View source for Projects:2015s1-58 Design And Development Of A New Respiratory Monitor For Detection Of Sleep Apnoea

== Supervisor ==
Dr. Said Al-Sarawi

== Co-supervisors ==
Prof. Jagan Mazumdar

Dr. Cleto Mernone

== Students ==
Jian Shi

Sandeep Singh

== Acknowledgements ==
First and foremost, we would like to express our sincere gratitude to my supervisor Dr. Said Al-Sarawi for his continuous assistance in the project, also for his patience, inspiration, enthusiasm and immense knowledge.

Secondly, we would like to thank our co-supervisors, Prof. Jagan Mazumdar and Dr. Cleto Mernone. The project could not be a success without their encouragement, insightful comments and helpful suggestions.

Our sincere thanks also goes to A/Prof. Baumert for providing the test data and valuable feedbacks.

== Introduction ==

=== Background Information===
Sleep apnea is a kind of sleep disorder that is characterized with suspension in breathing during sleep. Each interruption, called as an apnea interval, can last from ten seconds to several minutes.

Apneic events can be classified according to the nature of the underlying physiological cause. Central Sleep Apnea (CSA) is usually caused by the absence of correct signals from brain areas in charge of breathing to the relevant muscles and a normal breathing action cannot be implemented. Obstructive Sleep Apnea (OSA) is much likely to happen when the patient’s pharyngeal narrows or the upper airway collapses during sleep. The complex or mixed type of sleep apnea is a mix of CSA and OSA.

The latest research result illustrates that obstructive sleep apnea is the most common type with a percentage.

The below figure is an illustration of the cause mechanism of OSA.

[[File:apnea.jpg]]

=== Motivation ===
Regardless of type, an individual with sleep apnea suffers from difficulty of breathing during sleep, which leads to detrimental effects on sleep-quality and health. 

Some typical consequences of sleep apnea to the patient are as follows.

•	High blood pressure

•	Stroke

•	Heart failure, heart attack or irregular heart beats

•	Depression and lack of concentration in daytime

It is quite difficult to exactly specify how many Australians suffer from sleep apnea. It is generally thought that approximately 9% of women and 25% of men in Australia have clinically significant sleep apnea and 4% of men and women have symptomatic sleep apnea [5]. 

Due to the serious effects of sleep apnea and the enormous quantity of patients, the medical and health professions have been seeking for reliable sleep apnea detection and therapy approach in recent decades.

Fundamentally, the development of a sleep apnea monitor is extremely helpful to fill some gaps in the research of sleep apnea and make a contribution to the medical applications.

=== Project Context ===

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 relevant 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.

=== Project Objectives ===
Actually, this project is an ongoing project and the group received the outcome from the previous Bachelor group at the beginning of this project. 

Specifically, the previous group finished the detection and analysis work on one test case. However, with more cases tested by using their algorithm, we found that the feasibility and performance of their detection algorithm is not satisfying. 

Therefore, more work on the improvement of the detection algorithm is required first and designing a monitor prototype is the second step.

Following is the three objectives of this project established.

•	Fundamental – develop a reliable and accurate algorithm to detect the occurrence of sleep apnea

•	Improvement – investigate the sleep apnea events and find out more information regarding sleep apnea

•	Supplementary – put forward a design plan of a physical monitor prototype for implementation in the future

== Data Acquisition ==

The data for use in detection tests are provided by A/Prof Baumert under the assistance of the project supervisor, Dr. Said Al-Sarawi. 

Those data files are stored in EDF and XML formats.

•	EDF file – contains an multi-channel overnight recording of sleep signals and labels of each acquisition channel with some available information , such as sampling frequency.

•	XML file – contains the corresponding annotations of all the sleep events happening in the recording period and those annotations are used as reference while verifying if the detection statement is correct or not.
The initial step to do analysis work is to observe the features of those provided signals.

A free EDF viewer (see the figure below) from National Sleep Research Source is employed in this stage to combine the EDF files and XML files. The viewer enables the user to combine an EDF file with a corresponding sleep annotation file. The user has a myriad of ways to view the signals including scrolling, clicking on a specific annotation and determining the display channels of signal.

[[File:EDF viewer.jpg|600px|center]]


In this project, the channel of airflow signal is selected to implement the signal processing work.

== Detection based on Wavelet ==

=== CWT Analysis===

The figure below shows the frequency distribution variation from normal breathing session to apnea session by implementing a CWT operation.

Obviously, there is a significant power variation where the bright color denotes a higher power level and the darker color denotes a lower power level. 
A tendency of signal power from high frequency range to low frequency range has been discovered.

[[File:CWT result.png|600px]]

=== Power Density ===

Based on the CWT analysis, the phenomenon of power variation in higher frequency band is considered to be utilized as the indicator to detect the occurrence of sleep apnea. 

