Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Sara Mahvash-Mohammadi"'
Autor:
Ciro della Monica, Kiran K. G. Ravindran, Giuseppe Atzori, Damion J. Lambert, Thalia Rodriguez, Sara Mahvash-Mohammadi, Ullrich Bartsch, Anne C. Skeldon, Kevin Wells, Adam Hampshire, Ramin Nilforooshan, Hana Hassanin, The UK Dementia Research Institute Care Research & Technology Research Group, Victoria L. Revell, Derk-Jan Dijk
Publikováno v:
Clocks & Sleep, Vol 6, Iss 1, Pp 129-155 (2024)
Sleep and circadian rhythm disturbance are predictors of poor physical and mental health, including dementia. Long-term digital technology-enabled monitoring of sleep and circadian rhythms in the community has great potential for early diagnosis, mon
Externí odkaz:
https://doaj.org/article/f560e23cf07d4f548a65e6457d093280
Autor:
Kiran Kumar Guruswamy Ravindran, Ciro della Monica, Giuseppe Atzori, Shirin Enshaeifar, Sara Mahvash‐Mohammadi, Derk‐Jan Dijk, Victoria Revell
Publikováno v:
Alzheimer's & Dementia. 17
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9954d5e68be51f6b60aaa3e8f7f9f770
https://doi.org/10.1002/alz.056018
https://doi.org/10.1002/alz.056018
Publikováno v:
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society. 29
Sleep quality is an important determinant of human health and wellbeing. Novel technologies that can quantify sleep quality at scale are required to enable the diagnosis and epidemiology of poor sleep. One important indicator of sleep quality is body
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::192d0501f0449b4f6d0e900647423b98
https://pubmed.ncbi.nlm.nih.gov/33378261
https://pubmed.ncbi.nlm.nih.gov/33378261
Autor:
Sara Mahvash Mohammadi, Samaneh Kouchaki, Adrian Hilton, Derk-Jan Dijk, Sofia Khan, Kevin Wells
Publikováno v:
EMBC
In this study, a novel sleep pose identification method has been proposed for classifying 12 different sleep postures using a two-step deep learning process. For this purpose, transfer learning as an initial stage retrains a well-known CNN network (V
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e197043cfde450d8d5e5b3476c9573be
https://pubmed.ncbi.nlm.nih.gov/31946547
https://pubmed.ncbi.nlm.nih.gov/31946547
Autor:
Derk-Jan Dijk, Sara Mahvash Mohammadi, Adrian Hilton, Samaneh Kouchaki, Saeid Sanei, Kevin Wells
Publikováno v:
EUSIPCO
In this study, a novel hybrid tensor factorisation and deep learning approach has been proposed and implemented for sleep pose identification and classification of twelve different sleep postures. We have applied tensor factorisation to infrared (IR)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::60d06a0567ac316e84809226a557379c
https://doi.org/10.23919/eusipco.2019.8902979
https://doi.org/10.23919/eusipco.2019.8902979
Autor:
Sofia Khan, Derk-Jan Dijk, Kevin Wells, Sara Mahvash Mohammadi, Adrian Hilton, Majdi R. Alnowami
Publikováno v:
EMBC
Sleep is a process of rest and renewal that is vital for humans. However, there are several sleep disorders such as rapid eye movement (REM) sleep behaviour disorder (RBD), sleep apnea, and restless leg syndrome (RLS) that can have an impact on a sig
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0a8ca74cc87a595c704f695d172b9361
https://doi.org/10.1109/embc.2018.8513009
https://doi.org/10.1109/embc.2018.8513009
Publikováno v:
Journal of neuroscience methods. 273
© 2016 Elsevier B.V.Background Manual sleep scoring is deemed to be tedious and time consuming. Even among automatic methods such as time–frequency (T–F) representations, there is still room for more improvement. New method To optimise the effic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::05767739008c5da986fd9b11aff86a7c
https://pubmed.ncbi.nlm.nih.gov/27528379
https://pubmed.ncbi.nlm.nih.gov/27528379
Publikováno v:
EMBC
In this study, a single-channel electroencephalography (EEG) analysis method has been proposed for automated 3-state-sleep classification to discriminate Awake, NREM (non-rapid eye movement) and REM (rapid eye movement). For this purpose, singular sp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5c5263729af964310474861012296811
https://doi.org/10.1109/embc.2015.7319460
https://doi.org/10.1109/embc.2015.7319460