Open AccessA SOLUTION FOR IMPROVEMENT OF ECG ARRHYTHMIA RECOGNITION USING RESPIRATION INFORMATION
Author(s) -
Trần Hoài Linh
Publication year - 2018
Publication title -
vietnam journal of science and technology/science and technology
Language(s) - English
Resource type - Journals
eISSN - 2815-5874
pISSN - 2525-2518
DOI - 10.15625/2525-2518/56/3/10779
Subject(s) - computer science , breathing , pattern recognition (psychology) , artificial intelligence , signal (programming language) , wavelet , respiration , speech recognition , data mining , medicine , anesthesia , anatomy , programming language
Electrocardiogram (ECG) and respiration signals are two basic and important and valuable biomedical signals as source of information used to determine a person's health status. However, ECG signals are usually of small amplitude and are susceptible to various noises such as: the 50Hz grid noise, poor electrodes’ contacts with the patient's skin, the patient's emotional variations, the respiration and movement of the patient... The idea in this paper by filtering out the effect of the respiration in the ECG signal or by incorporating the information of breathing stage into the ECG signal classification the we can improve the reliability and accuracy of the arrythmia classification. This paper proposes a solution, which uses wavelet filter to reduce the effect of respiration in the ECG signals and will use additional information from the breathing rhythm (when available) to help better classifying the arrythmias. As the main nonlinear classifier we use the classical neuro-fuzzy TSK network. The proposed solution will be tested with data from the MIT-BIH and the MGH/MF databases.