Palm-based ECG biometrics
André Lourenço1

1

Instituto Superior de Engenharia de Lisboa(ISEL)
Instituto de Telecomunicações(IT)Instituto Superior Técnico(IST)
Lisbon, Portugal

2

IT-IST
Lisbon, Portugal

alourenco@deetc.isel.ipl.pt

Ana Fred2
afred@lx.it.pt

Hugo Silva2
hsilva@lx.it.pt

Abstract
We propose an experimental apparatus for ECG biometrics, that matches
the usability and intrusiveness levels of traditional biometric traits likefingerprint or hand geometry centered on subject’s hand or fingers, andthat require some degree of contact of proximity to the sensoring device. A hardware device was developed, comprised of a surface withintegrated electrodes and signal conditioning circuitry, where the subject
rests his/her hand palms, allowing the signal acquisition without the needfor conductive gel nor access to more intimate parts of the body. Preliminary results show that this setup led to competitive results matchingrecognition rate of ECG signals acquired at the fingers.

1Introduction

Electrocardiographic (ECG) signals are a recent trend in biometric recognition. It has very appealing characteristics, as intrinsic liveliness detection, and not depending on external physical landmarks, therefore being
more difficult to spoof.
Traditional biometric traits like fingerprint or hand geometry, are already centered on subject’s hand or fingers. For fingerprint, the subject
needs to place or scroll the finger in the reader, and for hand geometry the
subject needs to place the hand on the reader. The usage of hand/finger isthus an already familiar procedure for the user.
In the pioneer work by Biel et al. [1] a 12-lead setup was used to acquire the ECG signal mounted on the chest and limbs with pre-gelled electrodes or conductive paste to improve conductivity with the skin. Morerecently [4, 6, 7], it was shown that a 1-lead setup suffices. The sensing apparatus has also evolved, changing from acquisitions performed onchest with pre-gelled electrodes, to a non-intrusive and more practical acquisition at the fingers [4].
In this work we further explore the experimental apparatus presented
in [4], proposing a new acquisition setup, which reduces the number ofcontacts required for the acquisition of the ECG to only two measurement
leads. This is acomplished based on a new single differential sensor design [8] that does not require the traditional ground lead. The proposed
setup enables the acquisition of ECG using index and middle fingers, andusing hand palms. This setup is substantially more comfortable than previous ones, enabling people to use it in their daily routines.

2

Figure 1: Experimental apparatus: at the top Electrolycras enable the acquisition of ECG using the index and middle fingers; at the bottom dry
Ag/AgCl electrodes acquire ECG at the hand palms.

As scenario of application consider the adaptation of the setup on akeyboard, allowing that the ECG is being continuously acquired at thehand palms, while the user is typing, as illustrated in figure 2. We willshow that this setup can lead to high performance rates, and thus that it
can be used to improve protection in high security applications. Moreover, instead of regular electrodes, dry Ag/AgCl electrodes are used on
the acquisition, further improving the usability.

(a) Setup adapted to Keyboard(b) User typing

Figure 2: Example of adaptation of the purposed setup for the context ofcontinuous biometrics based on the ECG acquired at hand palms.

Proposed Approach

We propose an experimental apparatus for ECG biometrics, that matches
the usability and intrusiveness levels of conventional biometric systems.
Our device is comprised of a surface with integrated electrodes and signal
conditioning circuitry, where the subject rests his/her hand palms, allowing the signal acquisition without the need for conductive gel nor accessto more intimate parts of the body.
The system can either be used with Ag/AgCl electrodes or Electrolycras as interface between the sensor and the skin for improved usability.
Furthermore, a custom signal conditioning circuit was developed, which
only requires two contact points with the skin, through the use of a virtualground. This design, improves upon prior art work, as traditional ECG
sensors require positive (+) and negative (-) poles, together with a ground(GND) lead. With our design, only the (+) and (-) leads are required.
Figure 1, depicts the proposed setup. In this configuration, and for experimental validation purposes, the device allows the acquisition of ECG
signals at the hand palm through dry Ag/AgCl electrodes, and at the index
and middle fingers through Electrolycras.

