Key result
A heartbeat classification method using Hjorth parameters achieved an overall accuracy of 90.4% in the MIT-BIH Arrhythmia database.
Why the study?
Does an ECG feature extraction method using Hjorth parameters improve automatic heartbeat classification accuracy compared to other state-of-the-art methods?
Does an ECG feature extraction method using Hjorth parameters improve automatic heartbeat classification accuracy compared to other state-of-the-art methods?
A novel ECG feature extraction method utilizing Hjorth parameters achieves 90.4% accuracy in automatic heartbeat classification with low computational cost, suitable for hardware devices with limited resources.
Supports exploration of Hjorth parameters for efficient heartbeat classification; leaves open real-world accuracy versus established methods.
A method for electrocardiogram (ECG) feature extraction is presented for automatic classification of heartbeats, using values of RR intervals, amplitude and Hjorth parameters. Hjorth parameters have been used in a variety of research areas, but their application to ECG signal processing is still little explored. This paper also introduces a new approach to heartbeat segmentation, which avoids mixing information from adjacent beats and improves classification performance. The proposed model is validated in the Massachusetts Institute of Technology ‐ Beth Israel Hospital (MIT‐BIH) Arrhythmia database and presents an overall accuracy of 90.4%, better than other state‐of‐the‐art methods. There is an improvement over other models in positive predictivity for class S (66.6%) of supraventricular ectopic beats, and sensitivity for class N (93.0%). Results obtained indicate that the techniques used in this study can be successfully applied to the problem of automatic heartbeat classification. In addition, this new approach has low computational cost, which allows its later implementation in hardware devices with limited resources.
No takes yet. Share an insight, caveat, or question.
Leite et al. (2017) studied Arrhythmia. Heartbeat classification using Hjorth parameters and new segmentation approach vs. Other state-of-the-art methods was evaluated on Overall accuracy of heartbeat classification. A heartbeat classification method using Hjorth parameters achieved an overall accuracy of 90.4% in the MIT-BIH Arrhythmia database.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: