A.D. Kaplan

Washington University in St. Louis, Saint Louis, MO, United States

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Publications (11)5.94 Total impact

  • IEEE Trans. Biomed. Engineering. 01/2012; 59:744-753.
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    ABSTRACT: The electrocardiogram (ECG) is an emerging biometric modality that has seen about 13 years of development in peer-reviewed literature, and as such deserves a systematic review and discussion of the associated methods and findings. In this paper, we review most of the techniques that have been applied to the use of the electrocardiogram for biometric recognition. In particular, we categorize the methodologies based on the features and the classification schemes. Finally, a comparative analysis of the authentication performance of a few of the ECG biometric systems is presented, using our inhouse database. The comparative study includes the cases where training and testing data come from the same and different sessions (days). The authentication results show that most of the algorithms that have been proposed for ECG-based biometrics perform well when the training and testing data come from the same session. However, when training and testing data come from different sessions, a performance degradation occurs. Multiple training sessions were incorporated to diminish the loss in performance. That notwithstanding, only a few of the proposed ECG recognition algorithms appear to be able to support performance improvement due to multiple training sessions. Only three of these algorithms produced equal error rates (EERs) in the single digits, including an EER of 5.5% using a method proposed by us.
    IEEE Transactions on Information Forensics and Security 01/2012; 7(6):1812-1824. · 1.90 Impact Factor
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    ABSTRACT: The method of laser Doppler vibrometry (LDV) is used to sense movements of the skin overlying the carotid artery. When pointed at the skin overlying the carotid artery, the mechanical movements of the skin disclose physiological activity relating to the blood pressure pulse over the cardiac cycle. In this paper, signal modeling is addressed, with close attention to the underlying physiology. Segments of the LDV signal corresponding to single heartbeats, called LDV pulses, are extracted. Hidden Markov models (HMMs) are used to capture the dynamics of the LDV pulses from beat to beat based on pulse morphology; under resting conditions these dynamics are primarily due to respiration-related effects. LDV pulses are classified according to state, by computing the optimal state path through the data using trained HMMs. HMM state dynamics are examined within the context of respiratory effort using strain gauges placed around the abdomen. This study presented here provides a graphical model approach to modeling the dependence of the LDV pulse on latent states.
    IEEE transactions on bio-medical engineering 12/2011; 59(3):744-53. · 2.15 Impact Factor
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    ABSTRACT: In this paper, we present the results of an analysis of the electrocardiogram (ECG) as a biometric using a novel short-time frequency method with robust feature selection. Our proposed method incorporates heartbeats from multiple days and fuses information. Single lead ECG signals from a comparatively large sample of 269 subjects that were sampled from the general population were collected on three separate occasions over a seven-month period. We studied the impact of long-term variability, health status, data fusion, the number of training and testing heartbeats, and database size on ECG biometric performance. The proposed method achieves 5.58% equal error rate (EER) in verification, 76.9% accuracy in rank-1 recognition, and 93.5% accuracy in rank-15 recognition when training and testing heartbeats are from different days. If training and testing heartbeats are collected on the same day, we achieve 0.37% EER and 99% recognition accuracy for decisions based on a single heartbeat.
    Information Forensics and Security (WIFS), 2010 IEEE International Workshop on; 01/2011
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    ABSTRACT: A novel approach for remotely sensing mechanical cardiovascular activity for use as a biometric marker is proposed. Laser Doppler Vibrometry (LDV) is employed to sense vibrations on the surface of the skin above the carotid artery related to arterial wall movements associated with the central blood pressure pulse. Carotid LDV signals are recorded using noncontact methods and the resulting unobtrusiveness is a major benefit of this technique. Several recognition methods are proposed that use the temporal and/or spectral information in the signal to assess biometric performance both on an intrasession basis, and on an intersession basis where LDV measurements were acquired from the same subjects after delays ranging from one week to six months. A waveform decomposition method that utilizes principal component analysis is used to model the signal in the time domain. Authentication testing for this approach produces an equal-error rate of 0.5% for intrasession testing. However, performance degrades substantially for intersession testing, requiring a more robust approach to modeling. Improved performance is obtained using techniques based on time-frequency decomposition, incorporating a method for extracting informative components. Biometric fusion methods including data fusion and information fusion are applied to train models using data from multiple sessions. As currently implemented, this approach yields an intersession equal-error rate of 6.3%.
    IEEE Transactions on Information Forensics and Security 10/2010; · 1.90 Impact Factor
