1. Biometric Classification of Heart Sound for Continual User Authentication and Clinical Applications
Leigh Anne Clevenger
Pace University, DPS ’16
Advisor: Dr. Charles C. Tappert

2. Heart Sound Biometrics - Mobile Device User Authentication
Time = 0; Authenticate?
Yes No
No Access
Time = 0+ to 30 min; Authenticate?
Yes No
11/13/2018
L.A. Clevenger
No Access – return to fingerprint authentication

3. Heart Sound Biometrics – Mobile Device Clinical Applications
Time = 0; Authenticate?
Yes No
No Notification Sent
Time = 0+ to 30 min; Authenticate?
Notify Cardiologist or 911
Yes No
11/13/2018
L.A. Clevenger

4. Diagram of Heart and Typical Waveform
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L.A. Clevenger

5. Phao, 2007: Heart Sound as a Biometric
First paper in this research area, published in Pattern Recognition
User authentication and identification accuracy rates 96% - 99%, 1000 data samples on their own dataset
Welch Allyn Meditron electronic stethoscope, sampling rate of 2 KHz and 16 bits. 0.5 sec windows
11/13/2018
L.A. Clevenger

6. Spadaccini and Beritelli, 2007-2013: HSCT11 Heart Sound Evaluation
Spadaccini and Beritelli provide a 200 person, two recordings each, public database of normal heart sounds (HSCT11), and use the S1 and S2 features for user authentication
Biometric Analysis Modality
Signal split into 4 second windows
S1 and S2 identified, then window endpoints are connected
Apply Chirp z-Transform (CZT), allowing high resolution analysis of narrow frequency bands
Features extracted by Mel-Frequency Cepstral Coefficients (MFCC). Added to the feature vector First-to-Second Ratio (FSR) as the average power ratio of S1 to S2.
Structural and Statistical Classification
Structural – distance between input signal and stored template, using cepstral distance and an amplifying factor. Equal Error Rate (EER) of 36.86%
Statistically – uses Gaussian Mixture Model – Universal Background Model (GMM-UBM) recognition technique. EER of 13.66%
11/13/2018
L.A. Clevenger

7. Spadaccini and Beritelli: HSCT11 Heart Sound Evaluation
11/13/2018
L.A. Clevenger

8. Zhao, 2013: Marginal Spectrum Analysis-based System
Zhao et al use their own data, plus the HSCT11 dataset, for user authentication
Database – 40 participants, 280 heart sounds sec windows
Results
Marginal Spectrum Analysis (MSA) – recognition rate of 94%
Fourier spectrum (traditional algorithm) – recognition rate of 84%
MSA on HSCT11 database – recognition rate of 92%
11/13/2018
L.A. Clevenger

9. Heart Sound Biometric Analysis
Select waveforms from database
Preprocess to find S1-S2 pairs; Apply noise reduction if needed
Create data frames
Generate Feature Vectors for each frame
Calculate matching score
Train and test the system, and evaluate classifier results
11/13/2018
L.A. Clevenger

10. Pre-processing to improve authentication accuracy
Spadaccini
split input signal into time windows
S1 and S2 identified, placed in separate time windows, window endpoints are connected
Chirp z-Transform (CZT) is applied, allowing high resolution analysis of narrow frequency bands
Zhao, Sun, Abo-Zahhad
De-noise using selected wavelets: improved Empirical Mode Decomposition (EMD) wavelet algorithm and the Shannon energy envelope algorithm, Discrete Wavelet Transformation (DWT)
11/13/2018
L.A. Clevenger

11. FFT and Cepstrum Generation
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L.A. Clevenger

12. Mel Frequency Cepstral Coefficient Generation
Mel filterbank: maps frequency to mel scale – linear below 1kHz, log above
Cepstrum is spectrum of log of the mel spectrum
Result is 12 cepstral coefficients per frame, and 1 value for energy – the sum over time of the power of the samples in the frame
Additional data for improved authentication accuracy can be appended to the MFCC feature vector: FSR, wave average, wave average differences, etc
11/13/2018
L.A. Clevenger

13. Innovative Technologies supporting Heart Sound Biometrics
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L.A. Clevenger

14. Demo of Heart Sound Input
Visualization and audio using Audacity
ThinkLabs Digital Stethoscope, HSCT11 Data
iStethoscope Pro iPhone app, Heart Challenge A Data
3M Littmann Digital Stethoscope, Medscape Data
11/13/2018
L.A. Clevenger

15. Research Plans – Next Steps
Investigate literature and current regulations for cardiac clinical applications scenarios, to identify opportunities and challenges
Continue to create features for heart sound, using clinical features of 1st and 2nd beats, and additional features such as First-to-Second beat ratio. Use the HSCT-11 public heart sound database and scilab scripts.
Incorporate methodologies from related research
Implement machine learning algorithms for training and testing the system
11/13/2018
L.A. Clevenger

16. Backup
11/13/2018
L.A. Clevenger

17. Fundamentals of heart sound
Heart function
Is nonlinear
Is periodic
Has time-varying characteristics
Heart rate variability (HRV) is non-stationary – it is effected by breathing, variation in blood flow (cardic-hemodynamics), and the environment
Has randomness: palpitations - skipped or added beats; arrhythmia – change in beating patterns
Typical heart sound measurement facts
Range from Hz
S1 - First heart sound between Hz, closure of the tricuspid and mitral valves
S2 - Second heart sound between Hz, closure of the aortic and pulmonary valves
Second peak Hz
Research for user authentication often ignores 3rd and 4th heart sound as “murmur”
In research, sampling frequencies used for measurement vary, some examples are 6400 Hz and Hz
11/13/2018
L.A. Clevenger

18. MFCC Generation Widely used for speech and speaker recognition
Most often used feature extraction algorithm in heart sound biometric literature
Results in 13 element feature vector
Generate using *.wav files from Beritelli database and m scripts with scilab (free version of matlab)
Scripts from
Resources
D. Jurafsky, J. H. Martin, “Speech and Language Processing”
11/13/2018
L.A. Clevenger

19. Public Heartsound Databases and Digital Stethoscope Sensors
#signals
recording length (s)
sensor used
frequency
HSCT-11 [16,17]
206 60
ThinkLabs
11025 Hz, 16 bits per sample
WelchAllyn [18]
150 30
Not available
16 kHz
Heartchallenge A [19]
176
1 to 30
iStethoscope Pro
30 kHz to 44 kHz
Heartchallenge B [19]
656
DigiScope
4 kHz
Primer [20] 23
8 kHz to 44 kHz
Texasheart [21] 22
8 kHz
Medscape [22] 7
Littmann Stethoscope
10 kHz
Stethoscope sensor
Reference
ThinkLabs Rhythm Digital Electronic Stethoscope
iStethoscope Pro iOS app
DigiScope
3M Littmann Electronic Stethoscope Model 3200
11/13/2018
L.A. Clevenger