The next step is to extract the desired frequency components and calculate their power level so as to find out the power variation ratio.

The procedure of obtaining the power density of the airflow signal can be illustrated by the following chart.

[[File:power density.png|400px]]

The below figure demonstrates the power reduction in apnea intervals.

[[File:power.png|600px]]

Based on over 200 tests on the power variation ratio of apnea events, 70% is selected as the threshold to determine an apnea event.

[[File:ratio.png|500px]]


=== Duration Measurement ===

The duration of apnea interval is the other parameter to assess the sleep apnea events.

With the assistance of duration measurement algorithm, some short-time sleep apnea events can be discarded and the detection accuracy can increase correspondingly.

The step-by-step illustration of measuring the sleep apnea interval is shown below.

[[File:Duration Measurement of SA.png|400px]]

== Detection based on HHT ==
Decompose low pass filtered signal using HHT into Intrinsic mode functions (IMFs).First 3-level IMF has chosen to identify Sleep Apnea, because they eliminate background variations in Signal, moreover contains higher modulations while detecting sleep apnea.

Analysis on first three IMFs used for processing show a distinctive feature to identify sleep apnea. There is Power increase up to 0.4 Hz in case of sleep apnea .


[[File:10sec.png|600px]][[File:Power increase.png|600px]]


A sleep apnea event lasts for more than 10 seconds. The duration measurement of sleep apnea is useful to improve accuracy of detection algorithm. The combination of HHT and apnea duration measurement produce better improvement in results.

Parameter 1-Power increase at low frequencies in case of Sleep Apnea is utilized as an indicator 
     - Primary apnea detection parameter

Parameter 2-Duration measurement Algorithm detects time more  Than 10 seconds
     - Secondary parameter – improve accuracy

Combination of these two parameters give an indication of Apnea


[[File:3min.png|600px]]

== Result Analysis ==
After implementing massive tests on 100 different cases, the average detection accuracy of the method based on wavelet transform comes to around 85% and the sensitivity reaches up to 95%. 

[[File:Apnea detection result.png|600px]]

From the above results demonstration, the score of sensitivity is the highlight which has exceeded that of the approaches in literatures.

However, the accuracy is a drawback of the method, where there is still a potential to be improved in the future.

== Design Plan ==
The last objective of this project is to formulate a design plan of a monitor prototype to be used in the actual implementation by future groups.

The below diagram is an overview of the design plan.

[[File:Design Plan.png|600px]]

Functions of each component are listed below:

a. The combination of an airflow sensor and a mask is used as the signal acquisition device. The airflow from the patient will be accumulated by the medical mask mounted on the face of the patient. Subsequently, the airflow will pass through the tube and into the digital airflow sensor. The sensor is extremely sensitive to any variation of airflow and it could provide a digital output as well.

b. Considering that the interface of the airflow sensor is I^2 C, an adaptor is needed to achieve the interface transform from I^2 C to USB, which is compatible with the master PC.

c. Master PC is employed to control the operation of data acquisition device, process the input signals, visualize the collected signals, implement the apnea detection operation and store the measurement data into disk.

d. The software link is based on MATLAB and it could realize the data measurement connection between MATLAB and external hardware under the participation of the built-in Data Acquisition Toolbox.

== Conclusion ==
All things considered, the aims of the project have been accomplished successfully and a conclusion is required to be summarized at this stage.

The project focused on creating an effective algorithm for detection of sleep apnea and putting forward a draft design plan for use to construct a physical sleep monitor in the future.

The three objectives listed in sub-chapter 2.4 have been achieved and the present achievements are listed below.

•	Sleep apnea occurrence detection: the algorithm is able to utilize the power indication to capture the power variation in high frequency band from normal respiration to apnea interval so as to detect the occurrence of sleep apnea event.

•	Duration measurement for sleep apnea: a reduction function is employed to monitor the amplitude reduction of airflow signal through the comparison with a prior baseline established and the duration of apnea event can be measured by quantifying the positive interval length of the reduction function.

•	Confidence factor evaluation: a statistical confidence level is established to evaluate each detection statement via calculating the derivation of the power ratio from the mean value of the normal distribution which is used to represent the power ratio reference for apnea events.

•	Design plan of a monitor prototype: a preliminary design plan is put forward for the further integration of the algorithm with the data acquisition hardware.

== Future Work ==
The ultimate target of the project is to produce a physical monitor prototype with practical diagnosis value for implementation of sleep apnea detection in medical institutions. 

Due to the limitations of time and manpower, the group is not able to complete the work on both software programming and hardware construction. 

There is potential that could be implemented by future project groups. These include:

•	Algorithm improvement: 

As mentioned in the introduction, sleep apnea is classified into three types according to different cause mechanism, but at this stage, the classification work has not been considered. It might be advantageous to assess the features of some other signal channels (i.e. ECG, SaO2 etc.) in apnea period to extract the indication used to classify sleep apnea events.

