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DTSTART:20191103T020000
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UID:calendar.378691.field_event_date.0@www.wright.edu
DTSTAMP:20260220T000958Z
CREATED:20191127T182420Z
DESCRIPTION:Ph.D. Committee:  Drs. Tanvi Banerjee (advisor)\, Mateen Rizki\
 , Krishnaprasad Thirunarayan\, William Romine (Biological Sciences)\, and 
 Ali Azarbarzin (Brigham and Women’s Hospital/Harvard University) ABSTRACT:
 Humans spend almost a third of their lives asleep. Sleep has a pivotal eff
 ect on job performance\, memory\, fatigue recovery\, and both mental and p
 hysical health. Sleep quality (SQ) is a subjective experience and reported
  via patients’ self-reports. Predicting subjective SQ based on objective m
 easurements can enhance diagnosis and treatment of SQ defects\, especially
  in older adults who are subject to poor SQ. In this study\, we assessed e
 nhancement of subjective SQ prediction using an easy-to-use E4 wearable de
 vice in addition to discovering significant sleep-related risk factors fro
 m PSG data in elder people.First\, we designed a clinical decision support
  system to estimate SQ and feeling refreshed after sleep using data extrac
 ted from an E4 wearable device. Specifically\, we processed four raw physi
 ological signals of heart rate variability (HRV)\, electrodermal activity\
 , body movement\, and skin temperature using ensemble machine learning met
 hods. Overall\, the achievement of our system in predicting SQ demonstrate
 d the capability of using wearable sensors in monitoring sleep.Second\, we
  investigated discovering more effective features in SQ prediction using H
 RV features which are not only effortlessly measurable but also can reflec
 t sleep stage transitions and some sleep disorders. Evaluation of three ma
 chine learning methodologies demonstrated the outperformance of a convolut
 ional neural network (CNN) methodology in predicting light\, medium\, and 
 deep SQ. This outcome verified the capability of using HRV features\, whic
 h are effortlessly measurable by easy-to-use wearable devices\, in predict
 ing SQ.Third\, we scrutinized daytime sleepiness risk factors as a sign of
  poor SQ from physiological signals. The analysis demonstrates distinguish
 ability of the main risk factors of excessive daytime sleepiness (EDS) in 
 patients suffering from fragmented sleep (e.g. apnea) vs sleep propensity 
 (e.g. dementia). We also predicted EDS using new\, sleep-related biomarker
 s.Finally\, we designed a framework to further categorize the risk factors
  of poor SQ. We will use this framework to assess the role of dementia sev
 erity (cognitive decline) in SQ of older community-dwelling men\, who are 
 susceptible to poor sleep and dementia. We will specifically assess EDS in
  different levels of cognitive decline severity using objective sleep-rela
 ted biomarkers.
DTSTART;TZID=America/New_York:20191204T110000
DTEND;TZID=America/New_York:20191204T130000
LAST-MODIFIED:20191202T131939Z
LOCATION:311 Russ Engineering - Conference Room
SUMMARY:Ph.D. Dissertation Proposal Defense Predicting Subjective Sleep Qua
 lity Using Objective Measurements in Older Adults By Reza Sadeghi
URL;TYPE=URI:/events/phd-dissertation-proposal-defens
 e-predicting-subjective-sleep-quality-using-objective
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