A SOLUTION FOR IMPROVEMENT OF ECG ARRHYTHMIA RECOGNITION USING RESPIRATION INFORMATION

Authors

  • Tran Hoai Linh Hanoi University of Science and Technology

DOI:

https://doi.org/10.15625/2525-2518/56/3/10779

Keywords:

ECG signal recognition, arrythmia, respiration, neurofuzzy network, intelligent classifier

Abstract

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.

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Author Biography

Tran Hoai Linh, Hanoi University of Science and Technology

School of Electrical Engineering,

Department of Instrumentation and Industrial Informatics,

Associate Professor, Dr. Sc.

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Published

2018-06-11

Issue

Section

Electronics - Telecommunication