Block-Based Neural Networks for Personalized ECG Signal Classification

By Wei Jiang, Seong-Gon Kong. IEEE Transactions on Neural Networks, 2007

387 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cardiology and Cardiovascular Medicine, Signal Processing

View Paper: https://www.semanticscholar.org/paper/5a4b6da5e14a61b6ed47679d6999b3d18a8f1cbb

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. B. V. Vijaya Kumar
2. Matin Hashemi
3. Juan Pablo Martínez
4. Serkan Kiranyaz
5. Farid Melgani
6. Yakoub Bazi
7. Can Ye
8. Mohammadhosein Oveisi

In Cardiology and Cardiovascular Medicine

1. B. V. Vijaya Kumar
2. Can Ye
3. Miguel Tavares Coimbra
4. Matin Hashemi
5. Mohammadhosein Oveisi
6. Saeed Saadatnejad
7. Mariano Llamedo
8. Serkan Kiranyaz

In Signal Processing

1. Juan Pablo Martínez
2. Mariano Llamedo
3. B. V. Vijaya Kumar
4. Can Ye
5. Miguel Tavares Coimbra
6. Matin Hashemi
7. Mohammadhosein Oveisi
8. Saeed Saadatnejad

Citations in the corpus

• Deep learning approach for active classification of electrocardiogram signals (2016, methodology): “In particular, we use the standard measures: sensitivity , positive predictive value , specificity ), overall accuracy [14,34,35].”
• Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks (2016, methodology): “[15] W. Jiang and S. G. Kong, “Block-based neural networks for personalized ECG signal classification,” IEEE Trans.”
• A Generic and Robust System for Automated Patient-Specific Classification of ECG Signals (2009, methodology): “other hand, algorithms based on patient-adaptive architecture have demonstrated significant performance improvement over conventional global classifiers [11], [13], [16].”
• 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…”
• Heartbeat Classification Using Feature Selection Driven by Database Generalization Criteria (2011, background): “In terms of the data division, some works performed a beat-oriented division, no matter which subject the heartbeats belong to, so that both the training and testing datasets contain heartbeats from the same subjects [5], [9], [11].”
• Heartbeat Classification Using Morphological and Dynamic Features of ECG Signals (2012, methodology): “A number of machine learning algorithms have been proposed for classification, such as self-organizing map (SOM) [6], linear discriminats (LDs) [8], [12], [15], decision tree [10], support vector machine (SVM) [9], [11], artificial neural network (ANN) [13], [14], dynamic Bayesian network (DBN) [16]”
• LSTM-Based ECG Classification for Continuous Monitoring on Personal Wearable Devices (2018, methodology): “Jiang and Kong [16] proposed a block-based neural network algorithm, and Ince et al.”