Open AccessEVALUATION OF ELECTROMYOGRAPHIC NOISE STATISTICAL CHARACTERISTICS IN MULTICHANNEL ECG RECORDINGS
Author(s) -
E. B. Grigoriev,
Alexander Krasichkov,
Е. М. Нифонтов
Publication year - 2019
Publication title -
izvestiâ vysših učebnyh zavedenij rossii. radioèlektronika
Language(s) - English
Resource type - Journals
eISSN - 2658-4794
pISSN - 1993-8985
DOI - 10.32603/1993-8985-2018-21-6-118-125
Subject(s) - electromyography , noise (video) , joint probability distribution , pattern recognition (psychology) , mathematics , computer science , probability density function , probability distribution , bivariate analysis , speech recognition , artificial intelligence , statistics , medicine , physical medicine and rehabilitation , image (mathematics)
Electromyographic noise is one of the most common noises in electrocardiogram. In case of several electrocardiogram leads, electromyographic noise affects each lead to different extent. It can be taken into account when developing algorithms for multilead electrocardiogram record processing. However, in the existing literature, there is no information about the relationship of electromyographic noise in various ECG leads and their joint probability distribution. The purpose of this paper is to study statistical characteristics of electromyographic noise in ECG signal, from which the electromyographic noise is extracted. The paper proposes a method for extracting electromyographic noise from electrocardiogram signal, based on a polynomial approximation of electrocardiogram signal fragments in sliding window with overlapping fragment subsequent weight averaging. Using this method, fragments of electromyographic noise are extracted from multichannel electrocardiogram records. Based on the obtained data, a joint probability distribution function of electromyographic noise in two adjacent leads is selected, and the correlation relationships between the electromyographic noise in various ECG leads are investigated. The results show that the joint probability distribution function of electromyographic noise in two adjacent leads in the first approximation can be described using bivariate normal distribution. In addition, between the samples of electromyographic noise from two adjacent leads quite strong correlation relationships can be observed.