Robust Neural-Network-Based Classification of Premature Ventricular Contractions Using Wavelet Transform and Timing Interval Features

By Omer T. Inan, Laurent Giovangrandi, Gregory T. A. Kovacs. IEEE Transactions on Biomedical Engineering, 2006

412 citations recorded by the source corpus.

Subfields: Cardiology and Cardiovascular Medicine, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/43a9c59b5ad8f815baa20879bbe2506c07e35186

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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].”
Optimization of ECG Classification by Means of Feature Selection (2011, neutral): “, wavelet transform (WT) [15], [33], [34], principal component analysis [26], [35] and Hermite functions [23], [29], [31],”
ECG Heartbeat Classification: A Deep Transferable Representation (2018, background): “[5], [6], [7] These handcrafted features provide us with an acceptable representation of the signal, based on recent machine learning studies, automated feature extraction and representation methods are proven to be more scalable and are capable of making more accurate predictions.”
Heartbeat Classification Using Feature Selection Driven by Database Generalization Criteria (2011, methodology): “Several classifiers were adopted in the reviewed papers, from simple linear discriminant functions based on the Gaussian assumption of the data [4], [8] to more elaborated ones, as artificial neural networks (ANN’s), self-organizing maps (SOM), and learning vector quantization (LVQ) among others [2]”
Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals (2016, neutral): “Fuzzy Hybrid NN (Osowski and Linh, 2001) Linear Discriminant Classifier (de Chazal and Reilly, 2006) NN (Inan et al., 2006) Block-based NNs (BbNNs) (Jiang and Kong, 2007) Adaptive Wavelet NN (WNN) (Lin et al., 2008) Adaptive Neuro-Fuzzy (Übeyli, 2009a) Inference System (ANFIS) Extreme Learning…”
LSTM-Based ECG Classification for Continuous Monitoring on Personal Wearable Devices (2018, methodology): “Many previous ECG classification algorithms are mainly focused on signal processing techniques including extraction of morphological features [7], frequency domain analysis [8], Hermite function decomposition [9], wavelet transform [10], [11], support vector machines [12] and hidden Markov models [1”
Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal (2008, methodology): “In a recent paper [9] another neural network-based algorithm using the wavelet transform was developed for discriminating the NSR and PVC beats.”