Erik J. Sirevaag

ECG Biometric Recognition: A Comparative Analysis

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.

ECG biometrics: A robust short-time frequency analysis

In this paper, we present the results of an analysis of the electrocardiogram (ECG) as a biométrie 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 biométrie 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.

Laser Doppler Vibrometry Measures of Physiological Function: Evaluation of Biometric Capabilities

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%.

Visual P300 and the self-directedness scale of the Temperament and Character Inventory

Reduced amplitude of the P300 event-related brain potential has been associated with several psychopathological conditions and is thought to represent brain dysfunction in such conditions. Predisposition to personality disorders and psychopathology in general is also associated with low scores on the self-directedness (SD) scale of the Temperament and Character Inventory. The present preliminary study investigated the relationship between amplitudes of P300 elicited by rare target stimuli in a visual oddball task and SD scores in 58 healthy participants. P300 was found to be significantly reduced in subjects with low SD, as supported by correlational analysis and by comparison of groups formed on the basis of SD scores. This finding may be relevant to prior findings indicating reduced P300 amplitudes in a variety of psychopathological conditions and suggests that a common vulnerability factor, reflected in the low SD personality scores, may contribute to the P300 reduction in psychiatric populations.

Hidden State Models for Noncontact Measurements of the Carotid Pulse Using a Laser Doppler Vibrometer

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.

Biometrics with physical exercise using Laser Doppler Vibrometry measurements of the carotid pulse

Understanding the variability of the cardiacrelated 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.

A robust feature selection method for noncontact biometrics based on Laser Doppler Vibrometry

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.

An Integrative Approach for Studying the Etiology of Alcoholism and

Studies of alcoholism etiology often focus on genetic or psychosocial approaches, but not both. Greater understanding of the etiology of alcohol, tobacco and other addictions will come from integration of these research traditions. A research approach is outlined to test three models for the etiology of addictions--behavioral undercontrol, pharmacologic vulnerability, negative affect regulation--addressing key questions including (i) mediators of genetic effects, (ii) genotype-environment correlation effects, (iii) genotype x environment interaction effects, (iv) the developmental unfolding of genetic and environmental effects, (v) subtyping including identification of distinct trajectories of substance involvement, (vi) identification of individual genes that contribute to risk, and (vii) the consequences of excessive use. By using coordinated research designs, including prospective assessment of adolescent twins and their siblings and parents; of adult substance dependent and control twins and their MZ and DZ cotwins, the spouses of these pairs, and their adolescent offspring; and of regular families; by selecting for gene-mapping approaches sibships screened for extreme concordance or discordance on quantitative indices of substance use; and by using experimental (drug challenge) as well as survey approaches, a number of key questions concerning addiction etiology can be addressed. We discuss complementary strengths and weaknesses of different sampling strategies, as well as methods to implement such an integrated approach illustrated for the study of alcoholism etiology. A coordinated program of twin and family studies will allow a comprehensive dissection of the interplay of genetic and environmental risk-factors in the etiology of alcoholism and other addictions.

Laser Doppler vibrometry measures of physiological function: evaluation of biometric capabilities

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%.

Cardiovascular Biometrics: Combining Mechanical and Electrical Signals

The electrical signal originating from the heart, the electrocardiogram (ECG), has been examined for its potential use as a biometric. Recent ECG studies have shown that an intersession authentication performance <;6% equal error rate (EER) can be achieved using training data from two days while testing with data from a third day. More recently, a mechanical measurement of cardiovascular activity, the laser Doppler vibrometry (LDV) signal, was proposed by our group as a biometric trait. The intersession authentication performance of the LDV biometric system is comparable to that of the ECG biometric system. Combining both the electrical and mechanical aspects of the cardiovascular system, an overall improvement in authentication performance can be attained. In particular, the multibiometric system achieves ~2% EER. Moreover, in the identification mode, with a testing database containing 200 individuals, the rank-1 accuracy improves from ~80% for each individual biometric system, to ~92% for the multibiometric system. Although there are implementation issues that would need to be resolved before this combined method could be applied in the field, this report establishes the basis and utility of the method in principle, and it identifies effective signal analysis approaches.