Monitoring Lung Disease using Electronic Stethoscope ...€¦ · Electronic Stethoscope Arrays Kyle...
Transcript of Monitoring Lung Disease using Electronic Stethoscope ...€¦ · Electronic Stethoscope Arrays Kyle...
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Monitoring Lung Disease using Electronic Stethoscope Arrays
Kyle Mulligan, Andy Adler, Rafik Goubran
Department of Systems and Computer Engineering
Carleton University
CMBEC32
Calgary, AB
23 May 2009
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Agenda
� Background
� Motivation
� Solution
� Medical Instrument Development
� Data Processing Algorithm
� Phantom Models
� Experimental Results
� Conclusions
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Background
Adaptive Filtering
Stethoscope
Respiratory Disease
Auscultation
White Gaussian
Noise
Non-Linearities
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What is stethoscope auscultation?
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Respiratory Diseases
� This project focuses on airway obstructions caused by excess mucus in related diseases including:
• Pneumonia
• Bronchitis
• Emphysema
• Asthma
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White Gaussian Noise
� White Gaussian Noise is a randomly generated signal across a range of frequencies
� Useful for system identification due to wide frequency band and linear phase
Frequency (Hz)
Ma
gn
itu
de
(V
)
Transfer FunctionInput Signal Frequency
Output Signal Frequency
1 Value (500 Hz) 1 Value (500 Hz)
Range (0 – 4 kHz) Range (0 – 4kHz)
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Adaptive Filtering
Adaptive Filter
d[n]
y[n]
e[n]
Input Signal
Reference Signal
� Easily determine behaviour of unknown linear systems� Implemented using a Finite Impulse Response filter with a set
number of updatable coefficients� Coefficients are updated with each iteration of the algorithm by
the equation:
Mother + Fetal ECG
Mother ECG
Fetal ECG
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Impulse Response and Transfer Function
� The impulse response is derived from the adaptive filter coefficients upon convergence of the algorithm
� The coefficient that has the highest value designates the dominant transmission path of the input signal through the system to the measurement point
� The transfer function of a system dictates the system’s behavior to any input signal. Obtained using the equation for the impulse response
and sweeping over a range of frequencies (typically 0 – π rads/sample)
0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000-5
-4
-3
-2
-1
0
1
2
3
4
5x 10
-3 NLMS Filter Coefficients
Coefficient
|Coeff
icie
nt|
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-2
-1
0
1x 10
5
Normalized Frequency (×π rad/sample)
Phase (
degre
es)
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-100
-50
0
Normalized Frequency (×π rad/sample)M
agnitude (
dB
)
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Non-Linearities
� A non-linear system is that in which its output is not linearly proportional to the input into the system and thus cannot be described by a simple linear equation
� The performance of the algorithm depends on what excites the non-linear components of the system
� Caused by loudspeaker
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Motivation
� Improve patient health by reducing ventilator induced lung injury (VILI) and help in the selection of optimal ventilation parameters
� The instruments currently available either don't provide regional information (ie SpO2), or temporal information (ie X-ray CT)
� Breath Variability between auscultation points� Auditory training variability between physicians
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Solution
� Take advantage of signals generated from electronic
stethoscopes and adaptive filtering techniques to develop
an instrument capable of measuring changes in the
distribution of lung fluid and tissue densities within the
respiratory system
� Use an array of stethoscopes and a low frequency input
sound projected into the mouth
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Instrument Development
� Basic Structure
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Instrument Development cnt.
� Actual Components with Participant
Sound Generator Pre-Amplifier Computer
Stethoscope Array and Harness
Attached to a Participant
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Data Processing Algorithm
� Used adaptive filtering setup that employed the
Normalized Least Mean Squared (NLMS) algorithm
� Number of Coefficients = 1500
� Step Size µ = 0.296
� Input Signal = Reference Signal = WGN (0 – 4 KHz)
Unknown Linear System
Adaptive Filter
d[n]
y[n]
e[n]
Input Signal Chest
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Instrument Calibration
� Sound propagation delay must not take into account the delay of the instruments emitting and acquisition devices.
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Phantom Models
� Verify algorithm functionality using known predictable sound propagation models
� Add complexity in an effort to simulate actual human chests
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Open Air Column Model
� Hollow cylindrical tube with each stethoscope attached to the surface
� Use v = d/t, NLMS, Cross-Correlation to verify propagation delay of pulsed WGN input
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Plastic Bucket Model
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Chest Phantom Model
� Modified Plastic Bucket Model to provide a better phantom-stethoscope head interface.
Speaker Attachment / Y-Pipe
Foam Cylinder
Syringe and
Clear Tube
Stethoscope Array
and Harness
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Chest Phantom Experiment
� Inject sound into model
� Increase the volume of water inside the inner tube by 5cc until saturation
� Run NLMS Algorithm for 195 trials and plot average impulse response and retrieved delay
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Delay Estimation and Volume Location using the Impulse Response
0 5 10 15 20 25 30100
105
110
115
120
125
130
Volume (cc)
Dela
y (
Sam
ple
s)
Delay vs. Volume for Channel 3
0 5 10 15 20 25 3080
82
84
86
88
90
92
94
96
98
100
Volume (cc)
Dela
y (
Sam
ple
s)
Delay vs. Volume for Channel 2
No Water in FOV Water in FOV
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Conclusion and Future Work
� A novel instrument has been developed to measure changes in propagation delay as the density of water increases within a chest phantom model
� The instrument is capable of monitoring changes in the location of fluid within the chest phantom model
� Preliminary human trials correlate nicely with chest phantom model results