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智慧電子整合性人才培育先導型計畫 教材成果研習會. 計畫主題:智慧電子應用技術平台 醫療電子應用設計 專題. 2012 智慧電子應用設計聯盟成果展 醫療 電子應用設計 專題. Advisior : Evans Lee. Lesson 1. To sleep, or not to sleep, that is the question!. Outline. 1-1. Introduction 1-2. Related Work 1-3. REM vs. NREM 1-4. Comparison of REM & NREM

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slide1

智慧電子整合性人才培育先導型計畫

教材成果研習會

計畫主題:智慧電子應用技術平台醫療電子應用設計專題

advisior evans lee

2012 智慧電子應用設計聯盟成果展

醫療電子應用設計專題

Advisior : Evans Lee

slide4

Outline

  • 1-1. Introduction
  • 1-2. Related Work
  • 1-3. REM vs. NREM
  • 1-4. Comparison of REM & NREM
  • 1-5. staging of sleep
  • 1-6. parameters for staging
  • 1-7. parameters for sleep study
slide9

1-1. Introduction (Sleep study)(5/7)

  • During a sleep study the sleep cycles and stages of sleep are monitored.
    • Electrodes are placed to monitor continuousrecordings of brain waves, electrical activity of muscles, eye movement, respiratory rate, blood pressure, blood oxygen saturation, and heart rhythm.
    • The test (PSG) is performed for people who suffer from insomnia, excessive daytime sleepiness, obstructive sleep apnea, breathing difficulties during sleep, or behavior disturbances during sleep.
slide10

Percentage of Adult

25 %

Sleep Disorder

1-1. Introduction (Sleep Disorder)(6/7)

  • Sleep Disorder
    • Insomnia, Narcolepsy, Obstructive Sleep Apnea…
    • A lack of restorative rest can cause accidents on the job or on the road, affect your relationships, health, and mental prowess.
slide12

1-1. Introduction (Sleep Disorder)(7/7)

  • Sleep Disorder Index
    • Empiricism
    • Sleep Scale
      • Epworth Sleepiness Scale, ESS
      • Adaptation of Functional Outcomes of Sleep Questionnaire, FOSQ
      • Pittsburgh Sleep Quality Index, PSQI
    • Measurement Instruments
      • Polysomnogram, PSG
1 3 rem vs nrem
1-3. REM vs. NREM
  • There are two states of sleep:
    • NREM (non-rapid eye movement)

REM sleep is associated with dreaming

andparalysis of body muscles (except for

the eye and diaphragm muscles).

    • REM (rapid eye movement )

NREM sleep has four stages distinguishable by EEG waves. A person with normal sleep usually has four to five cycles of REM and NREM sleep during a night.

1 5 staging of sleep
1-5. staging of sleep
  • Sleepstaging was scored according to the criteria of Rechtschaffen and Kales.
    • Arousals were scored as defined in the American Sleep Disorders Association Atlas Task Force report on EEG arousals.
    • The arousal index is defined as the number of cortical arousals per hour of sleep (each ≧3 s).
    • The awakening index is defined as the number of cortical awakenings per hour of sleep (each ≧ 15 s).
    • The sleep disturbance index is defined as the sum of the arousal index plus the awakening index.
1 6 parameters for staging
1-6. parameters for staging
  • Sleep staging depends on :

1. EEG(Electroencephalogram): brain activity

2. EOG(Electroocculogram): eye movement

3. EMG(Electromyogram): muscle tone

1 7 1 electroencephalogram
1-7-1. Electroencephalogram
  • Six electrodes (labeled C3, C4, A1, A2 O1, and O2) and one ground electrode are placed around the cranium to record electrical activity across the brain.
  • These leads are used to determine the

stage of sleep the patient is in during

any given period of the night.

