Automated classification of five arrhythmias and normal sinus rhythm based on RR interval signals
Article
Faust, Oliver and Acharya, U. Rajendra. 2021. "Automated classification of five arrhythmias and normal sinus rhythm based on RR interval signals." Expert Systems with Applications. 181. https://doi.org/10.1016/j.eswa.2021.115031
| Article Title | Automated classification of five arrhythmias and normal sinus rhythm based on RR interval signals |
|---|---|
| ERA Journal ID | 17852 |
| Article Category | Article |
| Authors | Faust, Oliver and Acharya, U. Rajendra |
| Journal Title | Expert Systems with Applications |
| Journal Citation | 181 |
| Article Number | 115031 |
| Number of Pages | 15 |
| Year | 2021 |
| Publisher | Elsevier |
| ISSN | 0957-4174 |
| 1873-6793 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.eswa.2021.115031 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0957417421004723 |
| Abstract | Arrhythmias are abnormal heart rhythms that can be life-threatening. Atrial Fibrillation (AFIB), Atrial Flutter (AFL), Supraventricular Tachycardia (SVT), Sinus Tachycardia (ST), and Sinus Bradycardia (SB) are common arrhythmias that affect a growing number of patients. In this paper we describe a method to detect these arrhythmias in RR interval signals. We propose a deep learning algorithm to discriminate these fife arrhythmias and Normal Sinus Rhythm (NSR). The deep learning model was trained and tested with data from 10093 subjects. We used 10-fold cross-validation to establish the performance results. The overall accuracy for the six-class problem was 98.37%. When considering the binary problem of arrhythmia versus NSR, where the arrhythmia group is formed by combining the data from all fife arrythmias, the performance results are: Accuracy (ACC) = 98.55%, Sensitivity (SEN) = 99.40%, Specificity (SPE) = 94.30%. These results indicate that it is possible to discriminate RR interval sequences from SVT, ST, SB, AFIB, AFL, and NSR subjects with minimal error. Furthermore, the proposed model can provide a robust and independent second opinion when it comes to a decision if arrhythmia is present or not. Another positive aspect of the proposed arrhythmia detection algorithm is economic viability. RR interval signals are cost-effective to measure, communicate, and process. The discriminate powers of the proposed algorithm together with the advent of wearable technology and m-health infrastructure might lead to pervasive long-term arrhythmia monitoring. The detection results can support early diagnosis which helps to reduce the burden of the disease. |
| Keywords | Arrhythmia detection; Computer aided diagnosis ; Deep learning ; Residual Neural Network ; M-health |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | Sheffield Hallam University, United Kingdom |
| Ngee Ann Polytechnic, Singapore | |
| Asia University, Taiwan | |
| School of Management and Enterprise |
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