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dc.contributor.advisorThomas, Kevinen_US
dc.contributor.authorCarroll, James Peteren_US
dc.date.accessioned2021-02-23T18:53:04Z
dc.date.available2021-02-23T18:53:04Z
dc.date.issued2020
dc.identifier.urihttps://hdl.handle.net/2144/42087
dc.description.abstractThe conventional method for quantifying sleep is through the use of Polysomnography (PSG) and a trained human sleep scorer by observing and evaluating the output in 30-second epochs. A PSG device can be rather invasive to one’s regular sleep pattern and therefore can potentially result in irregular sleep patterns. Furthermore, human sleep scoring classification by a trained expert can be rather time consuming and subject to inter/intra rater variability. Nevertheless, human sleep scoring with PSG still remains the gold-standard for sleep measuring and classification for the diagnosis disorders related to sleep. The present pilot study explores the possibility of using a wearable device known as a ByteFlies Sensor Dot to measure signal activity from an individual during a night’s sleep. This validation study focuses on the signal capture of alpha frequency band through a phenomenon known as “the Berger effect.” Participants will be asked to open and close their eyes while being connected to the gold standard PSG device and exploratory ByteFlies Sensor Dot device. The resulting alpha signals will be identified with a machine learning algorithm for cross comparison and analysis. In conclusion, the validation study will discuss methods to improve on the measuring of EEG and sleep stage scoring with the ByteFlies Sensor Dot for sleep monitoring and sleep disorder diagnosis.en_US
dc.language.isoen_US
dc.subjectMedical imagingen_US
dc.titlePredicting sleep stages with machine learning and wearable byteflies sensor dots: a pilot studyen_US
dc.typeThesis/Dissertationen_US
dc.date.updated2021-02-20T02:04:07Z
etd.degree.nameMaster of Scienceen_US
etd.degree.levelmastersen_US
etd.degree.disciplineBioimagingen_US
etd.degree.grantorBoston Universityen_US


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