1 7 2 electroocculogram
1-7-2. Electroocculogram
  • One electrode is placed above and to the outside of the right eye, and another electrode is placed below and to the outside of the left eye.
  • These leads record the movements of the eyes during sleep and serve to help determine sleep stages.
1 7 3 electromyogram
1-7-3. Electromyogram
  • Three leads are placed on the chin (one in the front and center and the other two underneath and on the jawbone) and two are placed on the inside of each calf muscle 2-4cm apart.
  • These leads serve to demonstrate muscle movement during sleep. This is helpful in documenting a wake period, an arousal, or just a spastic movement.
1 7 4 ecg and flow detector
1-7-4. ECG and flow detector
  • Two electrodes are placed on the upper chest near the right and left arms. These record the heart rate and rhythm and serve to alert the technician to a possible emergency situation. They also demonstrate whether apneic desaturation leads to arrhythmias or not.
  • It senses the amount of air moving into and out of the airways and sends a signal to a physiological recorder. This tracing is used to determine the presence and extent of apneic episodes.
slide22

1-7-5. Pulse oximetry

  • The O2 saturation is measured by a pulse oximeter probe placed on the patient i.e. finger, earlobe, etc.
slide23

1-7-6. Respiratory Effort

  • Two Velcro bands, one placed around the chest under the breasts and one around the abdomen, serve to determine chest wall and abdominal movements during breathing. Each band is joined together by a piezo crystal transducer.
  • The force of chest/abdominal expansion on the bands stretches the transducer and alters the signal to a physiological recorder. These leads, combined with the airflow sensor, are how apnea is demonstrated and categorized during the test.
slide24

1-7-7. Video recording

  • If the sleep disorders center is equipped with video cameras in the patient rooms, the patient can be taped while sleeping.
  • This allows the technician to review the tape at any time during the test and verify whether strange looking waveforms were caused by an actual arousal, a period of wake, or normal patient movement in bed.
slide26

1-8-1. Polysomnography(1/3)

  • PSG is a comprehensive recording of the biophysiological changes during sleep.
  • Used to diagnosesleeping disorders including narcolepsy,  REM behavior disorder, parasomnias, and sleep apnea.
slide27

1-8-1. Polysomnography(2/3)

  • Sleeping stage estimation
    • EX:ECG,EOG
  • Physical activity monitoring
    • EX:EMG,body position
  • Breathing monitoring
    • EX:Snore,nose stream,SpO2
slide28

1-8-1. Polysomnography(3/3)

  • After the test is completed a "scorer" analyzes the data by reviewing the study in 30 second "epochs“
    • Cardiac rhythm abnormalities.
    • Leg movements.
    • Body position during sleep.
    • Oxygen saturation during sleep.
outline
Outline
  • 4-1. Motivation
  • 4-2. System Architecture
    • 1.Signal Processing
    • 2.Classification
  • 4-3. Experiments
  • 4-4. Implementation Results
  • 4-5. Conclusions
4 1 motivation

Expensive

Gold

Standard

Operation

Complex

PSG

Attach

Electrodes

One Night

Monitor

4-1. Motivation
4 1 motivation1

Expensive

Gold

Standard

Operation

Complex

PSG

Attach

Electrodes

One Night

Monitor

4-1. Motivation

Low Cost

Easy to Use

Pre-Screening

Home Device

Unnecessary

Attach

Long-Term

Monitor

4 1 system architecture 1 4
4-1. System Architecture(1/4)
  • System Overview
  • Testee
  • Sensors

Personal Computer

  • I/O devices

Sleep or Wake

Algorithms

  • Computer

Deep Sleep or

Light Sleep

Sleep Efficiency

Sleep latency

4 1 system architecture 3 4
4-1. System Architecture(3/4)
  • Hardware system architecture
4 1 system architecture 4 4
4-1. System Architecture(4/4)
  • Processing
    • Signals Processing
    • Classification
slide37

4-1. System

  • MSP430F1611 SOC System
  • PPG Device
  • Tri-axis Accelerometers
msp430f1611 soc system
MSP430F1611 SOC System

Photo Source:www.Ti.com

slide40

PPG Device(Pulse Oximetry )

This Sleep Monitor Device used a PPG

Device which produced by Nonin

Company to measure Pulse Oximeter

signal

slide42

PPG Output Status

  • Packet Description
  • A frame consists of 5 bytes; a packet consists of 25 frames.
  • Three packets (75 frames) are transmitted each second
slide43

PPG Output Status

Byte 1 : Start Byte(Always set to a 01 value)

Byte2 : Status Byte

(This byte provides status information at a rate of 1/75 of a second)

Byte3 : PLETH Byte This byte consists of an 8 bit plethysmographic waveform (pulse waveform)

Byte4 : FLAT Byte This byte is used for SpO2, Pulse Rate, and information that can be processed at a rate of 1/3 of a second

Byte5 : Check Byte

(This byte is used for the checksum of bytes 1 through 4)

