Sleep Apnea Detection from Single-Lead ECG Signal Using Hybrid Deep CNN

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F25%3A43975638" target="_blank" >RIV/49777513:23520/25:43975638 - isvavai.cz</a>

Výsledek na webu

<a href="https://rdcu.be/eo46z" target="_blank" >https://rdcu.be/eo46z</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-981-96-3294-7_9" target="_blank" >10.1007/978-981-96-3294-7_9</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Sleep Apnea Detection from Single-Lead ECG Signal Using Hybrid Deep CNN

Popis výsledku v původním jazyce

Sleep apnea (SA) is a prevalent disorder that disrupts breathing during sleep, posing risks to multiple organs and potentially causing sudden death. The electrocardiogram (ECG) is vital for diagnosing SA due to its ability to identify irregular heart activity. This study introduces hybrid CNN models designed to automatically detect SA using a single-lead ECG signal. We validated our method through experiments with the Physionet Apnea-ECG dataset, which contains 70 single-lead ECG recordings annotated by medical professionals. Our results surpass the current state-of-the-art methods in accurately detecting SA from single-lead ECG signals, achieving an accuracy of 91.4% for per-segment classification and 100% for per-recording classification.

Název v anglickém jazyce

Sleep Apnea Detection from Single-Lead ECG Signal Using Hybrid Deep CNN

Popis výsledku anglicky

Sleep apnea (SA) is a prevalent disorder that disrupts breathing during sleep, posing risks to multiple organs and potentially causing sudden death. The electrocardiogram (ECG) is vital for diagnosing SA due to its ability to identify irregular heart activity. This study introduces hybrid CNN models designed to automatically detect SA using a single-lead ECG signal. We validated our method through experiments with the Physionet Apnea-ECG dataset, which contains 70 single-lead ECG recordings annotated by medical professionals. Our results surpass the current state-of-the-art methods in accurately detecting SA from single-lead ECG signals, achieving an accuracy of 91.4% for per-segment classification and 100% for per-recording classification.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název statě ve sborníku

Brain Informatics. BI 2024. Lecture Notes in Computer Science

ISBN

978-981-9632-93-0

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

11

Strana od-do

110-120

Název nakladatele

Springer Nature Singapore

Místo vydání

Singapore

Místo konání akce

Bangkok

Datum konání akce

13. 12. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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