Difference between revisions of "Projects:2015s1-58 Design And Development Of A New Respiratory Monitor For Detection Of Sleep Apnoea"

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The holistic objective of this project is to design and develop a sleep apnea monitor capable of effectively detecting the apnea events during sleep.
 
  
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The apnea detection will be achieved through analyzing the respiratory signals from subjects with sleep disorder and the interaction of relevant hardware parts which are the sampling transducer and processing platform.
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== Introduction ==
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The ultimate aim of this project is to develop a respiratory monitor prototype for detection of sleep apnea. It is hoped that the prototype will eventually achieve the real-time monitoring of the patient’s sleep state and record the information of sleep apnea events. In this academic year, the work on detection algorithm has been accomplished and a design plan to construct a physical monitor has been established for the future groups.
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In this report, the author will deliver a novel intelligent method based on Wavelet Transform to detect the occurrence of sleep apnea, measure the duration of apnea interval and evaluate each detection statement by statistical analysis. In addition, a design plan for the expected monitor is also included. The performance of the detection algorithm has been assessed by testing 100 different cases provided by A/Prof. Baumert.
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In the exploring procedure, due to the complexity and variability of the respiratory signal during sleep, several feasible methods will be tested and compared with each other in terms of the accuracy in order to acquire a better performance in apnea detection.
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The accuracy of the presented detection method comes to 85% and the sensitivity is up to 95%, which is a significant improvement compared with some previous approaches in other literatures (In literatures, 90%-95% for accuracy, but only 85%-90% for sensitivity).
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Additionally, some other secondary objectives for each member involved in this project are as following.
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In conclusion, the performance of the detection algorithm has come to the expected level and the hardware construction work will be implemented by the future groups.
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• To inspire the public to gain a deeper understanding of sleep apnea and Sudden
 
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Infant Death Syndrome (SIDS).
 
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• Enhance the members’ communication skills, teamwork skills, project
 
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management skills etc.
 
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• Leave valuable resources and reliable reference for the possible further
 
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research in the future.
 
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This Research Project and Progress Report is an extended product within the context of Proposal Report, but also intended as a supplement to the previous Proposal Report. Some duplicate information previously addressed will not be reiterated. Specifically, the emphasis of this report is to demonstrate the individual contribution to the project and explain the outcomes with detailed justifications.
 
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Respiratory signal acquisition method has been proposed and three different methods in detection of apnea has also been applied over the sample signals. Meanwhile, what is planned to be done in the next semester is covered in this report as well.
 

Revision as of 21:02, 21 October 2015

Introduction

The ultimate aim of this project is to develop a respiratory monitor prototype for detection of sleep apnea. It is hoped that the prototype will eventually achieve the real-time monitoring of the patient’s sleep state and record the information of sleep apnea events. In this academic year, the work on detection algorithm has been accomplished and a design plan to construct a physical monitor has been established for the future groups. In this report, the author will deliver a novel intelligent method based on Wavelet Transform to detect the occurrence of sleep apnea, measure the duration of apnea interval and evaluate each detection statement by statistical analysis. In addition, a design plan for the expected monitor is also included. The performance of the detection algorithm has been assessed by testing 100 different cases provided by A/Prof. Baumert. The accuracy of the presented detection method comes to 85% and the sensitivity is up to 95%, which is a significant improvement compared with some previous approaches in other literatures (In literatures, 90%-95% for accuracy, but only 85%-90% for sensitivity). In conclusion, the performance of the detection algorithm has come to the expected level and the hardware construction work will be implemented by the future groups.

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