Sleep Apnea Detection from Single-Lead ECG Signal Using Hybrid Deep CNN
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Sleep Apnea Detection from Single-Lead ECG Signal Using Hybrid Deep CNN
Original language description
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.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Brain Informatics. BI 2024. Lecture Notes in Computer Science
ISBN
978-981-9632-93-0
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
11
Pages from-to
110-120
Publisher name
Springer Nature Singapore
Place of publication
Singapore
Event location
Bangkok
Event date
Dec 13, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—