CHK = (Byte 1 + Byte 2 +Byte 3 +Byte 4) mod 256

Xpod Packet output format

Source:www.nonin.com

slide44

PPG Output Status

Source:www.nonin.com

slide45

Tri-axis Accelerometers(1/8)(LIS344ALH)

Key Features

(1.)2.4 V to 3.6 V single supply operation

(2.)±2 g / ±6 g user selectable full-scale

(3.)Low power consumption

(4.)Output voltage, offset and sensitivity are ratiometric to the supply voltage

(5.)Factory trimmed device sensitivity and offset

(6.)Embedded self test

(7.)RoHS/ECOPACK® compliant

(8.)High shock survivability ( 10000 g )

slide47

Tri-axis Accelerometers(3/8)

The relationship between Cload and cut-off frequency

slide50

Tri-axis Accelerometers (6/8)

Position of LIS344ALH and corresponding output voltage

slide52

Tri-axis Accelerometers (8/8)

Relationship curve of tilt angle and output voltage

slide53

Power Supply

(1) Battery power supplies 5 V for BEEPER use(2) Battery power via step-up IC MC34063 step to the 9 V supply to the PPG device(3) Via LM317 drop down to 3.3 V for single-chip MSP430F1611 system and three-axis accelerometer

9v and 3.3v Power Supply System

slide54

User Control Interface

  • Start_mode
  • Stop_mode
slide56

4-2-1. Signal Processing(1/6)

  • Asleep/Awake identification(1/3)

Reference:[11]Non-constraining sleep/wake monitoringsystem using bed actigraphy

slide57

4-2-1. Signal Processing(2/6)

  • Asleep/Awake identification(2/3)

One Epoch:30s

slide58

4-2-1. Signal Processing(3/6)

  • Asleep/Awake identification(3/3)

TR

Awake

Sleep

slide59

4-2-1. Signal Processing(4/6)

  • So and Chan method(1/2)

slope

thres

slide60

4-2-1. Signal Processing(5/6)

  • So and Chan method(2/2)
slide61

4-2-1. Signal Processing(6/6)

  • Short-Term Fourier Transform, STFT
slide62

4-2-2.Classification(1/3)

  • GreyART Network
    • Adaptive Resonant Theory Network
      • ART-1, ART-2
    • Grey Relation Analysis

ART-2 Network

Grey Relation Analysis

Reference:[12] ECG Beat Classification Using the GreyART Network

slide63

4-2-2.Classification(2/3)

  • Traning

Reference:[13]居家睡眠品監測系統之研製

slide64

4-2-2.Classification(3/3)

  • Estimating Sleep Stage

64

Reference:[14] Pressure-diameter relationships of segments of human finger arteries

slide65

4-3.Experiments(1/8)

  • System Experiment (Database)
    • Training
slide66

4-3.Experiments(2/8)

  • System Experiment (Database)
    • Testing
slide67

4-3.Experiments(3/8)

  • Experiment MIT-BIH (Database) Results
    • ECG and BP data analysis – slp48

Original

Sleep Stage

ρ=0.95

Estimate

Sleep Stage

ECG HR

Accuracy=70.1%

BP HR

Accuracy=68.8%

slide68

4-3.Experiments(4/8)

  • Experiment MIT-BIH (Database) Results
    • ECG and BP data analysis – slp59

Original

Sleep Stage

ρ=0.95

Estimate

Sleep Stage

ECG HR

Accuracy=72.9%

BP HR

Accuracy=69.2%

slide69

4-3.Experiments(5/8)

  • Experiment MIT-BIH (Database) Results
    • ECG and BP data analysis – slp60

Original

Sleep Stage

ρ=0.95

Estimate

Sleep Stage

ECG HR

Accuracy=74.3%

BP HR

Accuracy=72.4%

slide70

4-3.Experiments(6/8)

  • Experiment MIT-BIH (Database) Result
    • ECG and BP data analysis – slp61

Original

Sleep Stage

ρ=0.95

Estimate

Sleep Stage

ECG HR

Accuracy=83.7%

BP HR

Accuracy=74.4%

slide71

4-3.Experiments(7/8)

  • Experiment MIT-BIH (Database) Results
    • ECG and BP data analysis – slp61

Original

Sleep Stage

ρ=0.95

Estimate

Sleep Stage

ECG HR

Accuracy=83.7%

BP HR

Accuracy=74.4%

slide72

4-3.Experiments(8/8)

  • Experiment (Database) Results
  • Vigilance and Number of Templates
  • Vigilance and Accuracy
slide73

4-4. Implementation Results(1/6)

3D

accelerometer

Battery

PPG

Device

MMC

Card

slide74

4-4. Implementation Results(2/6)

  • Real testing procedure:
    • The sleeping monitor device is carried by the testee all night to record physical changes.
    • Next morning, the testee fills out PSQI for a comparison.

