Name
Abstract | ||
|---|---|---|
In this paper we describe a technique that has successfully classified arrhythmia from an ECG dataset using a least square support vector machine (LSSVM). LSSVM was applied to the ECG dataset to distinguish between healthy persons and diseased persons (arrhythmia). The LSSVM classifier trained with four train-test parts including a training-to-test split of 50-50%, a training-to-test split of 70-30%, and a training-to-test split of 80-20%. We have used the classification accuracy, sensitivity and specificity analysis, and ROC curves to test the performance of LSSVM classifier on the detection of ECG arrhythmia. The classification accuracies obtained are 100% for all the training-to-test splits. These results show that the proposed method is more promising than previously reported classification techniques. The results suggest that the proposed method can be used to enhance the performance of a new intelligent assistance diagnosis system. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.dsp.2007.05.006 | Digital Signal Processing |
Keywords | Field | DocType |
square support vector machine,lssvm classifier,classified arrhythmia,roc curves,least square support vector machine,ecg dataset,roc curve,classification accuracy,diseased person,classification technique,ecg arrhythmia,training-to-test split,least squares support vector machine | Least squares,Receiver operating characteristic,Pattern recognition,Computer science,Support vector machine,Computer-aided diagnosis,Artificial intelligence,Classifier (linguistics),Machine learning | Journal |
Volume | Issue | ISSN |
18 | 1 | Digital Signal Processing |
Citations | PageRank | References |
15 | 1.16 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kemal Polat | 1 | 1348 | 97.38 |
| Bayram Akdemir | 2 | 37 | 6.32 |
| Salih Güneş | 3 | 1267 | 78.53 |