IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, VOL. 51, NO. 1,
JANUARY/FEBRUARY 2015
Esteban J. Pino, Senior Member, IEEE, Astrid Dörner De la Paz, Student Member, IEEE, and
Pablo Aqueveque, Member, IEEE
Presenter: Kai-Min Liang
Advisor: Dr. Chun-Ju Hou
Date:2015/12/16
Outline
Introduction
Research motivation
Sleep Disturbances In Miners
Measurement System
Signal Processing And Analysis
Results
Conclusion
2
Introduction
Mining has rapidly grown because of
globalization
 Having extended hard working shifts
 Without specialized medical supervision
 These conditions prevent having a restful sleep,
affect a worker’s job performance.
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Introduction
During sleep
 The body restores main nervous system functions to
successfully perform daily activities.
Memory
 Learning
 Reaction time among others

4
Introduction
 Twenty-eight percent of the world population suffer
from nocturnal diseases
 Sleep apnea hypopnea syndrome (SAHS)
9% woman
 24% man

 Brain damage
 Cardiovascular problems
 Hypertension and arrhythmia
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Introduction
Polysomnography (PSG)
 Different signals are recorded simultaneously
 Airflow, SpO2
 Respiratory effect
 EEG, ECG, EMG
 Expensive and time-consuming
 Unsuitable for continuous monitoring
6
Introduction
Actigraphy
 Measure sleep parameters and estimate sleep
problems
 Unobtrusive method
 The hardware is too simple and only considers sleep
efficiency to estimate sleep quality.
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Introduction
Pressure sensor arrays
 Record the time in bed (TB) and the BMs and
respiratory signal
 Noninvasive monitoring
 Installed in standard beds
 Continuous monitoring of sleep quality
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Research motivation
Portable and noninvasive device based on
pressure sensors to monitor sleep parameters
 Time in bed(TB)
 Body movements(BMs)
 Respiratory rate (RR)
 Apneas
9
Research motivation
From these parameters, possible to report the
sleep interval duration
 Sleep intervals longer than 20 min(SI>20)
 Sleep intervals short than 20 min(SI <20 )
From six volunteers and one SAHS-diagnosed
patient show the potential
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Sleep Disturbances In Miners
Miners are long hour shifts and high-altitude
efforts
 The shift work at Chilean mines
 Four days of work at a mining facility and three days to rest
 Seven days of work at a mining facility and seven days to rest
 Some accident caused by fatigue is higher in shift
workers than nonshift workers
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Sleep Disturbances In Miners
The most innovative system considers EEG to
estimate the alertness status of an operator.
 Scan the workers and try to predict an alertness
status when people are working
 Do not collect any information of fatigue or
somnolence
Need an objective system to improvements on a
worker’s sleep quality
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Measurement System
The system is divided into five stages
13
Measurement System
A. Sensor Array
 24 force sensor resistors (FSRs)
 Placed into three rows and eight columns
 Active area of each sensor is 38.1 mm × 38.1 mm
 Total sensing area of the array is 300 mm × 900 mm
 Must cover the width of the thorax
 Comfortable to the patient
 Safe to use
14
Measurement System
B. Analog Signal Multiplexing
 Three eight-channel multiplexers
 Reduce the size of the hardware
 Multiple ADC ports available in the microcontroller
C. Resistance –Voltage Transducer
 An inverting amplifier produces the resistance–
voltage transduction.
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Measurement System
D. ADC
 Three ADC channels of the microcontroller are
configured with 8 bits of conversion.
Sampling frequency of each channel is 250 Hz
 Sampling rate per sensor is 31.25 Hz

E. Serial Communication
 Serial communication at 38.4 K baud to a 2-GB
microSD card
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Signal Processing And Analysis
A. Sleep Parameters
Fig. 5. Twenty-four signals obtained from the pressure sensor array.
The BMs and the apnea episodes appear in most of the signals.
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Signal Processing And Analysis
A. Sleep Parameters
 TB calculation: indicates the time spent or devoted
to sleep
 BM detection: during sleep change in position or the
movement of an extremity
Remove those sections for the respiratory algorithm
 Appear as high frequency and high amplitude signals
 Detected by observing amplitude changes in a 2-s window
 At least three signals have to large amplitude change over a
threshold
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
Signal Processing And Analysis
A. Sleep Parameters
a. Full-night-sleep record
b. Vertical red lines represent the BM
detections along the record.
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Signal Processing And Analysis
A. Sleep Parameters
 Automatic signal selection: changes position,
channel that detects the best respiratory signal
Rejecting saturated signals, discarding the channels with a
mean above or below a previously determined threshold,
and leaving only valid signals.
 Selection of the signal with the smallest kurtosis along the
segment.

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Signal Processing And Analysis
A. Sleep Parameters
 RR calculation: corresponds to the number of
respiratory cycles per minute
Change with sex, age, or the wake/sleep stage
 Calculated by inverting the time intervals between two
consecutive peaks in the selected respiratory signal.

