TY - JOUR
T1 - Multi-Scale Evaluation of Sleep Quality Based on Motion Signal from Unobtrusive Device
AU - Coluzzi, Davide
AU - Baselli, Giuseppe
AU - Bianchi, Anna Maria
AU - Guerrero-Mora, Guillermina
AU - Kortelainen, Juha M.
AU - Tenhunen, Mirja L.
AU - Mendez, Martin O.
N1 - Funding Information:
The research was partly funded by the Lombardia project (Announcement POR-FESR 2014-2020) SIDERA⌃B–Sistema Integrato DomiciliarE e Riabilitazione Assistita al Benessere (https://www.liuc.it/ricerca/ricerca-accademica/progetti/siderab-sistema-integrato-domiciliare-e-riabilitazioneassistita-al-benessere/), a project that aims at implementing a new platform for monitoring and coaching of patients with chronic diseases during home rehabilitation.
Publisher Copyright:
© 2022 by the authors.
PY - 2022/7/15
Y1 - 2022/7/15
N2 - Sleep disorders are a growing threat nowadays as they are linked to neurological, cardiovascular and metabolic diseases. The gold standard methodology for sleep study is polysomnography (PSG), an intrusive and onerous technique that can disrupt normal routines. In this perspective, m-Health technologies offer an unobtrusive and rapid solution for home monitoring. We developed a multi-scale method based on motion signal extracted from an unobtrusive device to evaluate sleep behavior. Data used in this study were collected during two different acquisition campaigns by using a Pressure Bed Sensor (PBS). The first one was carried out with 22 subjects for sleep problems, and the second one comprises 11 healthy shift workers. All underwent full PSG and PBS recordings. The algorithm consists of extracting sleep quality and fragmentation indexes correlating to clinical metrics. In particular, the method classifies sleep windows of 1-s of the motion signal into: displacement (DI), quiet sleep (QS), disrupted sleep (DS) and absence from the bed (ABS). QS proved to be positively correlated (0.72±0.014) to Sleep Efficiency (SE) and DS/DI positively correlated (0.85±0.007) to the Apnea-Hypopnea Index (AHI). The work proved to be potentially helpful in the early investigation of sleep in the home environment. The minimized intrusiveness of the device together with a low complexity and good performance might provide valuable indications for the home monitoring of sleep disorders and for subjects' awareness.
AB - Sleep disorders are a growing threat nowadays as they are linked to neurological, cardiovascular and metabolic diseases. The gold standard methodology for sleep study is polysomnography (PSG), an intrusive and onerous technique that can disrupt normal routines. In this perspective, m-Health technologies offer an unobtrusive and rapid solution for home monitoring. We developed a multi-scale method based on motion signal extracted from an unobtrusive device to evaluate sleep behavior. Data used in this study were collected during two different acquisition campaigns by using a Pressure Bed Sensor (PBS). The first one was carried out with 22 subjects for sleep problems, and the second one comprises 11 healthy shift workers. All underwent full PSG and PBS recordings. The algorithm consists of extracting sleep quality and fragmentation indexes correlating to clinical metrics. In particular, the method classifies sleep windows of 1-s of the motion signal into: displacement (DI), quiet sleep (QS), disrupted sleep (DS) and absence from the bed (ABS). QS proved to be positively correlated (0.72±0.014) to Sleep Efficiency (SE) and DS/DI positively correlated (0.85±0.007) to the Apnea-Hypopnea Index (AHI). The work proved to be potentially helpful in the early investigation of sleep in the home environment. The minimized intrusiveness of the device together with a low complexity and good performance might provide valuable indications for the home monitoring of sleep disorders and for subjects' awareness.
KW - multi-scale analysis
KW - pressure bed sensor (PBS)
KW - shift-working
KW - sleep apnea–hypopnea syndrome (SAHS)
KW - sleep monitoring
KW - unobtrusive measure
UR - http://www.scopus.com/inward/record.url?scp=85135116682&partnerID=8YFLogxK
U2 - 10.3390/s22145295
DO - 10.3390/s22145295
M3 - Article
C2 - 35890975
AN - SCOPUS:85135116682
SN - 1424-8220
VL - 22
JO - Sensors
JF - Sensors
IS - 14
M1 - 5295
ER -