Artificial Intelligence Methods Applied to Parameter Detection of Atrial Fibrillation

[摘要] 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%).

[发布机构] Department of Biomedical Sciences, Grigore T. Popa University of Medicine and Pharmacy, Iasi, Romania^1

[效力级别] 医药卫生 [学科分类] 卫生学

[关键词] Artificial intelligence methods;Atrial fibrillation;Decision systems;Parameter detection;Root Mean Square;Sensitivity and specificity;Single objective;Statistical descriptors