Open AccessHeartbeat classification with low computational cost using Hjorth parameters
Author(s) -
Leite João Paulo R.R.,
Moreno Robson L.
Publication year - 2018
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
eISSN - 1751-9683
pISSN - 1751-9675
DOI - 10.1049/iet-spr.2017.0296
Subject(s) - heartbeat , computer science , feature extraction , pattern recognition (psychology) , artificial intelligence , sensitivity (control systems) , signal processing , speech recognition , digital signal processing , electronic engineering , computer hardware , computer security , engineering
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.