Projects:2014S1-21 Design And Development of a New Respiratory Monitor for Detection of Sleep Apnoea

Project

Background

Sleep Apnoea describes the situation that the breathing stops for a certain amount of time during sleep [1]. It often goes undiagnosed that usually cannot be detected by doctor, and also no blood test can help to diagnose the situation [2]. There are two types of Sleep Apnoea, Obstructive Sleep Apnoea (OSA) and Central Sleep Apnoea (CSA). Obstructive Sleep Apnoea (OSA) describes the most common condition that due to the patient’s pharyngeal narrowing, the airway collapses or becomes blocked during sleep [1]. Central Sleep Apnoea (CSA) is a less common type. It happened when the area of patient’s brain that control the breathing not sending the correct signals to the breathing muscles, the breath will stop for brief periods. In some case, Central Sleep Apnoea can occur with Obstructive Sleep Apnoea [2].

SIDS (Sudden Infant Death Syndrome) is a sudden and unexpected death of a healthy infant less than 1 year old, whose death remains unexplained after medical investigation. Most of SIDS cases occur at night when infant is asleep. According to the report from NSW Commission for Children and Young People, many cases happened when an infant sleeping face down, which may lead to periods of apnoea that my cause death of an infant [9].

Some research show there are some technique exist to detect the sleep apnoea. These methods included use impedance pneumography detect the changes in electrical impedance during the airflow through the lungs [4]; an automated classification algorithm that detects Sleep Apnoea based on the electrocardiogram (ECG) data features [5]; thermistors monitor to detect temperature of the air from the mouth or nostrils [6]; etc.

In this project, the acoustic acquisition technique will be used. Based on the research done by J. Mazumdar et al. on 1996, the acoustic signals have a large potential to identify the sleep apnoea. However, a critical issue in this technique is the signal that receives by the microphone also contained the surrounding sources of sound that interfering with the respiratory signal [6]. Therefore, the signal acquisition unit must be able filtered the noise signal as much as possible and result a clear and accurate respiratory signal. The signal processing unit must be able to identify the classes of the signal and recognize patient’s breathing status.

Significances

According to the Sleep Health Foundation, sleep disorders cost hospitals system $96.2millions in 2010, which 73.1 % of it are spend on the sleep apnoea [10]. Even though, there still have a large number of population had been diagnosed with sleep apnoea. The research show there were an approx. 1.5 million Australians (8.9 % of the population) with sleep disorders [10].

Furthermore, according to the research done by the SIDS and Kids organization, up to 2011, over last 10 years, an average of 0.3 per 1000 SIDS cases happened [3]. This number is shocking. Also, from the research done by NSW Commission for Children and Young People, sleep apnoea is one reasons of sudden infant death, especially face-down position has been prove to be strongly associated with SIDS [9]. Follow figure shows the relationship between infant face-down sleep position and the SIDS rate.

On the other side, the cause of the SIDS still is a mystery. The feature of the sleep apnoea monitor, which able to recognize the sleep apnoea type, can be provide some evidence in order to help with the investigation of the SIDS cause.

Therefore, this project is important because the new apnoea monitor may help to further decrease the number of SIDS. As the feature of this monitor, it will suitable for all classes of people

Aim

The aims of the project are:

• Ability to detect and recognize the sleep apnoea and send a warning to the caregivers
• The device have to be simple enough to be set-up (clear Instruction and easy to understand)
• Portable so the device has to be small enough to carry for users
• Non-invasive
• Power efficient
• Low-cost device

Team

Group Members

• Ran Chen
• Tuong Huy Dang

Supervisors

• Dr. Said Al-Sarawi
• Dr. Anacleto Mernone
• Prof. Jagan Mazumdar