A Patient-Adapting Heartbeat Classifier Using ECG Morphology and Heartbeat Interval Features

By Philip de Chazal, Richard B. Reilly. IEEE Transactions on Biomedical Engineering, 2006

500 citations recorded by the source corpus.

An adaptive system for the automatic processing of the electrocardiogram (ECG) for the classification of heartbeats into one of the five beat classes recommended by ANSI/AAMI EC57:1998 standard is presented. The heartbeat classification system processes an incoming recording with a global-classifier to produce the first set of beat annotations. An expert then validates and if necessary corrects a fraction of the beats of the recording. The system then adapts by first training a local-classifier using the newly annotated beats and combines this with the global-classifier to produce an adapted classification system. The adapted system is then used to update beat annotations. 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. Crucially, the performance of the system can be significantly boosted with a small amount of adaptation even when all beats used for adaptation are from a single class. This study illustrates the ability to provide highly beneficial automatic arrhythmia monitoring and is an improvement on previously reported results for automated heartbeat classification systems

Subfields: Cardiology and Cardiovascular Medicine, Cognitive Neuroscience, Pulmonary and Respiratory Medicine

View Paper: https://www.semanticscholar.org/paper/be8f6d1da12f0f6ebb6afb8a18c8b957f98a4bb1

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In Cardiology and Cardiovascular Medicine

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Citations in the corpus

Classification of Electrocardiogram Signals With Support Vector Machines and Particle Swarm Optimization (2008, methodology): “In [9], a patient-adapting heartbeat classifier system based on linear discriminants is proposed.”
LSTM-Based ECG Classification for Continuous Monitoring on Personal Wearable Devices (2018, methodology): “Dataset B is 100, 103, 105, 111, 113, 117, 121, 123, 200, 202, 210, 212, 213, 214, 219, 221, 222, 228, 231, 232, 233 and 234 [15].”
Heartbeat classification using disease-specific feature selection (2014, methodology): “Given the importance of morphological and interval features in automatic heartbeat classification [6,12], we introduce wave-areabased features in this study to take heartbeat morphology into account for evaluating the effect of feature selection and improving the classification performance.”
Heartbeat Classification Using Morphological and Dynamic Features of ECG Signals (2012, methodology): “lowing the database division used in [8], [12], [15], and [17].”
Deep learning approach for active classification of electrocardiogram signals (2016, methodology): “In addition, since it is well known that the temporal information is important for discerning the class signatures, we add four other temporal features [14,15].”
A Generic and Robust System for Automated Patient-Specific Classification of ECG Signals (2009, background): “other hand, algorithms based on patient-adaptive architecture have demonstrated significant performance improvement over conventional global classifiers [11], [13], [16].”
Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks (2016, background): “de Chazal in [12] and [14] also comply with the AAMI standards, since they used different training and test data, it is not possible to directly compare their results with the proposed method.”
Heartbeat Classification Using Feature Selection Driven by Database Generalization Criteria (2011, methodology): “The results presented in [7], where the automatic classifier of [4] is assisted by a LE to improve its performance, are also compared in Table III.”