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A New Biometric: Human Identification from Circulatory Function

John M. Irvine1, Steven A. Israel2, Mark D. Wiederhold3,

Brenda K. Wiederhold4

1SAIC, 20 Burlington Mall Road, Burlington, MA 01803, [email protected],

2

SAIC, 4001 North Fairfax Drive: Suite 450, Fairfax, VA 222033SAIC, 10260 Campus Point Drive, San Diego, CA 921214Virtual Reality Medical Center, 6160 Cornerstone Court East, San Diego, CA 92121

Abstract

Numerous biometric techniques exist for

verifying the identity of individuals. Traditional

biometrics, such as fingerprint, face, and iris

recognition, rely on a snapshot of data that are

rendered as images. This paper presents a new

biometric technique based on observation of

physiological functions related to circulation. In

particular, we present a biometric technique based on

the subjects electrocardiogram. The development

and validation of this new biometric technique poses

some interesting challenges for design of the

experiments and data analysis. Since a persons heart

rate can vary with mental and emotional state, we

developed a data collection protocol in which subjects

perform a variety of tasks designed to elicit varying

levels of stress or excitement. In developing and

validating the new biometrics, it is necessary to

identify features in the physiometric signals that are

unique to individuals, but invariant to mental and

emotional state. An estimate of the subjects mental

state, based on coincident physiological data, can be

used to refine the data processing and classification

techniques. In this paper we present the experimentprocedures, summarize the data analysis and

processing, and present initial performance results.

Introduction

Biometric techniques, such as face

recognition, finger print analysis, iris recognition, and

voice recognition have emerged as methods for

automatically identifying individuals. These

techniques can be implemented to provide automated

security for facilities, restrict access to computer

networks, or verify identification for on-line

transactions. This paper explores a new method for

human identification based on features of

cardiovascular function derived from standard

physilogical measurements. Several methods exist

for monitoring cardiovascular function, including the

electrocardiogram (ECG), pulse oximetry, dynamic

blood pressure, and acoustic monitoring of the heart

or pulse. Initial investigations suggest that these

signals contain information unique to an individual,

i.e., a biometric. [Irvine, et al (2001), Jang, et al

(2001), Biel, et al (2001)] A major challenge to

developing biometrics based on circulatory function

is the dynamic nature of the physiological process.

Heart rate varies with the subjects physical, mental,

and emotional state, yet a robust biometric must be

invariant across these changing states.

The ECG trace contains a wealth of

information. Researchers have been using ECG data

as a diagnostic tool since the early 20th century. More

recently, however, researchers been able to apply

digital analysis to the data [Golden (1973)]. In this

paper, we presents an extensive set of ECG

descriptors that characterize the trace of a heartbeat.

These ECG descriptors contain information that

appears to be stable across an individuals mental and

emotional state, while providing a unique identifier

for the individual. Analysis of several data sets

quantifies the performance of ECG as a biometric for

human identification.

The Electrocardiogram (ECG)

The ECG signal measures the change in

electrical potential over time. The trace of eachheartbeat consists of three complexes: P, R, and T.

The fiducial points corresponding to the peaks and

inflection points define each complex (Figure 1). The

labels in Figure 1 document the commonly used

medical science ECG fiducial points.

The heartbeat begins with the f iring of the

Sinoatrial (SA) node. The SA node () is the hearts

dominant pacemaker. The electrical signal radiates

outward causing the myocytes to depolarize and

compress rapidly by a movement of sodium (NA+)

ions. This is expressed as P wave of the ECG trace.

The depolarization rate slows dramatically when the

signal hits the atrio-ventricular (AV) node, where thechemical signal changes to relatively slow moving

calcium (CA+) ions. The change in contraction is

expressed as the gap between the P and the R

complexes. Once past the AV node, the signal passes

through to the cells lining the ventricles. The

ventricles contract rapidly, which produces the R

complex. Repolarization does not exactly mirror

polarization due to the chemical agents and the lag


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between the end of the electrical impulse and physical

displacement [Dubin (2000)].

Figure 1. Ideal ECG Signal: This figure depicts

two idealized heartbeats. The R-R interval

indicates the length of a heartbeat. The major

ECG complexes comprising one beat are indicated

by P, QRS, and T.

