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Wearable Respiratory Monitoring: Algorithms and System Design
An IEEE Distinguished Lecture, 2011
A/Prof. Wee [email protected]
School of EEENanyang Technological University (NTU)
Singapore
Wearable Respiratory Monitoring: Algorithms and System Design
An IEEE Distinguished Lecture, 2011
A/Prof. Wee [email protected]
School of EEENanyang Technological University (NTU)
Singapore
Acknowledgement
IEEE Circuits and Systems Chapter, Santa Clara (Dr. Ping Chen)
for inviting me to give this IEEE DL Talk
The IEEE
1-68
10
9
IEEE-USA
IEEE Canada
7
324 sections worldwide
IEEE Circuits and Systems Societywww.ieee-cas.org
CASS is a technical society of IEEE
IEEE CASS
Membership (38 Societies in IEEE) > 400,000 8,513
Number of Chapters 1,784 152
CASS is the 9th largest IEEE Society (after CS, ComSoc, MTTS, PES, SPS, SSCS, EDS, and IA)
• 1955: Established as the 1st Chinese-medium university outside China
• 1981: Nanyang Technological Institute set up (English) with 3 Engineering Schools
• 1991: NTU inaugurated, incorporating National Institute of Education
• Now (2011): 3rd NTI/NTU President–College of Engineering–Nanyang Business School–College of Science–College of Humanities, Arts and Social Sciences–School of Medicine–Student population > 30,000
Nanyang Technological University (NTU)
Outline of Presentation
I. Introduction – A Clinical Perspective
II. Motivations and Problem Formulation
III. Wearable Wheeze Monitoring System @ NTUa) Overall System Designb) Wearable Sensors and Platform Suitec) Wearable Signal Processing Algorithms
IV. Lab Tests, Pre-Clinical Trials and Results
V. Conclusions and Future Work
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Research Team @ Nanyang Technological University
A/Prof. W Ser, Dr. J Zhang, J Yu, Dr. T Zhang
Research Team @ National University Hospital
A/Prof. Dr. Daniel YT Goh and Team
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Acknowledgement
2
Introduction-
A Clinical
Perspective
I
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Respiratory Disorders
Chronic respiratory diseases include disorders that affect
any part of the respiratory system, not only the lung but also
the upper airway (nose, mouth, pharynx, larynx, and
trachea), chest wall and diaphragm, as well as the
neuromuscular system that provides the power for breathing
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Major features of lungs: bronchi, bronchioles and alveoli
OSA disorder is characterized by repetitive collapse of the
upper airway
Our Lungs and Airways
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Examples of Respiratory Disorders
Chronic Obstructive Pulmonary Disease (COPD)• Irritation of the lungs, can lead to asthma, emphysema,
chronic bronchitis, or a mix of these
Asthma• Periodic constriction of bronchi & bronchioles more
difficult to breathe
Chronic Bronchitis• Irritant in bronchi and bronchioles - stimulates secretion of
mucus air passages clogged persistent cough
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Basic Types of Respiratory Sounds
Normal Breath SoundsNormal vesicular sounds (100 – 1000 Hz)Normal tracheal sounds (from <100 to >3000 Hz)
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Adventitious SoundsContinuous sounds (e.g. Wheezes)Discontinuous sounds (e.g. Crackles)
Upper Airway SoundsSnore
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Normal vs’ Adventitious Lung Sounds
Long durationShort duration
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Wheezes9
Wheezes
Continuous, “sinusoidal-like”, “musical” sound
Most obvious during exhaling (breathing out) but may occur during inhaling too
One of the most common noises in respiratory diseases (indicative of disorders)
Causes: Asthma, Chronic Bronchitis, COPD (Chronic Obstructive Pulmonary Disease), Allergy, foreign object, forced expiration, etc.
