Title :
Cardiac Beat Classification using a Fuzzy Inference System
Author :
Monzon, Jorge E. ; Pisarello, Maria I.
Author_Institution :
Univ. Nacional del Nordeste, Corrientes
fDate :
6/27/1905 12:00:00 AM
Abstract :
This paper presents an adaptive-network-based fuzzy inference system (ANFIS) as a cardiac beat detector, able to classify normal vs. premature ventricular contractions. We used records from the MIT Arrhythmia Database and in-vivo records from cardiac voluntary patients to train and test our system. The system identifies premature ventricular contractions (PVC) within reasonable accuracy and compares favorably to other methods reported in the literature
Keywords :
electrocardiography; fuzzy set theory; medical signal processing; signal classification; ANFIS; adaptive-network-based fuzzy inference system; cardiac beat classification; cardiac beat detector; cardiac voluntary patients; premature ventricular contractions; Adaptive systems; Biomedical signal processing; Databases; Detectors; Electrocardiography; Fuzzy systems; Heart rate variability; Inference algorithms; Signal processing algorithms; System testing;
Conference_Titel :
Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the
Conference_Location :
Shanghai
Print_ISBN :
0-7803-8741-4
DOI :
10.1109/IEMBS.2005.1615750
