Active Broad Learning System for ECG Arrhythmia Classification

Abstract This paper presents an active and incremental learning system called active broad learning system (ABLS) for ECG arrhythmia classification to reduce the time-consumption of training and labor cost of experts labeling beats. An effective strategy is designed to convert the actual outputs in broad learning system (BLS) into approximated posterior probabilities for active learning to select the most valuable beats from unlabeled beats. The proposed ABLS is first pre-trained with a small number of labeled beats and then incremental trained with the selected beats labeled by the expert to fine-tune the connection weight. Due to the structural characteristics, ABLS does not need to retrain all the beats, which can greatly reduce the time-consumption. The experimental results on the MIT-BIH arrhythmia database show ABLS can greatly reduce the number of beats that need to be labeled and consume very little training time while maintaining excellent performance compared to state-of-the-art methods.

[1]  Amit K. Mishra,et al.  Local fractal dimension based ECG arrhythmia classification , 2010, Biomed. Signal Process. Control..

[2]  U. Rajendra Acharya,et al.  A deep convolutional neural network model to classify heartbeats , 2017, Comput. Biol. Medicine.

[3]  Dong Yu,et al.  Active Learning and Semi-supervised Learning for Speech Recognition: a Unified Framework Using the Global Entropy Reduction Maximization Criterion Computer Speech and Language Article in Press Active Learning and Semi-supervised Learning for Speech Recognition: a Unified Framework Using the Global E , 2022 .

[4]  G. Sayantan,et al.  Classification of ECG beats using deep belief network and active learning , 2018, Medical & Biological Engineering & Computing.

[5]  Eric A. Wan,et al.  Neural network classification: a Bayesian interpretation , 1990, IEEE Trans. Neural Networks.

[6]  Bruce W. Suter,et al.  The multilayer perceptron as an approximation to a Bayes optimal discriminant function , 1990, IEEE Trans. Neural Networks.

[7]  Di Wang,et al.  Automatic recognition of arrhythmia based on principal component analysis network and linear support vector machine , 2018, Comput. Biol. Medicine.

[8]  Daban Abdulsalam Abdullah,et al.  Local feature descriptors based ECG beat classification , 2020, Health Information Science and Systems.

[9]  W.J. Tompkins,et al.  A patient-adaptable ECG beat classifier using a mixture of experts approach , 1997, IEEE Transactions on Biomedical Engineering.

[10]  Yu Tian,et al.  High-Performance Personalized Heartbeat Classification Model for Long-Term ECG Signal , 2017, IEEE Transactions on Biomedical Engineering.

[11]  Moncef Gabbouj,et al.  Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks , 2016, IEEE Transactions on Biomedical Engineering.

[12]  Remco C. Veltkamp,et al.  Interactive rodent behavior annotation in video using active learning , 2019, Multimedia Tools and Applications.

[13]  Deyu Meng,et al.  Hyperspectral Image Classification With Convolutional Neural Network and Active Learning , 2020, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Philip de Chazal,et al.  Automatic classification of heartbeats using ECG morphology and heartbeat interval features , 2004, IEEE Transactions on Biomedical Engineering.

[15]  K. J. Liu,et al.  A high-resolution technique for multidimensional NMR spectroscopy , 1998, IEEE Transactions on Biomedical Engineering.

[16]  Jin Yuan,et al.  Multi-criteria active deep learning for image classification , 2019, Knowl. Based Syst..

[17]  Che Wun Chiou,et al.  Analyzing ECG for cardiac arrhythmia using cluster analysis , 2012, Expert Syst. Appl..

[18]  Yaqin Zhao,et al.  ECG arrhythmia classification using artificial intelligence and nonlinear and nonstationary decomposition , 2019, Signal, Image and Video Processing.

[19]  Farid Melgani,et al.  Active Learning Methods for Electrocardiographic Signal Classification , 2010, IEEE Transactions on Information Technology in Biomedicine.

[20]  Moncef Gabbouj,et al.  A Generic and Robust System for Automated Patient-Specific Classification of ECG Signals , 2009, IEEE Transactions on Biomedical Engineering.

[21]  Sung-Nien Yu,et al.  Electrocardiogram beat classification based on wavelet transformation and probabilistic neural network , 2007, Pattern Recognit. Lett..

[22]  Elif Derya Übeyli,et al.  ECG beat classifier designed by combined neural network model , 2005, Pattern Recognit..

[23]  Li Chen,et al.  Visualization-Based Active Learning for Video Annotation , 2016, IEEE Transactions on Multimedia.

[24]  Brian Mac Namee,et al.  Active learning for text classification with reusability , 2016, Expert Syst. Appl..

[25]  Cong Wang,et al.  ECG beat classification via deterministic learning , 2017, Neurocomputing.

[26]  Mouloud Koudil,et al.  A Novel Active Learning Method Using SVM for Text Classification , 2018, Int. J. Autom. Comput..

[27]  C. L. Philip Chen,et al.  Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[28]  Pu Wang,et al.  LSTM-Based Auto-Encoder Model for ECG Arrhythmias Classification , 2020, IEEE Transactions on Instrumentation and Measurement.

[29]  Robert D. Nowak,et al.  Minimax Bounds for Active Learning , 2007, IEEE Transactions on Information Theory.

[30]  Rong Jin,et al.  Active Learning by Querying Informative and Representative Examples , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  Sung-Nien Yu,et al.  Integration of independent component analysis and neural networks for ECG beat classification , 2008, Expert Syst. Appl..

[32]  Bo Du,et al.  A Novel Semisupervised Active-Learning Algorithm for Hyperspectral Image Classification , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[33]  Philip de Chazal,et al.  A Patient-Adapting Heartbeat Classifier Using ECG Morphology and Heartbeat Interval Features , 2006, IEEE Transactions on Biomedical Engineering.

[34]  William J. Emery,et al.  SVM Active Learning Approach for Image Classification Using Spatial Information , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[35]  Dilek Z. Hakkani-Tür,et al.  Active learning: theory and applications to automatic speech recognition , 2005, IEEE Transactions on Speech and Audio Processing.