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    ABSTRACT: A novel approach using mechanical physiological activity as a biometric marker is described. Laser Doppler Vibrometry is used to sense activity in the region of the carotid artery, related to arterial wall movements associated with the central blood pressure pulse. The non-contact basis of the LDV method has several potential benefits in terms of the associated non-intrusiveness. Several methods are proposed that use the temporal and/or spectral information in the signal to assess biometric performance both on an intra-session basis, and on an intersession basis involving testing repeated after delays of 1 week to 6 months. A waveform decomposition method that utilizes principal component analysis is used to model the signal in the time domain. Authentication testing for this approach produces an equal-error rate of 0.5% for intra-session testing. However, performance degrades substantially for inter-session testing, requiring a more robust approach to modeling. Improved performance is obtained using techniques based on time-frequency decomposition, incorporating a method for extracting informative components. Biometric fusion methods including data fusion and information fusion are applied in multi-session data training model. As currently implemented, this approach yields an inter-session equal-error rate of 9%.
    Proc SPIE 01/2010; 5:449-460.
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    ABSTRACT: A laser Doppler vibrometer (LDV) is used to sense movements of the skin overlying the carotid artery. Fluctuations in carotid artery diameter due to variations in the underlying blood pressure are sensed at the surface of the skin. Portions of the LDV signal corresponding to single heartbeats, called the LDV pulses, are extracted. This paper introduces the use of hidden Markov models (HMMs) to model the dynamics of the LDV pulse from beat to beat based on pulse morphology, which under resting conditions are primarily due to breathing effects. LDV pulses are classified according to state, by computing the optimal state path through the data using trained HMMs. HMM state dynamics are compared to simultaneous recordings of strain gauges placed on the abdomen. The work presented here provides a robust statistical approach to modeling the dependence of the LDV pulse on latent states.
    Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 01/2010; 2010:5273-6.
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    ABSTRACT: It is known that when a pattern recognition system is not subject to memory constraints on pattern representations and sensory information is not compressed, the recognition rate is bounded by the mutual information between the memory and sensory representations. In this paper, we investigate the recognition rates of Laser Doppler Vibrometry (LDV) signals obtained from 285 individuals. We consider four cases corresponding to four different assumptions as to the structure of the data source and noisy measurements, and present the results of the mutual information bounds. In particular, we show the bounds of the recognition rates for each feature of the LDV signal.
    01/2010;
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    ABSTRACT: Understanding the variability of the cardiac-related signals caused by physical exercise is an interesting and important problem. To our knowledge, there is no paper evaluating the biometric consistency of the cardiovascular based signals during the physical exercise, or the extent to which the signals can recover after that. A novel method of remotely sensing mechanical activity related to the carotid pulse with Laser Doppler Vibrometry (LDV) has been developed. Encouraging results are obtained on the evaluation of the LDV cardiovascular signal as a biometric marker. A new protocol is set up to produce changes in heart rate by physical exercise. Spectral based approaches are applied following the success in general biometric authentication. An equal error rate of 2.8% for inter-state tests indicates that the LDV pulse signal is quite stable even after moderate physical exercise. The performance degrades during exercise, especially when the heart rate reaches 55% of the age-adjusted theoretical maximum heart rate. When the test individuals start resting, the performance improves as the heart rate recovered within seconds. We can say that the short-term variability caused by heart rate fluctuations and respiration changes recover with enough stability in a short time for biometric consistency.
    Biometrics, Identity and Security (BIdS), 2009 International Conference on; 10/2009
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    ABSTRACT: Small movements of the skin overlying the carotid artery, arising from pressure pulse changes in the carotid during the cardiac cycle, can be detected using the method of Laser Doppler Vibrometry (LDV). Based on the premise that there is a high degree of individuality in cardiovascular function, the pulse-related movements were modeled for biometric use. Short time variations in the signal due to physiological factors are described and these variations are shown to be informative for identity verification and recognition. Hidden Markov models (HMMs) are used to exploit the dependence between the pulse signals over successive cardiac cycles. The resulting biometric classification performance confirms that the LDV signal contains information that is unique to the individual.
    Proc SPIE 05/2009;
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    ABSTRACT: We propose a new biometric approach based on cardiovascular signals recorded using laser Doppler vibrometry (LDV) with a robust feature selection method. A novel feature selection method provides robustness against physiological variability of a given individual. LDV signals were collected from 191 individuals under controlled conditions during three sessions, each at intervals of one week to six months. The methods described here are based on a time-frequency decomposition of the LDV signal in which the log-power of the decomposition values are used as features. In identity verification tasks, equal error rates in the single digits can be achieved with testing periods as short as 4 s.
    Biometrics Symposium, 2008. BSYM '08; 10/2008