A ROBUST METHOD FOR CLASSIFICATION OF MYOCARDIAL INFARCTION SIGNALS FROM VIDEO IMAGES USING FAST ICA AND ANFIS

Abstract

This paper presents a simple, low-cost method for measuring multiple physiological parameters using fast ica and an intelligents system to classify myocardial infarction signal using adaptive neuro-fuzzy inference system (ANFIS) model, using a basic webcam. By applying FAST ICA algorithm for independent component analysis on the color channels in video recordings, we extract the blood volume pulse from the facial regions. Heart rate (HR), respiratory rate, and HR variability were subsequently quantified. The developed method classifies cardiac signal as normal or carrying an AtrioVentricular heart Block (AVB).