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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20601 - Medical engineering

Projekt

<a href="/cs/project/VJ02010031" target="_blank" >VJ02010031: Modulární multisenzorický profesní oděv k řízení rizika, ochraně zdraví a bezpečnosti členů IZS pomocí metod umělé inteligence</a><br>

Návaznosti

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

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

AI

ISSN

2673-2688

e-ISSN

2673-2688

Svazek periodika

6

Číslo periodika v rámci svazku

10

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

15

Strana od-do

—

Kód UT WoS článku

001603180900001

EID výsledku v databázi Scopus

2-s2.0-105019980614