Data Collection

Two data collection campaigns provided the

ECG data for this study. For the first experiment,

data were collected from males and females between

the ages of 22 and 48. Twenty-nine individuals were

used and with twelve repeat sessions totaling forty-

one sessions within the dataset. Each individual

session contained a set of 7 two-minute tasks. The

tasks were designed to elicit different levels of mental

and emotional stress. The low stress tasks included a

baseline, a meditation task, and two recovery periods

following high stress tasks. The high stress were a

reading task, an arithmetic task, and virtual reality

driving simulation. Unlike conventional ECG data,the hardware for this series of experiments collected

ECG data at 1000 Hz, a much higher temporal

resolution than is typical for clinical instruments.

The second experiment used a greatly

simplified protocol and a standard, FDA approved

ECG device. Data were acquired from 36 subjects

during 51 sessions. Thus, data for two session are

available for 15 individuals. The two tasks performed

during this protocol were a (low s tress) baseline and

the same arithmetic stressor that was used in the first

experiment. Because a clinical instrument was used,

the ECG signal was recorded at 256 Hz and quantized

to 7 bits.

ECG Processing

To realize the ideal data structure (Figure 1),

the raw ECG data must be processed to remove the

non-signal artifacts. The first step is to eliminate

obvious the noise in the signal. Based upon the

structure of these noise sources, a filter was designed

and applied to the raw data. Figure 2 (a and b) show

the data sample of the high resolution ECG data. The

figures show that the raw data contain both high and

low frequency noise components. These noise

components alter the expression of the ECG tracefrom its ideal structure (Figure 1). The low

frequency noise is expressed as the slope of the

overall signal across multiple heartbeat traces in

Figure 2b. The low frequency noise is generally

associated with changes in baseline electrical

potential of the device and is slowly varying. Over

the 20 second segment, the potential change of the

ECG baseline inscribes approximately 1 wave

periods. The high frequency noise is associated with

electric/magnetic field of building power (electrical

noise) and the digitization of the analog potential

signal (A/D noise). The goal of filtering is to remove

the 0.06 Hz and 60 Hz noise while retaining theindividual heartbeat information between 1.10 and 40

Hz.

b.

Figure 2. Raw ECG Data 1000 Hz (a) 20 seconds (b) 2 seconds. The Y axis is electrical potential and the X

axis is time in seconds.

R-R Interval

P

QS

T

R


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Once the non-signal components were

removed from the ECG datastream, analysis of the

ECG trace located the fiducial positions. For human

identification, attributes were extracted from the P, R,

and T complexes (Figure 3). Four additional fiducial

points were identified. The locations of the four new

fiducial positions noted by an apostrophe () are at the

basal positions of the P and T complexes (Figure 3).

Collectively, the fiducials exploit the unique

physiology of an individual. Physically, the L and P

fiducials indicate the start and end of the atrial

depolarization. The corresponding S and T

positions indicate the start and end of repolarization.

Atrial

Depolarization

VentricularDepolarization

Ventricular

Repolarization

P

Q

R

S

T

L P S TS-T

segmentP-Q

interval

Q-T

interval

Atrial

Depolarization

VentricularDepolarization

Ventricular

Repolarization

P

Q

R

S

T

L P S TS-T

segmentP-Q

interval

Q-T

interval

Figure 3. ECG Trace based upon Cardiac

Physiology. L and P indicate the start and end ofatrial depolarization, the R complex indicates

ventricular depolarization, and the T complex

indicates the repolarization.

The fiducial points were extracted in the

time domain in two stages. The peaks were

established by finding the local maximum in a region

surrounding each of the P, R, and T complexes. The

base positions were determined by tracking downhill

and finding the location of minimum radius of

curvature. The potential response of a heartbeat is a

function of sensor placement for magnitude only.The sensor position does not affect the observed

timing of the individual P, R, and T complexes.

Therefore, the temporal distances among the fiducial

points are independent of the sensor placement.

Since the R position of the heartbeats was used for

aligning the waterfall diagram, the distances were

computed from the other fiducial points to the R

position.

The distances between the fiducial points

and the R position vary with heart rate. If a linear

relationship existed between heart rate and those

distances, normalization is computed as the extracted

distance divided by the LT distance. This approach

effectively scales the heartbeat to unit length. The

normalized features then represent the relative

positions of the fiducials within the heartbeats. The

linear normalization has a heuristic rather than a

physiological basis. The distance that an electrical

impulse travels along the atrial axis is fixed, so that

changes in heart rate are not evenly distributed across

the P, R, and T complexes.

Problems did arise in some of the processing

data. These problems fall into two classes: excessive

noise due to poor data collection and atypical ECG

traces where identification of the fiducial points is

difficult. To address the first type of problem, we are

investigating improved sensor placement and data

acquisition, with the aim of developing a device

suitable for operational use. The second type ofproblem generally corresponds to individuals with

unusual features in their ECG traces. For example,

one individual had a double peak in the P complex.

