Singh, Ritu and Rajpal, Navin and Mehta, Rajesh (2020) Wavelet and kernel dimensional reduction on arrhythmia classification of ECG signals. EAI Endorsed Transactions on Scalable Information Systems, 7 (26): e6. ISSN 2032-9407
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eai.13-7-2018.163095.pdf - Published Version Available under License Creative Commons Attribution No Derivatives. Download (4MB) | Preview |
Abstract
Electrocardiogram (ECG) monitoring is continuously required to detect cardiac ailments. At times it is challenging to interpret the differences in the P- QRS-T curve. The proposed approach aims to show the excellence of kernel capabilities of Kernel Principal Component Analysis (KPCA) and Kernel Independent Component Analysis (KICA) in the wavelet domain. In this work, experiments are performed using five different categories of cardiac beats. The supervised classifiers like feed-forward neural network (FNN), backpropagation neural network (BPNN), and K nearest neighbor (KNN) statistically evaluates the impact of discrete wavelet with KPCA and KICA on extracted beats. The performance evaluation also compares the outcomes with existing techniques. The obtained results justify the supremacy of the combination of wavelet, kernel, and KNN approach, yielding a 99.7 % classification success rate. The five-fold crossvalidation scheme is used for measuring the efficacy of classifiers.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Electrocardiogram, MIT/BIH, Discrete Wavelet Transform, Kernel, classifiers |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science QA75 Electronic computers. Computer science |
| Depositing User: | EAI Editor II. |
| Date Deposited: | 08 Oct 2020 13:51 |
| Last Modified: | 08 Oct 2020 13:51 |
| URI: | https://eprints.eudl.eu/id/eprint/670 |
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