Going Beyond Atrial Fibrillation in Arrhythmia Classification from Photoplethysmography Signals

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0191259" target="_blank" >RIV/00216305:26220/26:0191259 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.22489/CinC.2024.110" target="_blank" >http://dx.doi.org/10.22489/CinC.2024.110</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.22489/CinC.2024.110" target="_blank" >10.22489/CinC.2024.110</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Going Beyond Atrial Fibrillation in Arrhythmia Classification from Photoplethysmography Signals

Popis výsledku v původním jazyce

Photoplethysmography (PPG) offers a simple, affordable, and non-invasive method for continuous vascular system monitoring, seamlessly integrated into user-friendly smart devices. Considering the rapid expansion of smart devices, there exists a significant opportunity to extend health monitoring to a broader population. This paper focuses on cardiac arrhythmia (CA) detection from PPG signals. CAs pose significant health risks, often leading to complications such as stroke and heart failure. While most studies focus solely on detecting atrial fibrillation (AF), our research aims to classify six different rhythm types into three classes: Sinus rhythm, AF, and Other. To achieve this goal, we trained a machine learning model – Random Forest. The model was trained on 12 features, which include not only features derived from pulse intervals but also statistical and morphological features. Our results demonstrate an overall accuracy of 0.88 on the test set, indicating the efficacy of our method. Additionally, when tested on a completely independent dataset, the model achieved an accuracy of 0.87. This study highlights the potential of PPG technology to detect various types of CAs beyond AF, offering valuable insights for improved cardiovascular health monitoring.

Název v anglickém jazyce

Going Beyond Atrial Fibrillation in Arrhythmia Classification from Photoplethysmography Signals

Popis výsledku anglicky

Photoplethysmography (PPG) offers a simple, affordable, and non-invasive method for continuous vascular system monitoring, seamlessly integrated into user-friendly smart devices. Considering the rapid expansion of smart devices, there exists a significant opportunity to extend health monitoring to a broader population. This paper focuses on cardiac arrhythmia (CA) detection from PPG signals. CAs pose significant health risks, often leading to complications such as stroke and heart failure. While most studies focus solely on detecting atrial fibrillation (AF), our research aims to classify six different rhythm types into three classes: Sinus rhythm, AF, and Other. To achieve this goal, we trained a machine learning model – Random Forest. The model was trained on 12 features, which include not only features derived from pulse intervals but also statistical and morphological features. Our results demonstrate an overall accuracy of 0.88 on the test set, indicating the efficacy of our method. Additionally, when tested on a completely independent dataset, the model achieved an accuracy of 0.87. This study highlights the potential of PPG technology to detect various types of CAs beyond AF, offering valuable insights for improved cardiovascular health monitoring.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20601 - Medical engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název statě ve sborníku

Computing in Cardiology

ISBN

—

ISSN

—

e-ISSN

2325-887X

Počet stran výsledku

4

Strana od-do

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Název nakladatele

—

Místo vydání

—

Místo konání akce

Karlsruhe

Datum konání akce

8. 9. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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