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Signal Processing And Analysis
A. Sleep Parameters
 Apnea detection: defined as the absence of airflow
for at least 10s
Between two BMs, all the valid signals are added together,
and the standard deviation is calculated.
 Compared with its moving average
 Current value is less than 40%, it is marked as a possible
apnea event.
 After that, a 10-s window adds the events.
 Best in terms of the sensitivity to detect the events (Se) and
the positive predictivity (+P) to avoid false alarms.
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
Signal Processing And Analysis
B. Sleep Quality Indexes
 During sleep, the subject goes through different stages.
 Rapid eye movement (REM)
 Deepest stage of sleep
 Muscle atony
 Lasts between 20 and 30 min
 Non-rapid eye movement (NREM)
 Characterized absence of REM
 Shallow stages of sleep
 Muscle contraction
 A typical sleep cycle lasts about 90 min.
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Signal Processing And Analysis
B. Sleep Quality Indexes
 Sleep interval duration: sleep interval duration is
calculated between two consecutive BMs
 SI > 20: deep-sleep stage (REM)
 SI < 20: shallow-sleep quality
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Signal Processing And Analysis
B. Sleep Quality Indexes
 Sleep depth from BMs: BMs may be used as an
estimate of the sleep depth
A 20-min sliding window adds the number of BMs along
the measurement.
 Low number of added BMs indicates a deeper sleep stage.

 A moving average helps visualize the sleep cycles.
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Signal Processing And Analysis
B. Sleep Quality Indexes
 Sleep depth from the RR variability: respiratory
variability is also associated with the sleep stage
Low RR variability is associated with a deeper sleep stage
 Sleep depth is predicted with the standard deviation of the
RR series (RRsd) in 20-min windows.

Fig. 7. (a) Instantaneous RR. (b) RRsd computed in a 20-min sliding window.
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Results
A. Respiratory Signal Validation
 Fifteen volunteers between 50 and 90 kg are
measured for 6 min
a. Signals obtained from the pressure sensor array
b. Signal obtained with the airflow sensor
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Results
A. Respiratory Signal Validation
 Pearson’s correlation coefficient, obtaining ρ = 0.904
 T-test for pressure sensor array and airflow sensor
signals are different is less than α = 0.001.
Fig. 10. Airflow sensor signal and the pressure sensor signals have a positive
linear correlation of ρ = 0.904
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Results
B. Evaluation of BM Detection Algorithm
 Fifteen volunteers evaluate the BM algorithm
 Three position changes : supine, lateral, and prone
 Compared with the known body position changes
 Se = 100%
 +P = 100%
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Results
C. Evaluation of Apnea Detection Algorithm
 Apnea detection algorithm, using a PSG and the
implemented device simultaneously.

Se = 71% and +P = 76%
Fig. 11. Segment of a PSG record. The
red marks represent the neurologist’s
classification of an obstructive sleep
apnea.
Fig. 12. Segment of Fig. 11 obtained
with the pressure sensors.
The blue rectangular
signal represents the apnea detection
algorithm.
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Results
D. Evaluation of Automatic Signal Selection
Algorithm
 Analyzed 485 segments processed by the algorithm
 In 86.6% of the cases, the algorithm agreed with at
least one expert.
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Results
E. Analysis of Full-Night-Sleep Records
Rec
TB[min]
BM
SI>20
[min]
SI<20
[min]
Apenalike
events
V1n1
389.47
42
5
37
15
V1n2
369.35
28
7
21
16
V1n3
438.09
32
7
25
13
V2n1
300.77
28
5
23
56
V2n2
420.03
70
3
67
13
V3n1
415.02
97
3
94
35
V4n1
434.95
67
3
64
8
V5n1
553.95
63
9
54
34
V6n1
435.61
66
3
63
31
P1n1
537.42
151
4
147
288
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Results
E. Analysis of Full-Night-Sleep Records
 The worst case (p1n1) spent 87% of the time in short
sleep intervals.
 The best case (v1n2) spent 37% of the time in short
sleep intervals.
Fig. 13 shows the cumulative frequency of the sleep interval duration.
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Results
E. Analysis of Full-Night-Sleep Records
 Number of sleep apneas provides information about
SAHS
 Can be used as a parameter to estimate sleep quality
 Table I shows healthy volunteers have much lower
apnea like events than the p1n1, who has 288 apnea
like events
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Conclusion
This paper has presented a noninvasive
monitoring device for sleep studies.
 Using simple sensor sheet
 Contain relevant information such as BMs and the
respiratory signal
 Sleep parameters can be then obtained by processing
those signals.
 This can prove to be a highly valuable tool to
evaluate corrective actions to ensure the restful sleep
of mine workers.
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Thanks for your attention.
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