Currently, no reliable method exists to detect Premature Atrial Complexes (PAC). The detection of PACs is clinically essential to predict supraventricular tachycardia, postoperative atrial fibrillation and paroxysmal atrial fibrillation. We propose an algorithm for intra-class classification that includes an analysis of the R-R time series. In the pre-processing phase, we used Butterworth filters to remove the baseline wander and the other noise. In the feature extraction phase, we detected the RR interval duration and the distance between the occurrence of P wave and T wave. Using these features we applied Fisher's Linear Discriminant to create a criterion that can be used for classification. Combining pre-processing, feature extraction and Fisher's Linear Discriminant we succeed in separating Normal and PAC beats with 99% Accuracy. � 2008 IEEE.
|Title of host publication||Proceedings of the 7th IEEE International Conference on Cognitive Informatics ICCI 2008|
|Place of Publication||San Francisco, United States|
|Publisher||IEEE, Institute of Electrical and Electronics Engineers|
|Number of pages||6|
|Publication status||Published - 2008|
|Event||IEEE International Conference on Cognitive Informatics (ICCI) 2008 7th - Stanford, United States|
Duration: 14 Aug 2008 → 16 Aug 2008
|Conference||IEEE International Conference on Cognitive Informatics (ICCI) 2008 7th|
|Period||14/08/08 → 16/08/08|
Elgendi, M., Jonkman, M., & De Boer, F. (2008). Premature atrial complexes detection using the Fisher Linear Discriminant. In Proceedings of the 7th IEEE International Conference on Cognitive Informatics ICCI 2008 (pp. -). IEEE, Institute of Electrical and Electronics Engineers.