9]–[14], mixture-of-experts method [12], and statistical Markov models [15], [16].”
Automated Atrial Fibrillation Detection using a Hybrid CNN-LSTM Network on Imbalanced ECG Datasets (2021, background): “In the last decades there has been a significant growth of interest in the field of automated classification of various heart arrhythmias based on ECG signals proposing machine learning (ML) techniques including support vector machines (SVM) [10], k-nearest neighbors algorithm (KNN) [11], principal ”
Heartbeat Classification Using Morphological and Dynamic Features of ECG Signals (2012, background): “All SVM algorithms are implemented using the LIBSVM [37] package.”
Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks (2016, methodology): “There have been several methods for generic and fully automatic ECG classification based on signal processing techniques, such as frequency analysis [2], wavelet transform [3] and filter banks [4], statistical [5] and heuristic approaches [6], hidden Markov models [7], support vector machines [8], a”
A Generic and Robust System for Automated Patient-Specific Classification of ECG Signals (2009, background): “wavelet transform [3], [4], filter banks [5], statistical [6] and heuristic approaches [7], hidden Markov models [8], support vector machines [9], artificial neural networks (ANNs) [10],”
ECG-based heartbeat classification for arrhythmia detection: A survey (2016, methodology): “Other techniques have also been employed, such as linear predictive coding [87], high order accumulates [88,89], clustering [84,90,91], correlation dimension and largest Lyapunov exponent [92,93], Hermite transform [94], local fractal dimension [95].”