Sleep apnea is a common sleep disorder affecting approximately 6.6% of the population around the world and is associated with severe health consequences. Existing sleep apnea diagnosis and screening methods require the use of expensive and invasive medical devices, which greatly limits the accessibility and affordability of diagnosis. This project is aimed at developing a novel method for accurate sleep apnea screening with only a few physiological signals that can be readily measured with off-the-shelf wearable devices. An understanding of the types of signals and the specific statistical features that may help differentiate apnea and non-apnea episodes were developed. To achieve the goal, contrast set mining was applied to an open sleep apnea dataset. The generated contrast rule suggests that a combination of higher heart rate, lower blood oxygen saturation, and a certain body movement pattern together was associated with the occurrence of sleep apnea events.

• This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.

Creator

• Lukovnikova, Alena
• Jayne, Hannah

Publisher

• Worcester Polytechnic Institute

Identifier

• E-project-012323-143952
• 88156

Keyword

• Sleep Apnea
• Wearable Devices
• Data Mining

Advisor

• Powell, Adam Clayton
• Lammert, Adam

Year

• 2023

Center

• Kyoto, KUAS Japan MQP

Date created

• 2023-01-23

Resource type

• Major Qualifying Project

Major

• Biomedical Engineering
• Mechanical Engineering

Source

• E-project-012323-143952

Rights statement

• In Copyright

Last modified

• 2023-02-06