Open AccessProceedings Article
Combining general multi-class and specific two-class classifiers for improved customized ECG heartbeat classification
Can Ye,B. V. K. Vijaya Kumar,Miguel Coimbra +2 more
- pp 2428-2431
Reads0
Chats0
TLDR
The proposed approach is applied to the study of ECG heartbeat classification problem, significantly outperforming state-of-the-art methods and can also be useful in anomaly detection of other biomedical signals.Abstract:
We present an approach for customized heartbeat classification of electrocardiogram (ECG) signals, based on the construction of one general multi-class classifier and one specific two-class classifier. The general classifier is trained on a global training dataset, containing examples of all possible classes and patterns. On the other hand, the individual-specific classifier is built using a small amount of individual data, which is a binary one-against-the-rest classifier, providing discrimination between normal and abnormal patterns from that individual. Such an individual-specific classifier can be a two-class classifier or a one-class classifier, depending on the availability of abnormal patterns in the individual training dataset. The classifications from the two classifiers are fused to obtain a final decision. The proposed approach is applied to the study of ECG heartbeat classification problem, significantly outperforming state-of-the-art methods. The proposed method can also be useful in anomaly detection of other biomedical signals.read more
Citations
More filters
Journal ArticleDOI
ECG-based heartbeat classification for arrhythmia detection
TL;DR: This work surveys the current state-of-the-art methods of ECG-based automated abnormalities heartbeat classification by presenting the ECG signal preprocessing, the heartbeat segmentation techniques, the feature description methods and the learning algorithms used.
Journal ArticleDOI
Arrhythmia detection using deep convolutional neural network with long duration ECG signals.
TL;DR: A new deep learning approach for cardiac arrhythmia (17 classes) detection based on long-duration electrocardiography (ECG) signal analysis based on a new 1D-Convolutional Neural Network model (1D-CNN).
Journal ArticleDOI
Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals
TL;DR: The proposed work based on 744 segments of ECG signal is obtained from the MIT-BIH Arrhythmia database and can be applied in cloud computing or implemented in mobile devices to evaluate the cardiac health immediately with highest precision.
Journal ArticleDOI
Novel methodology of cardiac health recognition based on ECG signals and evolutionary-neural system
TL;DR: An innovative research methodology is presented that enables the efficient classification of cardiac disorders (17 classes) based on ECG signal analysis and an evolutionary-neural system and these results are some of the best results to date.
Journal ArticleDOI
Automated arrhythmia detection using novel hexadecimal local pattern and multilevel wavelet transform with ECG signals
Turker Tuncer,Sengul Dogan,Paweł Pławiak,U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya +5 more
TL;DR: DWT coupled with novel 1-dimensional hexadecimal local pattern (1D-HLP) technique are employed for automated detection of arrhythmia detection and the results show that the proposed method is more superior than other already reported classical ensemble learning and deep learning methods for arrhythmmia detection using ECG signals.
References
More filters
Williamson, estimating the support of a high-dimensional distribution
TL;DR: The algorithm is a natural extension of the support vector algorithm to the case of unlabeled data by carrying out sequential optimization over pairs of input patterns and providing a theoretical analysis of the statistical performance of the algorithm.
Journal ArticleDOI
Probability Estimates for Multi-class Classification by Pairwise Coupling
TL;DR: In this paper, the authors present two approaches for obtaining class probabilities, which can be reduced to linear systems and are easy to implement, and show conceptually and experimentally that the proposed approaches are more stable than the two existing popular methods: voting and the method by Hastie and Tibshirani (1998).
Journal ArticleDOI
A patient-adaptable ECG beat classifier using a mixture of experts approach
TL;DR: A "mixture-of-experts" (MOE) approach to develop customized electrocardiogram (EGG) beat classifier in an effort to further improve the performance of ECG processing and to offer individualized health care.
Journal ArticleDOI
A Patient-Adapting Heartbeat Classifier Using ECG Morphology and Heartbeat Interval Features
P. de Chazal,Richard B. Reilly +1 more
TL;DR: The results of this study show that the performance of a patient adaptable classifier increases with the amount of training of the system on the local record and the performance can be significantly boosted with a small amount of adaptation.
Journal ArticleDOI
A Generic and Robust System for Automated Patient-Specific Classification of ECG Signals
TL;DR: This paper presents a generic and patient-specific classification system designed for robust and accurate detection of ECG heartbeat patterns that can adapt to significant interpatient variations in ECG patterns by training the optimal network structure, and achieves higher accuracy over larger datasets.