Bibliographic Details
| Title: |
Continuous Monitoring of Sleep-Related Biomarkers via a Nearable Solution Based on Fiber Bragg Grating Technology. |
| Authors: |
De Tommasi F, D'Antoni F, Presti DL, Silvestri S, Fortino G, Schena E, Merone M, Massaroni C |
| Source: |
IEEE journal of biomedical and health informatics [IEEE J Biomed Health Inform] 2026 Aug; Vol. 30 (8), pp. 6971-6984. |
| Publication Type: |
Journal Article |
| Language: |
English |
| Journal Info: |
Publisher: Institute of Electrical and Electronics Engineers Country of Publication: United States NLM ID: 101604520 Publication Model: Print Cited Medium: Internet ISSN: 2168-2208 (Electronic) Linking ISSN: 21682194 NLM ISO Abbreviation: IEEE J Biomed Health Inform Subsets: MEDLINE |
| Imprint Name(s): |
Original Publication: New York, NY : Institute of Electrical and Electronics Engineers, 2013- |
| MeSH Terms: |
Sleep*/physiology , Signal Processing, Computer-Assisted*/instrumentation , Fiber Optic Technology*/instrumentation , Fiber Optic Technology*/methods , Polysomnography*/methods , Polysomnography*/instrumentation, Respiratory Rate/physiology ; Heart Rate/physiology ; Biomarkers/analysis ; Monitoring, Physiologic/methods ; Monitoring, Physiologic/instrumentation ; Posture/physiology ; Humans ; Beds ; Support Vector Machine ; Male ; Adult ; Female ; Algorithms |
| Abstract: |
This study explores the innovative application of a nearable solution (i.e., mattress) based on fiber Bragg grating (FBG) technology for continuously monitoring of critical sleep-related biomarkers. Based on biocompatible silicone compounds, the mattress embeds thirteen strategically positioned FBG sensors to detect bed occupancy, sleeping posture, respiratory rate (RR), and heart rate (HR). Our experimental protocol involves ten participants who underwent simulated sleeping conditions to evaluate the mattress's performance across different postures and respiratory patterns. Employing traditional machine learning algorithms, including decision tree, support vector machine (SVM), and Naïve-Bayes classifiers, the mattress achieves 100% accuracy in bed occupancy detection. It also effectively distinguishes between axial and lateral sleeping positions, with SVM achieving the highest accuracy of 78.4% for axial versus lateral differentiation and convolutional neural networks achieving 75.9% in distinguishing left from right positions. Additionally, for most participants, the system successfully estimates RR and HR with mean absolute errors of less than 0.7 breaths per minute and 4 bpm, respectively, across various breathing patterns in terms of frequencies and amplitudes employing different algorithms (frequency and time-domain approaches). The promising findings highlight the potential of the proposed system for a comprehensive evaluation of sleep-related breathing disorders in clinical and home settings. |
| Substance Nomenclature: |
0 (Biomarkers) |
| Entry Date(s): |
Date Created: 20250410 Date Completed: 20260805 Latest Revision: 20260806 |
| Update Code: |
20260806 |
| DOI: |
10.1109/JBHI.2025.3559724 |
| PMID: |
40208764 |
| Database: |
MEDLINE |