Recognition of Patient Groups with Sleep Related Disorders Using Bio-signal Processing and Deep Learning

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11150%2F20%3A10418399" target="_blank" >RIV/00216208:11150/20:10418399 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21730/20:00347458 RIV/00179906:_____/20:10418399 RIV/60461373:22340/20:43920987
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=qQBpZV026F" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=qQBpZV026F</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/s20092594" target="_blank" >10.3390/s20092594</a>

Result language
angličtina
Original language name
Recognition of Patient Groups with Sleep Related Disorders Using Bio-signal Processing and Deep Learning
Original language description
Accurately diagnosing sleep disorders is essential for clinical assessments and treatments. Polysomnography (PSG) has long been used for detection of various sleep disorders. In this research, electrocardiography (ECG) and electromayography (EMG) have been used for recognition of breathing and movement-related sleep disorders. Bio-signal processing has been performed by extracting EMG features exploiting entropy and statistical moments, in addition to developing an iterative pulse peak detection algorithm using synchrosqueezed wavelet transform (SSWT) for reliable extraction of heart rate and breathing-related features from ECG. A deep learning framework has been designed to incorporate EMG and ECG features. The framework has been used to classify four groups: healthy subjects, patients with obstructive sleep apnea (OSA), patients with restless leg syndrome (RLS) and patients with both OSA and RLS. The proposed deep learning framework produced a mean accuracy of 72% and weighted F1 score of 0.57 across subjects for our formulated four-class problem.
Czech name
—
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
—
OECD FORD branch
30502 - Other medical science

Project
<a href="/en/project/EF17_048%2F0007441" target="_blank" >EF17_048/0007441: PERSONMED - Center for the Development of Personalized Medicine in Age-Related Diseases</a><br>
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
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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