Computer aided diagnosis of ECG data on the least square support vector machine
Küçük Resim Yok
Tarih
2008
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
ACADEMIC PRESS INC ELSEVIER SCIENCE
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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. (C) 2007 Elsevier Inc. All rights reserved.
Açıklama
Anahtar Kelimeler
ECG dataset, least square support vector machine, ROC curves
Kaynak
DIGITAL SIGNAL PROCESSING
WoS Q Değeri
Q2
Scopus Q Değeri
Q2
Cilt
18
Sayı
1