This anomaly was stable across tasks and sessions

and, therefore, would serve as a unique identifier.

Such anomalies, however, make it difficult or

impossible to compute the distance features we have

chosen. Methods for robust exception handling

could, or course, be developed for these types of

cases.

Classification of Heartbeat and Subjects

Using the features extracted from each

heartbeat, classification was performed to assign each

heartbeat to the corresponding individual. From the

original 15 attributes, 10-12 attributes were

commonly selected based on stepwise discriminant

analysis. The attribute selection process was

performed to ensure stable discrimination. To link

the performance of the heartbeat classification to

human identification, a voting procedure assigned the

classification to the individual corresponding to the

largest number of heartbeats.

The classification results correspond to

different partitionings of the data into training andtesting sets. To use ECG as a biometric, individuals

will enroll their information into the security system.

After enrollment, the users ECG will be interrogated

at the system. Operationally, the enrollment process

corresponds to training the classifier and the use of

the biometric to identify an individual corresponds to

the classifier testing. Because of the limited number

of subjects, performance shown here may overstate

expected performance in an operational setting.


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Further experimentation is needed to address large-

scale performance. The results presented here show

good performance on data from the initial experiment

(figure 5) and some loss in performance with the

clinical instrument (figure 6).

Figure 4. Examples of Problem and Good Data. The upper graphs show the ECG traces, while the lower

ones depict the waterfall diagram in which the heartbeats are aligned according to the R peaks.

Figure 5. Classification Results from the First Data Collection

Problem Data Good Data


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Classification performance depends on the

variability across subjects compared to the within

subject variation. Within subject variation can arise

from several sources. Changes in the subjects

mental and emotional state are critical, since heart

rate responds to a persons level of excitement. In

addition, variation across sessions, including both

changes related to sensor placement and slowly

varying changes in physiological characteristics, must

be understood. To provide a useful biometric for

human identification, the signature should be unique

to the individual, while the variation attributable to

either mental state or session-to-session changes must

be small.

The data from the first collection was

analyzed in several ways to explore these issues. In

the first set of experiments, training data consisted of

one 20-second segment within a single session and

task. The testing data was the remaining 100 seconds

of data from the same session and task. These sevenexperiments (one for each task) are labeled intra

task in figure 5. The first and second bars indicate

the percent of heartbeats correctly classified in the

training and testing data, respectively. The third bar

shows the percent of subject classified correctly based

on the voting analysis of the heartbeat data. The

second set of bars, labeled task 1, task 2, etc.,

correspond to training on a 20-second segment for

one task and testing on all other tasks. The label

identifies that task used for training. The final set of

bars indicates performance when the classifier is

trained on data from one session and tested against

data for a different session.

Because the second data collection

employed a simplified protocol, the classification

analysis spans fewer training and testing conditions.

Task 1 represents baseline conditions, while task 2

was the arithmetic task designed to induce stress. As

with the first data collection, the results include

training and testing within a task, training on one task

and testing on another, and training on one session

and testing on a separate session (figure 6). In

general, performance was slightly lower on this data

set than on the first one. Two factors account for the

difference. The reduced temporal sampling (256 Hz

vs. 1,000 Hz) introduces a loss in precision of the

location of the fiducial pints that define the features

used in classification. In addition, less rigorous lab

procedures resulted in higher noise arising from

sensor placement. Subsequent assessment of the

procedures have verified that this noise source can be

eliminated through improved procedures.

The critical issue for an operational

biometrics is performance across sessions. Given

data from one session (the enrollment data), can new

data be acquired in a subsequent session days or

weeks later that provide an accurate identification of

the individual. To address this question, data from

the two collections were augmented by clinical ECG

data from another study. The classification

performance on this pooled data set shows about 90%

correct classification of individuals using the methods

described above (table 1).

The classification analysis exhibits good

performance when training the classifier on data from

one task and testing on data from another task. In

general, the shape of the heartbeat is stable across

tasks, but differs across subject. There can be some

stretching or contracting of the overall heartbeat as

the subjects rate varies, but the normalization of the

extracted features compensates for this source of

variation. The average heartbeat within a single taskshows relatively little task-to-task variation (figure 7).

The differences across subjects, however, are clearly

evident. The stability of the features across tasks

suggests that identification based on cardiovascular

function should be robust. Analysis of the specific

features reinforces this idea.