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Waveform and Spectrum of Wheezes
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Definition of Wheeze
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Wheeze
ATS Signal Duration > 250ms
Fundamental Frequency > 400Hz
CORSA Signal Duration >100ms
Fundamental Frequency > 100Hz
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Motivations
&
Problem
Formulation
II
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Why Monitor Respiratory Disorders ? Common and important cause of illness and death
Can affect people of all ages, both genders
300 million people suffer from Asthma and 210 million people with COPD (2007), world wide
1 in 7 in UK affected by chronic lung disease
Responsible for over 10% of hospitalizations & over 16% of deaths in Canada (including lung cancer)
American Lung Association: • About 335,000 Americans die of lung
disease every year
• Lung disease is No. 3 killer in America, responsible for 1 in 7 deaths
• Lung disease and other breathing problems are No. 1 killer of babiesyounger than 1 year old
• > 30 million Americans are living with Chronic Obstructive Pulmonary Disease
Source: University of Maryland Medical Center web site
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Market / Commercialization Potential
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U$ (billions)
Source: GlobalData Report16
Wheezing is a common noise in respiratory disorders
All ages
Prevalence of Childhood Asthma:* 10% - 20% (varies with countries)
Adult Asthma:* 12%
Adult COPD:* 5%
USA
23 million Asthmatics
Over 15 million cases of COPD
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Wheeze Monitoring
Values Accurate detection and assessment of wheezes
prompt diagnosis & appropriate treatment of Asthma, COPD, etc,
Improved symptom (wheeze) control reduced hospitalization healthcare savings
How (Current Clinical Methods) Auscultation using a stethoscope Based on history - patient recall or asthma diary or audio
or audio-visual recording by patients
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Problem of Wheeze Monitoring
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Auscultation with stethoscope is subjective, not for long-duration monitoring
Next!
History of occurrence is important but patients’ descriptions are often
subjective with limited accuracy
Wheezes often occur at night; Intermittent wheezes are not
easily monitored at night18
The System Needed
A screening system that records and analyzes wheezes over an extended duration of time
A simple and robust system suitable for both clinical and home use
A system that allows the patient to use at anywhere and anytime - hence no skin contact, no external wirings that restrict movements
Non-Skin Contact Wearable System
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Wearable
Wheeze
Monitoring
System @ NTU
III
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Overall System Design
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Goal
Study and develop a wearable solution for the detection,
recording, and analysis of wheezes,
anywhere and over a long duration,
for the purpose of wheeze conditions monitoring
(including treatment response)
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Wearable wheeze detection and recording system with Signal Analysis Tools for physicians
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PI: A/Prof. W Ser (NTU) and A/Prof. Daniel YT Goh (NUH)Funded by A*STAR & MOE
Wearable System
Signal Analysis
Tools
Signal Analysis
Tools
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Our Wearable System Design
Respiratory sounds (lung, tracheal) are recorded by sound sensors (stethoscopes or air-conductance microphones)
Sensor outputs are band-passed, amplified, sampled, and segmented.
A low-complexity algorithm is applied to detect presence of wheezes
Segments with wheezes are recorded & their severity estimated; respiratory & heart rates are estimated too
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Our Signal Analysis Tools (SAT)
Data (wheezing sounds) recorded in wearable suite are further processed by the SAT
The SAT has several functions
Signal manipulation
General signal processing
Respiratory signal analysis
The SAT can also be used for training purposes
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Signal Manipulation
• Signal copy, cut, paste• Signal amplification • Labeling• Filtering• Segmentation
General Signal Processing
• Signal Energy Analysis• Power Spectral Analysis• Pitch Analysis• Format Analysis• Zero-Crossing Analysis• Classification
Respiratory Signal Analysis
Respiratory parameters• Amplitude & periodicity of wheeze, Wheeze
length in a breathing cycle, etc. Physiological parameters
• Types of wheeze (monophonic or polyphonic, inspiration or expiration, etc.), changes in severity after treatment, Wheeze location, etc.
