Going Beyond Atrial Fibrillation in Arrhythmia Classification from Photoplethysmography Signals

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

Going Beyond Atrial Fibrillation in Arrhythmia Classification from Photoplethysmography Signals

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

20601 - Medical engineering

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2024

Confidentiality

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

Article name in the collection

Computing in Cardiology

ISBN

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ISSN

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e-ISSN

2325-887X

Number of pages

4

Pages from-to

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Publisher name

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Place of publication

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Event location

Karlsruhe

Event date

Sep 8, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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