Identification of Human Vital Functions Directly Relevant to the Respiratory System Based on the Cardiac and Acoustic Parameters and Random Forest - 22/05/21
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Abstract |
Regarding sleep research, polysomnography (PSG) also called a sleep study, is a gold standard. It incorporates brain waves, the oxygen level in the blood, heart rate and breathing, and leg movement recordings. PSG is a complicated and expensive laboratory-based procedure, usually done in hospitals or special sleep center. In this study, an alternative technique for Sleep-Related Breathing Disorders (SRBD) based on selected cardiac and acoustic parameters and the Random Forest (RF) has been studied. A system dedicated to the detection of simultaneously acquired ECG and acoustic signals, which are collected during sleep at home environment is proposed. Results obtained indicate that classification and regression tree models such as RF are appropriate for the evaluation of sleep disorders like SRBD. The best identification of sleep irregularities at level 89.00 percent for the raw database was obtained. Thus, statistical predictive models allow identification of breathing events with high levels of sensitivity and specificity, providing an inexpensive and accurate diagnosis.
Le texte complet de cet article est disponible en PDF.Graphical abstract |
Highlights |
• | Sleep home-monitoring system based on ECGs and acoustic signals is developed. |
• | Random Forest classifier dedicated to patient monitoring during sleep is considered. |
• | Proposed algorithms enable to identify sleep-related disorders, even in its initial stages. |
Keywords : Patient monitoring, Random forest, Disorders, Biomarkers
Plan
Vol 42 - N° 3
P. 174-179 - juin 2021 Retour au numéroBienvenue sur EM-consulte, la référence des professionnels de santé.
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