25
Problems and Challenges I
Sound based design sensitive to external audio noise How sensors are placed on wearable suite important Need filter and noise reduction algorithms
Wearable power and packaging constraint Need to operate at low sampling rates Need low complexity processing algorithms Need low-power platform (processor) Only small dry batteries allowed No external wiring
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Problems and Challenges II
Unsupervised wearing and monitoring Need to design for ease of wearing Need to use multiple sensors Cannot have air-gap between sensors and body
Different sizes of wearers Need flexible length of suite fastening Need a few sizes
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Wearable
Wheeze
Monitoring
System @ NTU
III
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Wearable Sensors & Platform
Suite
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Our Inward- & Outward- LookingSensing Designs
Wearable Suite
+
Outward-looking Microphone Array
Inward-looking Distributed Sensors
Medical Knowledge
Sound Sensors
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Wearable Wheeze Detection SystemVersion #1: Microphone Array, Sampling (1K, 12-bit)
DSP based System SpeakerNotebook for
Playing Sounds
TMS320C6713 DSP board
4-channels Microphone Array (amplifier and filter included)
Experimental Set UpLED Indicators
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(Version #2: PDA based, 2-microphone design)
PDA based System
Microphones (head phone)
PDA (HP iPAQ hx2700 series)
Signal conditioning
NI CF6004 DAQ 15-pin Compact Flash connector
Waveform display
Wheeze and Snore Indicators
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(Version #3: Embedded Processor based System)
Notebook for experimental controlFont View of Wearable Suite
Signal ConditioningNI Daq
Signal conditioning
4-channel modified stethoscopes or microphones
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Sensors and Placements of Sensors
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Inward-looking system Stethoscope is modified by replacing long
acoustic tube with a microphone and wire Microphone based system uses special acoustic
coupler design 4 distributed sensors: tracheal, left lung, heart,
and right lung
Outward-looking system 4-channel microphone array design End-fire array configuration
Bandpass Filter, ADC, Platform
The bandpass filter is designed to have a bandwidth of up to 1 KHz (without DC)
The ADC has a sampling rate of 2 KHz and a resolution of 10 bits
The platform uses one MSP430 chip for each channel (for 4 channels)
The selected sounds are stored in a micro SD card
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Used for Processing
1 Recording Cycle
(Wheeze Detection)
Data Block(3 sec)
Data Block(3 sec)
Data Block(3 sec)
Data Block(3 sec)
Data Block(3 sec)ADC 1 Data Block
(3 sec)
Used for Processing(Wheeze Detection)
Data Block(3 sec)
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Wearable
Wheeze
Monitoring
System @ NTU
III
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Wearable Signal
Processing Algorithms
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Types of Wheeze Detection Algorithms
Time-domain methods (waveform analysis, Kurtosis, zero-crossing rate …)
Frequency-domain spectral analysis methods
Time-frequency analysis (peak detection)
Pattern recognition approach
etc.
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Spectral Analysis Methods
T. R. Fenton, H. Pasterkamp, A. Tal and V. Chernick, “Automated Spectral Characterization of Wheezing in Asthmatic Children”, IEEE Trans. on Biomedical Engineering, vol. BME-32, No. 1, pp50-55, 1985.
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• f1 to f2: Wheeze frequencies
• Pave: Average power in f1 to f2• Af: Scaling factor (= 15)
• Wheeze Detection Criteria:• > f1 and > Af x Pave
• Results: (5 Asthmatic and 2 normal teenagers): < 2% for false +ve and false -ve
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Time-Frequency Analysis Methods Search for peaks in successive spectrum & decide using
threshold relating to amplitude, frequency, duration and number of wheezes
A. Homs-Corbera, R. Jane etc., “Time-frequency detection and analysis of wheezes during forced exhalation,” IEEE Trans. on Biomedical Eng., vol. 51, No. 1, pp182-186, 2004.
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Time-Frequency Plot
Peaks Extraction
Results (37 asthmatic, 23 normal) at high, medium and low air flow rates
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Pattern Recognition Methods
Typically 3 stages: Feature extraction Dimensionality reduction Pattern classification.
Commonly used Features: MFCC, LPC, AR model, Wavelet coefficients, etc.
Dimensionality Reduction Algorithms: SVD, PCA, etc.
Classification Methods: k-NN Classifier, DTW, VQ, NN, HMM, etc.
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Pattern Recognition Methods (Example) Use Wavelet Packet Decomposition for feature extraction Use Learning Vector Quantization for classification
L. Pesu, P. Helistö, E. Ademovic, J.-C. Pesquet, A. Saarinen and A.R.A. Sovijärvi, “Classification of respiratory sounds based on wavelet packet decomposition and learning vector quantization,” Technology and Health Care, vol. 6, pp. 65–74, Jun1998.
