Apnea Detection in Polysomnographic Recordings Using Machine Learning Techniques

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00023752%3A_____%2F21%3A43920766" target="_blank" >RIV/00023752:_____/21:43920766 - isvavai.cz</a>
Alternative codes found
RIV/00216208:11120/21:43922696 RIV/68407700:21460/21:00354169
Result on the web
<a href="https://www.mdpi.com/2075-4418/11/12/2302" target="_blank" >https://www.mdpi.com/2075-4418/11/12/2302</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/diagnostics11122302" target="_blank" >10.3390/diagnostics11122302</a>

Result language
angličtina
Original language name
Apnea Detection in Polysomnographic Recordings Using Machine Learning Techniques
Original language description
Sleep disorders are diagnosed in sleep laboratories by polysomnography, a multi-parameter examination that monitors biological signals during sleep. The subsequent evaluation of the obtained records is very time-consuming. The goal of this study was to create an automatic system for evaluation of the airflow and SpO(2) channels of polysomnography records, through the use of machine learning techniques and a large database, for apnea and desaturation detection (which is unusual in other studies). To that end, a convolutional neural network (CNN) was designed using hyperparameter optimization. It was then trained and tested for apnea and desaturation. The proposed CNN was compared with the commonly used k-nearest neighbors (k-NN) method. The classifiers were designed based on nasal airflow and blood oxygen saturation signals. The final neural network accuracy for apnea detection reached 84%, and that for desaturation detection was 74%, while the k-NN classifier reached accuracies of 83% and 64% for apnea detection and desaturation detection, respectively.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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