Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/77812
Title: Home monitoring and alert system for obstructive sleep apnea
Authors: Lim, Ken Feng Guan
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2019
Abstract: Sleep disorders are growing increasingly prevalent globally. In the US, roughly 50 to 70 million adults suffer from some form of sleep disorder. There are different types of sleep disorders and one of them is sleep apnea. Sleep apnea is classified into two subtypes: Central Sleep Apnea and Obstructive Sleep Apnea. In Central Sleep Apnea, irregular breathing patterns occur due to the brain failing to send signals to the muscles which are responsible for breathing to occur. In Obstructive Sleep Apnea, the airway is partially or fully blocked by a muscle located at the back of the throat known as the soft palate, resulting in nearly zero airflow to the lungs. An apneic event is known as a complete blockage in the airway accompanied by a drop of at least 3% in the oxygen level. To date, approximately 25 million adults in the US suffer from Obstructive Sleep Apnea. If untreated, Obstructive Sleep Apnea has been associated with various negative health effects, such as excessive daytime sleepiness and an increased risk in contracting cardiovascular-related diseases. The current gold standard for the diagnosis of Obstructive Sleep Apnea is by doing an overnight Polysomnography in a sleep clinic or laboratory. However, due to the huge disparity between the number of sleep clinics and patients, waiting for an overnight Polysomnography might take a few days to weeks, resulting in long waiting times. Thus, many studies have been done to explore the feasibility of home devices in diagnosis and detecting Obstructive Sleep Apnea. This project explores the feasibility of using two sensors, namely the SPO2 and microphone, to detect apneic events with the Raspberry Pi as the platform. The system will be able to monitor and alert the individual if an apneic event is detected, as well as having data logging capabilities which will be useful in determining the effectiveness of treatments.
URI: http://hdl.handle.net/10356/77812
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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