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Results { Sensitivity = TP / (TP + FN); Positive predictivity = TP / (TP + FP) }
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Problems of Existing AlgorithmsNot accurate enough or computationally
too expensive for wearable solutions
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Our ApproachReview wheeze signal characteristics and
attempt to derive simpler but effective methods that over-detect/record
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Re-visiting Wheeze Signals
Energy concentrates at well below 1000 Hz Contrasting energy distribution across frequency Typically, wheeze occurs only in part of a breathing cycle
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1 2 3 4 5 6 7 8
-0.5
0
0.5
Time (s)
ampl
itude
Time (s)
Freq
uenc
y (H
z)
1 2 3 4 5 6 7 80
500
1000
Time (s)
frequ
ency
(Hz)
1 2 3 4 5 6 7 80
500
1000
Normal Breath Wheeze
Am
plitu
deFr
eque
ncy
(Hz)
43
Our Wheeze Detection Algorithm
Design goal: 1 or 2 parameters, low complexity, robust, and suitable for wearable systems
Received signals:
• w(t): Wheeze signal• n(t): Non-wheeze signals (e.g. breath sound, noise)
( ) ( ) ( )s t w t n t= +
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Our Wheeze Detection Algorithm (Cont’d) Short-Time Fourier Transform
• h(t) is the window function, τ is the time shift, and “*” is the complex conjugate operator.
Peak Detection by Masking: For each STFT, determine moving thresholds and use them to select “peaks” (dominant values)
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* 2( , ) ( ) ( ) dj ftS f s t h t e tπτ τ+∞ −
−∞= −∫
∑=
=L
na fnS
LfnS
1),(1),(
≤−>−−
=0),(),(00),(),(),(),(
),(fnSfnSiffnSfnSiffnSfnS
fnSa
aap
45
Our Wheeze Detection Algorithm (Cont’d)
Entropy based Feature Extraction: Assuming there are Ndominant components, C1, C2, …, CN, in Sp(n, f), calculate
Entropy can be calculated as
Smoothed Entropy (by averaging filtering)
Features can be extracted as, for example,
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, 1, 2, ,nn N
nn
cp n Nc
=
= =∑
1log ( )
N
n b nn
E p p=
= −∑
∑=
=M
tta E
ME
1
1
)min()max()min(/)max(
aadiff
aaratio
EEEEEE
−==
46
Our Wheeze Detection Algorithm (Cont’d)
Wheeze detection:
Set Tratio and Tdiff as the thresholds for Eratio and Ediff
Presence of wheeze can be decided by (for example)
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ratioratio TE > diffdiff TE >andifWheeze is present
ratioratio TE < diffdiff TE <andifNo Wheeze
OtherwiseUnable to determine
47
Our Wheeze Severity Estimation Algorithm
Values of Eratio or Ediff are computed once every 3 seconds
Severity Score = Number of positive detections in 1 minute
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Our Respiratory Rate Estimation Algorithm
Rate estimated from second peak of autocorrelation
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Lab Tests,
Pre-Clinical
Trials, Results
IV
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Results of Wheeze Detection (Downloaded Data)
At SNR = 2dB, 80% of Sensitivity & Specificity have beenachieved (9 wheezy samples and 6 normal samples)• Sensitivity = TP/(TP+FN) x 100%; Specificity = TN/(TN+FP) x 100%
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-10 -8 -6 -4 -2 0 2 4 60
10
20
30
40
50
60
70
80
90
100
SNR (dB)
Det
ectio
n A
ccur
acy
(%)
true Positivefalse Negativetrue Negativefalse Positive
1 1.5 2 2.5 3 3.50.4
0.6
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1
1.2
1.4
1.6
1.8
2
2.2
2.4
Ent
ropy
Diff
eren
ce
Entropy Ratio
normalwheeze
Sensitivity
Specificity
51
Results of Wheeze Detection (Pre-Clinical Trial)
Performance: Sensitivity > 85%; Specificity > 70%
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Data Collection at National University Hospital, Singapore
More than 80 samples have been collected
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Results of Severity Estimation Algorithm (Pre-Trial)
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Samples Severity Scores Doctor’s Assessment
1 0 No Wheeze
2 10 Low
3 35 Yes
4 15 Low
5 10 Low
6 20 Low
7 5 Low
8 95 Yes
53
Results of Respiratory Rate Estimation (Pre-Trial)
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10 15 20 25 30 35 40 45 5010
15
20
25
30
35
40
45
50
55
Actual RR (bpm)
Est
imat
ed R
R (b
pm)
Conclusions
and
Future Work
V
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Conclusions & Future Work
Wearable sound based sensing (+ PC signal analysis) can be effective for automatic wheeze detection
Sensitivity higher than 85% and specificity above 70% have been achieved using our algorithm/system for data collected from some real patients (more data are needed to validate the results)
Future work includes extensive clinical trials and studies on other types of respiratory disorders
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Some of our Research Publications W Ser, JM Zhang, JF Yu, and TT Zhang, “A Wearable Multiple Audio-Sensor based
Design for Respiration Monitoring,” patent filed 2010. W Ser, ZL Yu, JM Zhang, and JF Yu, “A Wearable System Design with Wheeze
Signal Detection,” 5th International Workshop on Wearable and Implantable BodySensor Networks, 2008.