•	Decline of the false positive rate: 

The drawback of the presented detection algorithm is the presence of false positive detections, which has a adverse effect on the accuracy. There is a need to consider an approach to lower the false positive rate. This issue could be achieved by employing a new indicator to have a more strict restriction in detection.

•	Software and hardware integration: 

The next stage in the process would be to turn this detection algorithm into actual hardware and a physical monitor is expected to be constructed by future groups. Some issues worth mentioning include the sensor selection (i.e. specifications of the sensor, resolution, I/O voltage, sampling frequency etc.), interface where the program and hardware interact and budget control.

== References ==
[1] W. Roger & F. Henry, Sudden Infant Death Syndrome, 2001. 

[2] Varaday, Peter, et al. “A Novel Method for Detection of Apnea and Hypopnea Events in Respiration Signals”, IEEE Transactions on Biomedical Engineering 49.9 (2002): 936-42. 

[3] Z. Shinar et al. 2000 “Obstructive Sleep Apnea Detection Based on Electrocardiogram Analysis”, Computers in Cardiology, vol.27, pp. 757-760.

[4] J. Soler et al. 2003 “Spectral Envelope Analysis in Snoring Signals from Simple Snores and Patients with Obstructive Sleep Apnea”, Proceedings of the 25th Annual International Conferences of the IEEE EMBS, pp.17-21.

[5] SIDS and Kids. Fast Facts 2013. [online]. Available: http://www.sidsandkids.org/wpcontent/uploads/SIDS-7032-Fast-FactsLR5 

[6] A. Ajmani et al. 1996, “Spectral Analysis of an Acoustic Respiratory Signal with a View to Developing an Apnoea Monitor”, Australian Physical & Engineering Sciences in Medicine, vol.19, no.2, pp. 1-7. 

[7] S. Hill et al. 2010, “Acoustic Sleep Apnea Detector”, Department of Anesthesiology, Vanderbilt University Medical Center 

[8] HANS PASTERKAMP, S. KRAMAN STEVE, and R. WODICKA GEORGE "Respiratory Sounds",American Journal of Respiratory and Critical Care Medicine, Vol.156, No. 3 (1997)

 [9] A.Ajmani , J Mazumdar ,D.Jarvis,”Spectral analysis of an acoustic respiratory system with a view to developing a sleep apnoea monitor”,Australian physical and engineering science in medicine(1996)vol.19 no.2 

[10] Varaday, Peter, et al. "A Novel Method for the Detection of Apnea and Hypopnea Events in Respiration Signals." IEEE Transactions on Biomedical Engineering 49.9 (2002): 936-42. 

[11] Cafer Avcı1, İbrahim Delibaşoğlu2, Ahmet Akbaş3 ,”Sleep Apnea Detection Using Wavelet Analysis of ECG Derived Respiratory Signal” 2012 International Conference on Biomedical Engineering (ICoBE),27-28 February 2012,Penang.

[12] Qiang Zhu, Yansong Wang and Gongqi Shen,”Research and Comparison of Time-frequency Techniques for Nonstationary Signals”, JOURNAL OF COMPUTERS, VOL. 7, NO. 4, APRIL 2012

 [13] T. Penzel1 J. McNames2 P. de Chazal3 B. Raymond4A. Murray, Moody6,”Systematic comparison of differentalgorithms for apnoea detection basedon electrocardiogram recordings”, Med. Biol. Eng. Comput., 2002, 40, 402–407 [14] S. Mallat, “A Theory for Multi-Resolution Signal Decomposition Wavelet Representation,” IEEE Trans. On Pattern Analysis and Machine Intell. 11(7), 674-693, 1989. 

[15] N.Padmaja1 Dr.S.Varadarajan2 R.Swathi,”SIGNAL PROCESSING OF RADARECHOESUSING WAVELETS ANDHILBERT HUANG TRANSFORM” Signal & Image Processing : An International Journal (SIPIJ) Vol.2, No.3, September 2011 

[16] Z.K. Peng, Peter W. Tseb, F.L. Chu. “An improved Hilbert–Huang transform and its application in Vibration signal analysis”. Journal of Sound and Vibration 286 (2005) 187–205 

[17] Yuan Li. ”Wavelet Analysis for Change Points and Nonlinear Wavelet Estimates in Time Series.” Beijing: China Statistics Press, 2001. [18] Narcolepsy and Overwhelming Daytime Sleep Society of Australia. Sleep Apnea. [online]. Available: http://www.nodss.org.au/sleep_apnoeas.html 

[19] S.L. Chuah, “SIDS Monitoring Device”, Rep, 2004. 

