ECG beat classifcation using a cost sensitive classifier

Zahia Zidelmala (1) , Ahmed Amirou (1) , Djaffar Ould Abdeslam (2) , Jean Merckle (2)

1 LAMPA - Laboratoire d'Analyse & Modélisation des Phénomènes Aléatoires [Tizi-Ouzou]
2 MIPS - Modélisation, Intelligence, Processus et Système

In this paper, we introduce a new system for ECG beat classification using Support Vector Machines (SVMs) classifier with rejection. After ECG preprocessing, the QRS complexes are detected and segmented. A set of features including frequency information, RR intervals, QRS morphology and AC power of QRS detail coefficients is exploited to characterize each beat. An SVM follows to classify the feature vectors. Our decision rule uses dynamic reject thresholds following the cost of misclassifying a sample and the cost of rejecting a sample. Significant performance enhancement is observed when the proposed approach is tested with the MIT-BIH arrhythmia database. The achieved results are represented by the average accuracy of 97.2% with no rejection and 98.8% for the minimal classification cost.

Mots clés: ECG beat classification; Support Vector Machines (SVMs); Classification cost

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Zahia Zidelmala, Ahmed Amirou, Djaffar Ould Abdeslam, Jean Merckle. ECG beat classifcation using a cost sensitive classifier. ECG beat classification using a cost sensitive classifier, 2013, 111 (3), pp.570-577. ⟨10.1016/j.cmpb.2013.05.011⟩. ⟨hal-00985552⟩