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

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/68407700:21730/20:00347458 RIV/00179906:_____/20:10418399 RIV/60461373:22340/20:43920987

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30502 - Other medical science

Projekt

<a href="/cs/project/EF17_048%2F0007441" target="_blank" >EF17_048/0007441: PERSONMED - Centrum rozvoje personalizované medicíny u věkem podmíněných onemocnění</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název periodika

Sensors

ISSN

1424-8220

e-ISSN

—

Svazek periodika

20

Číslo periodika v rámci svazku

9

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

14

Strana od-do

2594

Kód UT WoS článku

000537106200158

EID výsledku v databázi Scopus

2-s2.0-85084328394