Figure 6. Classification Performance for the

Second Data Collection


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Table 1. Combined Performance Results

Experiment Training

Subjects

Test

Subjects

% Training

Heartbeats

% Test

Heartbeats

% Identified

Train on first half of data 104 104 56 58 91

Train Session 1 95 55 64 67 88Train Session 2 59 56 73 62 88

Each block of curves

represents the average

heartbeat for one

individual for each of

the 7 tasks, i.e., 7curves per subject.

Each block of curves

represents the average

heartbeat for one

individual for each of

the 7 tasks, i.e., 7curves per subject.

Figure 7. Mean Heartbeat Within a Subject and Task, for Several Subjects and Seven Tasks from the First

Analysis of Features

To insure good performance as a biometric,

the underlying features extracted from the ECG signalshould be stable across mental and emotional state,

stable across session, but show good variability

across subjects. A multivariate ANOVA was

performed to assess these sources of variance. The

contribution of each factor task, session, and subject

was estimated for each of the features used in the

classification analysis. Figure 8 shows the relative

contribution for each source of variation, with the

bars summing to 100% for each feature. It is clear

that features are stable across tasks, although certain

features show higher variation across sessions than

would be ideal. Further investigation is underway to

minimize the effects of session-to-session variationon classification performance.

A more challenging problem, however,

arises from the detailed analysis of the feature space.

When viewed marginally (figure 9) and jointly (figure

10), the distribution of the feature values within an

individual is sometime multi-modal. This suggests

that the classes (subjects) may not be linearly

separable and the linear discriminant analysis

presented here is not the best approach. Classifiers

that can handle disjoint sets are expected to provide

better classification performance. Initial

investigations of a neural network approach showssubstantial promise and we hope to report complete

results in the near future.

Figure 8. Relative Contributions to Overall

Variance for Each ECG Feature

Discussion

The analysis presented here indicates that

human identification based on cardiovascular


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function is feasible. By measuring a subjects ECG

and extracting features from the signal, it is possible

to classify individuals with high accuracy. The

features used for classification are stable across a

range of mental and emotional tasks, indicating that

this biometric should be robust to an individuals

current mood. The limited data from multiple

sessions also shows good behavior. Analysis of the

feature space suggests that alternative classifiers

merit consideration and these investigations are

currently underway.

References

D. Dubin, (2002) Rapid interpretation of ECGs,

Cover, Inc., Tampa, Florida, 2000.

D. P. Golden Jr, R. A. Wolthuis and G. W. Hoffler,

(1973) A spectral analysis of the normal resting

electrocardiogram, IEEE Transactions on Biomedical

Engineering, BME 20 (September) (1973) 366-373.

L. Biel, O. Pettersson, L. Philipson and P. Wide,

(2001) ECG analysis: A new approach in human

identification, IEEE Transactions on Instrumentation

and Measurement, 50 (3) (2001) 808-812.

R. Hoekema, G. J. H. Uijen and A. van Oosterom,

(2001) Geometrical aspect of the interindividual

variability of multilead ECG recordings, IEEE

Transactions on Biomedical Engineering, 48 (2001)

551-559.

J. M. Irvine, B. K. Wiederhold, L. W. Gavshon, S. A.

Israel, S. B. McGehee, R. Meyer and M. D.

Wiederhold, (2001) Heart rate variability: A new

biometric for human identification, International

Conference on Artificial Intelligence (IC-AI'2001),

Las Vegas, Nevada, 2001, pp. 1106-1111.

D. P. Jang, S. A. Israel, B. K. Wiederhold, M. D.

Wiederhold, S. B. McGehee, L. W. Gavshon, R.

Meyer and J. M. Irvine, (2001) Protocols for

protecting patient information within a biometric

analysis, Biometrics Section of the International

Conference on Information Security, Seoul, Korea,2001, pp.

Figure 9. Marginal Distribution for One Feature (LT) for Two Subjects

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Figure 10. Joint Distribution of Two Features (RL

and RP) for Several Subjects.

Acknowledgements

This research was supported by the DARPA

Human Identification program under contract

DABT63-00-C-1039. Additional assistance was

provided by Dr. Rodney Meyer, Dr. Lauren Gavshon,

Ms. Shannon McGee, and Ms. Elizabeth Rosenfeld.

The authors also wish to thank Dr. P. Jonathon

Phillips, DARPA, for valuable comments concerning

the development of this work. The views expressed

here are those of the authors and do not necessarily

reflect the positions of DARPA, SAIC, or the Virtual

Reality Medical Center.

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