Home

Device

Pulse

Oximeter

slide75

4-4. Implementation Results(3/6)

  • Real case result:Testee 001(Day 01)
slide76

4-4. Implementation Results(4/6)

  • Real case result:Testee 002(Day 01)
slide77

4-4. Implementation Results(5/6)

  • Real case result:Testee 003(Day 01)
slide78

4-4. Implementation Results(6/6)

Q:Evaluation value

S:Total sleep duration

SD: Deep sleepduration

LightSleep:Evaluation value

S:Total sleep duration

SL: Light sleepduration

slide79

4-5.Conclusions

  • The portable monitoring device
    • Non-aware monitoring
      • Non-invasive
      • Unnecessary attach electrodes
      • Long term monitoring
    • Physiological signals
      • Two signals
    • Add on
      • Convenient
      • Easy to use
    • Auto classification sleep stage
      • Low Degree of Complexity
      • Evaluating sleep quality
slide80

Appendix

  • Grey Relation Analysis
  • All Real Case Results
slide81

Grey Relation Analysis(1/4)

Example

Input Data:

Input Data

Normalization

Computing

Grey Relation Coefficient

Computing

Grey Relation Grade

Normalization:

x1= {8222, 9077, 9818, 10912}

Average(x1)= 9507.25

x1’= {8222/9507.25, 9077/9507.25, 9818/9507.25,10912/9507.25}

End

slide82

Grey Relation Analysis(2/4)

Example

Input Data

Input Data:

Normalization

Computing

Grey Relation Coefficient

Computing

Grey Relation Grade

Computing Grey Relation Coefficient :

∆xi, yj= |xi- yj|

End

slide83

Grey Relation Analysis(3/4)

Computing Grey Relation Coefficient:

Input Data

Normalization

Computing

Grey Relation Coefficient

∆i, max= 0.1082, ∆i, min = 0, ξ=0.5

Computing

Grey Relation Grade

End

slide84

Grey Relation Analysis(4/4)

Computing Grey Relation Grade

Input Data

Normalization

Computing

Grey Relation Coefficient

Computing

Grey Relation Grade

End

slide85

All Real Case Results (1/3)

  • Testee 001(Day 02)
slide86

All Real Case Results (2/3)

  • Testee 001(Day 03)
slide87

All Real Case Results (3/3)

  • Testee 002(Day 02)
commercial design and verification of sleep quality analysis system for home care service
居家睡眠品質分析系統之商業化設計與驗證

Commercial Design and Verification of Sleep Quality Analysis System for Home Care Service

Speaker : Prof. Ren-Guey Lee

Date : 09.23.2011

88

slide89
起心動念

學術研究

基於小波轉換之腦電訊號分析與長期多項生理訊號自動分類系統—2006 (與台北醫學大學合作)

居家型睡眠品質監控系統之研製—2008 (用MIT/BIH發展兩種生理信號的睡眠等級判斷演算法--MATLAB)

可攜式睡眠品質評估裝置之研製—2009 (硬體實做並將演算法用單晶片實現)

商品設計與臨床實驗 (經濟部醫材試作計畫)

居家睡眠品質分析系統之商業化設計與驗證--2011

slide90
第一代成果基於小波轉換之腦電訊號分析與長期多項生理訊號自動分類系統—2006 (與台北醫學大學合作)
  • Joe-Air Jiang, Chih-Feng Chao, Ming-Jang Chiu, Ren-Guey Lee*, Chwan-Lu Tseng, and Robert Lin, “An Automatic Analysis Method
  • for Detecting and Eliminating ECG Artifacts in EEG,”Computers in Biology and Medicine, Vol. 37, No. 11, pp. 1660–1671, Nov. 2007.
  • (SCI, EI)
  • Chih-Feng Chao, Joe-Air Jiang, Ming-Jang Chiu, and Ren-Guey Lee*, “Automated Long-Term Polysomnography Analysis with
  • Wavelet Processing and Adaptive Fuzzy Clustering,”Biomedical Engineering: Applications, Basis and Communications, Vol. 18,
  • No. 3, pp. 119–123, Jun. 2006. (SCI-E)
2008 matlab
第二代成果居家型睡眠品質監控系統之研製—2008 (發展兩種生理信號的睡眠等級判斷演算法--MATLAB)
experiment

