Discrimination ability of time-domain features and rules for arrhythmia classification
Mathematical and Computational Applications, cilt.17, sa.2, ss.111-120, 2012 (Scopus, TRDizin)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 17 Sayı: 2
- Basım Tarihi: 2012
- Doi Numarası: 10.3390/mca17020111
- Dergi Adı: Mathematical and Computational Applications
- Derginin Tarandığı İndeksler: Scopus, TR DİZİN (ULAKBİM)
- Sayfa Sayıları: ss.111-120
- Anahtar Kelimeler: Arrhythmia, C4.5, Classification, ECG, Hot spot algorithm, Multilayer perceptron (MLP) and support vector machines (SVM), Naive bayes, Rule extraction
- Boğaziçi Üniversitesi Adresli: Evet
Özet
This study investigates relevant diagnosis information for arrhythmia classification from previously collected cardiac data. Discrimination ability of various time-domain attributes and rules were discussed for automatic diagnosis of arrythmia using electrocardiogram (ECG) signals. Naive Bayes, C4.5, multilayer perceptron (MLP) and support vector machines (SVM) algorithms were tested on a number of the input features selected by correlative feature selection (CFS) method. Hot Spot algorithm was employed to extract a number of rules that is useful in diagnosing cardiac problems from ECG signal. 257 time domain features of 452 cases from a cardiac arrhythmia database [1] were used. Various testing configurations and performance measures such as accuracy, TP and FP rates, precision, recall and AUC were considered. The discrimination ability of selected-features and the extracted-rules were demonstrated.