[20] Sleep Health Foundation, “Re-awakening Australia: the economic cost of sleep disorders in Australia, 2010”, Deloitte Access Economics Pty Ltd, UK, Rep, 2011. 

[21] Andrew K. Ng et al. “Bispectral Analysis of Snore Signals for Obstructive Sleep Apnea Detection”, Proceeding of the 29th Annual International Conference of the IEEE EMBS, August 23-26, 2007.

[22] Findley LJ, Wilhoit SC, Suratt PM.” Apnea duration and hypoxemia during REM sleep in patients with obstructive sleep apnea”, Chest. 1985 Apr;87(4):432-6.
  
[23] Peretz Lavie, professor,Paula Herer, statisticiaaVictor Hoffstein, professor of medicine. ”Obstructive sleep apnoea syndrome as a risk factor for hypertension: population study”,BMJ 2000;320:479  

[24] P. Caseiroa, R. Fonseca-Pintoa, A. Andradea,” Screening of obstructive sleep apnea using Hilbert–Huang decomposition of oronasal airway pressure recordings”, Medical Engineering & Physics 32561–568 

[25] John I. Salisbury a,, Ying Sun b,” Rapid screening test for sleep apnea using a nonlinear and nonstationary signal processing technique”, Medical Engineering & Physics 29 (2007) 336–343
  
[26] D. Liu, Z. Pang, and S. Lloyd, “A Neural Network Method for Detection of Obstructive Sleep Apnea EEG,” in IEEE Transactions on Neural Networks., vol. 19, no. 2,pp.308–318, 2008.  

[27] R. Lin, R. Lee, C. Tseng, H. Zhou, C. Chao, and J. Jiang, “A New Approach for Identifying Sleep Apnea Transform and Neural Networks,” Biomedical Engineering:Applications, Basis & Communications., vol.143,2006 
 
[28] Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng A, et al. “The empirical mode decomposition and and non-stationary time series analysis”. Proc Roy Soc London Ser A1998;454:903–95. 
 
[29] Huang NE, Shen Z, Long SR. A new view of nonlinear water waves:the Hilbert spectrum. Ann Rev 

[30] P. DUHAMEL,M. VETTERLI.” FAST FOURIER TRANSFORMS: A TUTORIAL REVIEW ANDA STATE OF THE ART”.
 
[31] Yufeng Zhang ab,∗, Zhenyu Guo a, Weilian Wang ab, Side He ab, Ting Lee a,Murray Loew a.” A comparison of the wavelet and short-time fourier transforms for Doppler spectral analysis”. Medical Engineering & Physics 25 (2003) 547–557  

[32] Semion Kizhner, Thomas P. Flatley, Dr. Norden E. Huangl Karin Blank, Evette Conwell.” On the Hilbert-Huang Transform Data Processing System Development”2. 2004 IEEE Aerospace Conference 

[33] P. Caseiroa,b,∗, R. Fonseca-Pintoa,c, A. Andradea.’”Screening of obstructive sleep apnea using Hilbert–Huang decomposition of oronasal airway pressure recordings”. Medical Engineering & Physics 

[34] B. Lind, J. Goodwin, J. Hill, T. Ali, S. Redline, and S. Quan, “Recruitment of healthy adults into a study in the home: experience of the sleep heart health study,” Sleep Breath, vol. 7, no. 1, pp. 13–24, 2003 

[35]EDF viewer [online].Available https://sleepdata.org/  

[36] EDF viewer [online].Available https://sleepdata.org/tools/edf-viewer 
 
[37] Huang N, Shen S. Hilbert–Huang transform and its applications. World Scientific Publishing Co; 2005.
  
[38] Nandakumar Selvaraj_ and Ravi Narasimhan. Detection of Sleep Apnea on a Per-Second Basis Using Respiratory Signals, 35th Annual International Conference of the IEEE EMBS 
Osaka, Japan, 3 - 7 July, 2013  

[39] Philip de Chazal*, Conor Heneghan, , Elaine Sheridan, Richard Reilly, Member, IEEE,Philip Nolan, and Mark O’Malley. Automated Processing of the Single-Lead  
“Electrocardiogram for the Detection of Obstructive Sleep Apnoea, IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING”, VOL. 50, NO. 6, JUNE 2003  
 
[40] HANS PASTERKAMP, S. KRAMAN STEVE, and R. WODICKA GEORGE "Respiratory Sounds, " American Journal of Respiratory and Critical Care Medicine, Vol. 156, No. 3 (1997)  

[41] A.Ajmani, J Mazumdar, D.Jarvis,”Spectral analysis of an acoustic respiratory system with a view to developing a sleep apnoea monitor”,Australian physical and engineering science in medicine,vol.19 no.2 , 1996  

[42] Thinklabs Stethoscope Software ,[Online] ,Available:http://www.thinklabs.com/