Cardiac Beat Classification using a Fuzzy Inference System

Author

Monzon, Jorge E. ; Pisarello, Maria I.

Author_Institution

Univ. Nacional del Nordeste, Corrientes

fYear

2005

fDate

6/27/1905 12:00:00 AM

Firstpage

5582

Lastpage

5584

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/IEMBS.2005.1615750

Filename

1615750

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1582534