W Ser, TT Zhang, JF Yu, and JM Zhang, “Detection of Wheeze by DistributedMicrophone Array Analysis of Breathe and Lung Sounds,” The 6th InternationalWorkshop on Wearable and Implantable Body Sensor Networks, 2009.
JM Zhang, W Ser, JF Yu, and TT Zhang, “A Novel Wheeze Detection Method forWearable Monitoring Systems,” The International Symposium on IntelligentUbiquitous Computing and Education, 2009.
TT Zhang, W Ser, Daniel YT Goh, JM Zhang, JF Yu, C Chua, IM Louis, “SoundBased Heart Rate Monitoring for Wearable Systems,” The 7th International Workshopon Wearable and Implantable Body Sensor Networks, 2010.
Daniel YT Goh, W Ser, JM Zhang, JF Yu, C Chua, IM Louis, TT Zhang,“Development of a Wearable System to Record and Analyse Respiratory Sounds:Preliminary Data from Wheeze Analysis,” The 26th International PaediatricAssociation Congress, 2010.
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Some other References
T. R. Fenton, H. Pasterkamp, A. Tal and V. Chernick, “Automated SpectralCharacterization of Wheezing in Asthmatic Children”, IEEE Trans. on BiomedicalEngineering, vol. BME-32, No. 1, pp50-55, 1985.
Y. Shabtai-Musih, J.B. Grotberg and N. Gavriely, “Spectral content of forcedexpiratory wheezes during air, He, and SF6 breathing in normal humans,” J. Appl.Physiol., vol. 72, pp. 629–635, 1992.
R. Jané, D. Salvatella, J. A. Fiz, and J. Morera, “Spectral analysis of respiratorysounds to assess bronchodilator effect in asthmatic patients,” Proc. of the 20th IEEEEMBS, Hong Kong, 1998.
M. Waris1, P. Helistö1, S. Haltsonen1, A. Saarinen2, A.R.A. Sovijärvi, “A new methodfor automatic wheeze detection,” Technology and Health Care, vol. 6, pp. 33-40, Jun1998.
L. Pesu, P. Helistö, E. Ademovic, J.-C. Pesquet, A. Saarinen and A.R.A. Sovijärvi,“Classification of respiratory sounds based on wavelet packet decomposition andlearning vector quantization,” Technology and Health Care, vol. 6, pp. 65–74,Jun1998.
A. Homs-Corbera, R. Jane etc., “Algorithm for time–frequency detection and analysisof wheezes,” IEEE EMBS, pp. 2977-2980, New York, USA, Sept. 2000.
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Some other References (Cont’d)
S. Lehrer, “Understanding Lung Sounds,” third edition., W.B. Saunders Company,Pennsylvania, USA, 2002.
M. Bahoura and C. Pelletier, “New parameters for respiratory sound classification,”Proc. IEEE CCECE, pp. 1457-1460, Montreal, Canada, May 2003.
Y. P. Kahya, E. Bayatli ect. , “Comparison of different feature sets for respiratorysound classifiers,” Proc. of the 25th IEEE EMBS, pp. 2853-2856, Cancun, Mexico,Sept. 2003.
A. Kandaswamy, C. Sathish Kumar, Rin. PL Ramanathan, S. Jayaraman, and N.Malmurugan, “Neural classification of lung sounds using wavelet coefficients,”Computers in Biology and Medicine, vol. 34, pp. 523-537, 2004.
Patents and product information of KarmelSonic Products LVQ-PAK Software, http://www.cis.hut.fi/research/som_lvq_pak.shtm, Department of
Information and Computer Science, Helsinki University of Technology, Finland. School of Medicine, University of California at Davis, USA, Review of Lung Sounds.
[Online] http://medocs.ucdavis.edu/IMD/420C/sounds/lngsound.htm
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Questions or
Comments?
Thank You
60
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