Artificial Intelligence Methods Applied to Parameter Detection of Atrial Fibrillation. (August 2015)

• Record Type: Journal Article
• Authors: Arotaritei, D; Rotariu, C
• Is Part Of: Journal of physics. Number 637(2015)
• Publication Date: 2015-08
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/637/1/012023
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: In this paper we present a novel method to develop an atrial fibrillation (AF) based on statistical descriptors and hybrid neuro-fuzzy and crisp system. The inference of system produce rules of type if-then-else that care extracted to construct a binary decision system: normal of atrial fibrillation. We use TPR (Turning Point Ratio), SE (Shannon Entropy) and RMSSD (Root Mean Square of Successive Differences) along with a new descriptor, Teager- Kaiser energy, in order to improve the accuracy of detection. The descriptors are calculated over a sliding window that produce very large number of vectors (massive dataset) used by classifier. The length of window is a crisp descriptor meanwhile the rest of descriptors are interval-valued type. The parameters of hybrid system are adapted using Genetic Algorithm (GA) algorithm with fitness single objective target: highest values for sensibility and sensitivity. The rules are extracted and they are part of the decision system. The proposed method was tested using the Physionet MIT-BIH Atrial Fibrillation Database and the experimental results revealed a good accuracy of AF detection in terms of sensitivity and specificity (above 90%).