Record slp59 - Sleep Stage

Record slp59 - Modify Estimate Sleep Stage

6

6

Awake

Awake

Awake

Awake

REM

REM

Stage1

Stage1

REM

5

REM

5

Stage2

Stage2

Stage3

Stage3

Stage4

Stage4

4

4

Stage1

Stage1

Stage2

Stage2

3

3

Stage3

2

Stage3

2

Stage4

1

Stage4

1

0

0

0

1

2

3

4

0

1

2

3

4

Hours

Hours

Experiment

MIT/BIH

Record Sleep Stage

Estimate Sleep Stage

  • System Experiment (Database) Result

ρ=0.94, Accuracy=76.0

experiment1

Record slp60 - Sleep Stage

Record slp60 - Modify Estimate Sleep Stage

6

6

Awake

Awake

Awake

REM

REM

Stage1

Stage1

REM

5

5

Stage2

Stage2

Stage3

Stage3

Stage4

Stage4

4

4

Stage1

Stage2

3

3

Stage3

2

2

Stage4

1

1

0

0

0

1

2

3

4

5

6

0

1

2

3

4

5

6

Hours

Hours

Record slp61 - Sleep Stage

Record slp61 - Modify Estimate Sleep Stage

6

6

Awake

Awake

Awake

Awake

REM

REM

Stage1

Stage1

REM

REM

5

5

Stage2

Stage2

Stage3

Stage3

Stage4

Stage4

4

4

Stage1

Stage1

Stage2

Stage2

3

3

Stage3

Stage3

2

2

Stage4

Stage4

1

1

0

0

0

1

2

3

4

5

6

0

1

2

3

4

5

6

Hours

Hours

ρ=0.94, Accuracy=75.5

Experiment

Awake

REM

Stage1

Stage2

Stage3

Stage4

ρ=0.94, Accuracy=66.1

experiment2

Record slp67x - Sleep Stage

Record slp67x - Modify Estimate Sleep Stage

6

6

Awake

Awake

Awake

Awake

REM

REM

Stage1

Stage1

REM

REM

5

5

Stage2

Stage2

Stage3

Stage3

Stage4

Stage4

4

4

Stage1

Stage1

Stage2

Stage2

3

3

Stage3

Stage3

2

2

Stage4

Stage4

1

1

0

0

0

0.2

0.4

0.6

0.8

1

1.2

1.4

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Hours

Hours

Record Patient A - Sleep Stage

Record Patient A - Modify Estimate Sleep Stage

6

6

Awake

Awake

Awake

Awake

REM

REM

Stage1

Stage1

REM

REM

5

5

Stage2

Stage2

Stage3

Stage3

Stage4

Stage4

4

4

Stage1

Stage1

Stage2

Stage2

3

3

Stage3

Stage3

2

2

Stage4

Stage4

1

1

0

0

0

2

4

6

8

0

2

4

6

8

Hours

Hours

ρ=0.94, Accuracy=72.7

Experiment

TMUH

ρ=0.94, Accuracy=54.7

experiment3

Record Patient B - Sleep Stage

Record Patient B - Modify Estimate Sleep Stage

6

6

Awake

Awake

Awake

Awake

REM

REM

Stage1

Stage1

REM

REM

5

5

Stage2

Stage2

Stage3

Stage3

Stage4

Stage4

4

4

Stage1

Stage1

Stage2

Stage2

3

3

Stage3

Stage3

2

2

Stage4

Stage4

1

1

0

0

0

1

2

3

4

5

6

7

0

1

2

3

4

5

6

7

Hours

Hours

Record Patient C - Sleep Stage

Record Patient C - Modify Estimate Sleep Stage

6

6

Awake

Awake

Awake

Awake

REM

REM

Stage1

Stage1

REM

REM

5

5

Stage2

Stage2

Stage3

Stage3

Stage4

Stage4

4

4

Stage1

Stage1

Stage2

Stage2

3

3

Stage3

Stage3

2

2

Stage4

Stage4

1

1

0

0

0

1

2

3

4

5

6

7

0

1

2

3

4

5

6

7

Hours

Hours

ρ=0.94, Accuracy=48.0

Experiment

ρ=0.94, Accuracy=56.4

slide98
第三代成果可攜式睡眠品質評估裝置之研製—2009(硬體實做並將演算法用單晶片實現)第三代成果可攜式睡眠品質評估裝置之研製—2009(硬體實做並將演算法用單晶片實現)
implementation results 1 6
Implementation Results(1/6)

