Convolutional Neural Network for Automatic Detection of Segments Contaminated by Interference in ECG Signal

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21460%2F25%3A00387920" target="_blank" >RIV/68407700:21460/25:00387920 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.3390/ai6100250" target="_blank" >https://doi.org/10.3390/ai6100250</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/ai6100250" target="_blank" >10.3390/ai6100250</a>

Result language

angličtina

Original language name

Convolutional Neural Network for Automatic Detection of Segments Contaminated by Interference in ECG Signal

Original language description

Various types of interfering signals are an integral part of ECGs recorded using wearable electronics, specifically during field monitoring, outside the controlled environment of a medical doctor's office, or laboratory. The frequency spectrum of several types of interfering signals overlaps significantly with the ECG signal, making effective filtration impossible without losing clinically relevant information. In this article, we proceed from the practical assumption that it is unnecessary to analyze the entire ECG recording in real long-term recordings. Conversely, in the preprocessing phase, it is necessary to detect unreadable segments of the ECG signal. This paper proposes a novel method for automatically detecting unreadable segments distorted by superimposed interference in ECG recordings. The method is based on a convolutional neural network (CNN) and is comparable in quality to annotation performed by a medical expert, but incomparably faster. In a series of controlled experiments, the ECG signal was recorded during physical activities of varying intensities, and individual segments of the recordings were manually annotated based on visual assessment by a medical expert, i.e., divided into four different classes based on the intensity of distortion to the useful ECG signal. A deep convolutional model was designed and evaluated, exhibiting a 87.62% accuracy score and the same F1-score in automatic recognition of segments distorted by superimposed interference. Furthermore, the model exhibits an accuracy and F1-score of 98.70% in correctly identifying segments with visually detectable and non-detectable heart rate. The proposed interference detection procedure appears to be sufficiently effective despite its simplicity. It facilitates subsequent automatic analysis of undisturbed ECG waveform segments, which is crucial in ECG monitoring using wearable electronics.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20601 - Medical engineering

Project

<a href="/en/project/VJ02010031" target="_blank" >VJ02010031: Modular multisensory professional clothing for risk management, health protection and safety of IRS members using artificial intelligence methods</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

AI

ISSN

2673-2688

e-ISSN

2673-2688

Volume of the periodical

6

Issue of the periodical within the volume

10

Country of publishing house

CH - SWITZERLAND

Number of pages

15

Pages from-to

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UT code for WoS article

001603180900001

EID of the result in the Scopus database

2-s2.0-105019980614