1. Carotid Pulse Detection and Signal Enhancement in a Biometric Identification System
Tayseer O’Brien
Dr. R. Eric McGregor
INTRODUCTION
The existing MatLab software program named Thoracic Identification System (TIS), written by Dr. R. Eric McGregor for the project titled Thoracic Biometric –Investigations of the Human Heartbeat as a Biometric: Heart Sounds, Electrocardiogram, and Vibrometry, investigated the use of traits from the human heart as a biometric that cannot be easily disguised. The program demonstrated good performance on Electrocardiogram(ECG) data but not on the Carotid Pulse(CP) data. This project focused on improving the performance on the CP data by modifying the R-Peak Heartbeat Detection and Heartbeat Segment Selection algorithms. These modifications significantly increased the overall performance from 27.27% to 60.61%.
MODIFICATION#1  R-Peak Heartbeat Detection Algorithm
Algorithm rewrite
A window size, that is set by user, traverses through the heartbeats and segments them by detecting the maximum value (R-Peak)
The window size determines the least distance between each possible R-Peak found
Peaks are missed but there are no false-positives
TEST RESULTS
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Before
Biometric Identification System
After
MODIFICATION#2  Heartbeat Segment Selection Algorithm
Original Algorithm – chooses k random beats or k consecutive beats
Improved Algorithm - picks k beats that are closest to the subject’s nominal (average) heartbeat
unique heartbeat segments that are different from the rest of the subjects
EXPERIMENTAL DATA
36 total subjects recordings trials from each
Trials 1-4  at rest
Trials 5-8  after exercise
Trials 9-12  5 minutes after exercise
Collected the following cardiac data:
Carotid Pulse (CP) using Laser Doppler Vibrometer (LDV)
Electrocardiogram (ECG) using electrodes placed on subjects’ thoracic region
Phonocardiogram (PCG) using a digital stethoscope
CONCLUSION
This project allowed the matching performance of TIS to significantly increase from 27.27% to 60.61% in identifying subjects based on their CP data when training on rested heartbeats from recording 1 and testing on elevated heartbeats in recording 9. Modifying the Heartbeat Detection and the Heartbeat Segment Selection algorithms made it more possible for the program to see the R-Peaks for segmentation purposes and to determine which segments are mostly unique to that subject.
BIBLIOGRAPHY
R.E.McGregor; S.Schuckers; J.Skufca, "Thoracic Biometrics - Investigations of the Human Heartbeat as a Biometric: Heart Sounds, Electrocardiogram, and Vibrometry," CIA Report (not published), 2013.
M. Chen; J.A. O'Sullivan; N. Singla; E.J. Sirevaag; S.D. Kristjansson; P.H. Lai; A.D. Kaplan; J.W. Rohrbaugh; , "Laser Doppler Vibrometry Measures of Physiological Function: Evaluation of Biometric Capabilities," IEEE Transactions on Information Forensics and Security, v..5, no.3, pp , 2010.
M. Chen, “Laser Doppler Vibrometry as Biometric”, M.S. Thesis, Washington University, 2007.
Select recordings for training and testing
Training
Identify R-Peaks
Construct spectrogram
Heartbeat segment selection
Classification
(Matching)
Testing
Identify R-Peaks
Construct spectrogram
Heartbeat segment selection