3D

accelerometer

Battery

PPG

Device

MMC

Card

implementation results 2 6
Implementation Results(2/6)
  • Real testing procedure:
    • The sleeping monitor device is carried by the testee all night to record physical changes.
    • Next morning, the testee fills out PSQI for a comparison.

Home

Device

Pulse

Oximeter

implementation results 3 6
Implementation Results(3/6)
  • Real case result:Testee 001(Day 01)
implementation results 4 6
Implementation Results(4/6)
  • Real case result:Testee 002(Day 01)
implementation results 5 6
Implementation Results(5/6)
  • Real case result:Testee 003(Day 01)
implementation results 6 6
Implementation Results(6/6)

Q:Evaluation value

S:Total sleep duration

SD: Deep sleepduration

LightSleep:Evaluation value

S:Total sleep duration

SL: Light sleepduration

slide105
第四代成果商品設計與臨床實驗 (經濟部醫材試作計畫)居家睡眠品質分析系統之商業化設計與驗證
introduction 3 4
Introduction(3/4)
  • 失眠的人每年都在增加
    • 相較於四年前平均增長了5%左右

[1] 94年社會發展趨勢調查報告-健康安全

introduction 4 4
Introduction(4/4)

Light sleep

Slightly deeper

sleep

Sleep Cycle

Stage 1

REM

Stage 2

Very Deeper

sleep

Deeper sleep

Stage 4

Stage 3

NREM(Non-rapid eye movement)

REM(Rapid eye movement)

http://www.merckmanuals.com/media/home/figures/MMHE_06_081_01_eps.gif

experiments
Experiments

PCB Layout(Front)

實際配帶圖

PCB Layout(Back)

experiments1
Experiments
  • Experiments procedures
    • Subjects: 22 people
      • Sex: 12 males and 10 female
      • Age: 27.2years old
    • Different date
    • Same time
        • PM22:00 ~ PM6:00
discussions2
Discussions
  • The data fail:
    • 1.RTCtime Incorrect.
    • 2.NO data.
    • 3. 3 Axis output “FF”.
  • Measurement timeonly five hours:
    • 1. Access the memory card is overflow.
conclusions
Conclusions
  • The Features:
    • The portable monitoring device.
    • Two signals.
    • Easy to use.
    • Auto classification sleep stage.
    • Clinical trials.
    • Low Accuracy Rate
  • Future Work
    • Increasemore pattern.
    • Make the system more robust.
    • Improve the volume size
references
References

[1]S. Akselrod, D. Gordon, F. A. Ubel, D. C. Shannon, A. C. Barger and R. J. Cohen, "Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control," Science, vol. 213, no. 4504, pp. 220-222, 1981.

[2]N. de Vicq, F. Robert, T. Torfs, J. Penders, and B. Gyselinckx,“Wireless Body Area Network for Sleep Staging,” IEEE BioCas, 2008.

[3]J. Penders, B. Gyselinckx, R. Vullers, M. D. Nil, J. van de Molengraft, F. Yazicioglu, T. Torfs, V. Leonov, P. Mercken, and C. Van Hoof,“Human++: from technology to emerging health monitoring concepts,” In Proceedings of the 5th International Workshop on ‘Wearable and Implantable Body Sensor Networks (BSNS), Hong Kong, China, 2008, pp. 94-98.

[4]R. Jane, J. Sola-Soler, J. A. Fiz, and J. Morera, “Automatic Detection of Snoring Signals: Validation with Simple Snorers and OSAS Patients ,” Annual International Conference of the IEEE Engineering in Medicine and Biology – Proceedings, vol.4, 2000, pp. 3129-3131.

[5]J. Liljencrants, “Experiments with analysis by synthesis of glottal airflow,” in Proceedings ICSLP Conference, vol. 2, 1996, pp. 1289–1292.

[6]P. Jean, K. Anna, and C. Julie, “Wake Detection Capacity of Actigraphy During Sleep,” Sleep. vol. 30, no. 10, 2007, pp. 1362–1369.