Chia, Nyoke Goon and Hau, Yuan Wen and Jamaludin, Mohd. Najeb (2017) Robust arrhythmia classifier using wavelet transform and support vector machine classification. In: 13th IEEE International Colloquium on Signal Processing and its Applications, CSPA 2017, 10 - 12 March 2017, Penang, Malaysia.
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Official URL: http://dx.doi.org/10.1109/CSPA.2017.8064959
Abstract
The Electrocardiogram (ECG) is the most widely used signal in clinical practice for the assessment of cardiac condition. This paper presents a robust arrhythmia classifier based on the combination of wavelet transform and timing features, as well as support vector machine classification technique. The proposed technique is able to detect a total of 11 different types of arrhythmia. Results show that the average classification accuracy is up to 87.93% using the 46 MIT-BIH offline ECG database as the testing dataset. A user-friendly Graphical User Interface (GUI) is developed to ease the layman users. This proposed tool aims to reduce the workload of cardiac vascular technologist, medical staff and physicians as assisting cardiac monitoring equipment.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Uncontrolled Keywords: | arrhythmia, electrocardiograph (ECG), support vector machine (SVM) |
| Subjects: | Q Science > Q Science (General) |
| Divisions: | Biosciences and Medical Engineering |
| ID Code: | 97277 |
| Deposited By: | Narimah Nawil |
| Deposited On: | 26 Sep 2022 03:27 |
| Last Modified: | 26 Sep 2022 03:27 |
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