3

Experimental Evaluation

To evaluate the experimental setup, we performed an extensive data collection in 32 subjects (25 males and 7 females) with an average age of
31.1±9.46 years. Subjects were only asked to rest their left/right hands
as indicated in the device.
Two custom ECG sensors [8] were used for signal acquisition, withtotal gain of 1000 and analog band pass filtering between the 1-30Hz
range. In Figure 1 both sensors are visible, one connected to the Ag/AgClelectrodes, and another connected to the Electrolycras strips placed at thefinger level.
To guarantee electrical insulation of the sensors, two independent
biosignal acquisition units were used, one per sensor. We recurred to thebioPLUX research system [5], which enables Bluetooth wireless transmission of the collected signals to the base station. Synchronization ofthe acquisition units was performed optically using a syncPLUX kit and alight-dependent resistor (LDR). To one of the systems a triggering switch
was connected, which simultaneously activated the digital input port ofthe system and an light-emitting diode (LED). To the other system, aLDR was connected to one of the analog input channels. This allowed
us to have the data collected by each system synchronized, without recurring to any electrical connection between them. Signals were acquired
during a period of approximately 2 minutes, during which the supervisor
would describe the experiment and related work.
The processing of this signal consisted in a filtering step using a bandpass with 1-30Hz frequency span and passed a segmentation step using aQRS-complex detection algorithm based on the commonly used Engleseand Zeelenberg algorithm [2]. Heartbeat waveforms are then segmented
and the mean waves computed in order to obtain a representative wave
template pattern of the subjects ECG. We evaluate the performance of thesystem using a varying the number of segments, m, used to compute themean wave.
For evaluation purpose, a 30-fold cross validation was implemented;
runs with two randomly selected exclusive datasets were created with thetraining set containing 30% of the total collected segments as the templates database, and a test set with 70% of the remaining segments.

illustrates the false acceptance rate (FAR), and false rejection rate (FRR),
when using m = 5, which are obtained using different operating pointsof the classifier. The EER corresponds to the intersection of those twocurves. The receiver operating characteristic (ROC curve) is presented inFigure 3(c).
These obtained results are within the confidence intervals of otherstudies in literature [3, 4, 6, 7]. In authetication scenarios, using chest andfinger signals, results are in the order of ∼ [2 − 5]%.

4Conclusions

The field of application of electrocardiographic signals is expanding to
new areas which far extend the medical and quality of life applications towhich it is typically associated with. Biometrics is currently emerging as
one of these novel application fields.
Within the scope of biometric recognition, conventional acquisition
apparatuses have specificities which limit the acceptability by the potential end-users. This arises from the fact that, in general, devices requirepre-gelled electrodes to acquire the signals, but more importantly, becausethey need to be applied to the subjects body.
In this paper we presented an experimental setup, proposing ECG acquisition at the hand palms. Furthermore, our approach recurs to a custom
two-lead ECG sensor, that can use either dry Ag/AgCl electrodes or Electrolycras as interface with the skin. For signal acquisition, the user only
needs to rest his/her hand over the sensors without any other constraint.
Experimental results have revealed that the collected signals provide
adequate informative content reaching a mean EER of 2.75%±0.29 when
averages of 5 heartbeat waveforms are used, which constitutes a promising alternative for conventional biometric systems.

ACKNOWLEDGEMENTS(a) EERThis work was partially funded by Fundação para a Ciência e Tecnologia(FCT) under grants SFRH/BD/65248/2009 and SFRH/PROTEC/49512/2009
and Departamento de Engenharia de Electrónica e Telecomunicações e deComputadores, Instituto Superior de Engenharia de Lisboa, whose support the authors gratefully acknowledge.

References[1] L. Biel, O. Pettersson, L. Philipson, and P. Wide. Ecg analysis: anew approach in human identification. In Instrumentation and Measurement Technology Conference, 1999. IMTC/99. Proceedings of the
16th IEEE, volume 1, pages 557 –561 vol.1, 1999.
[2] W. A. H. Engelse and C Zeelenberg. A single scan algorithm for QRS(b) FAR-FRR
detection and feature extraction. IEEE Comp in Card, pages 37–42,
1979.
[3] S. Israel, J. Irvine, A. Cheng, M. Wiederhold, and B. Wiederhold.
ECG to identify individuals. Pattern Recognition, 38(1):133–142,
2005.
[4] A. Lourenço, H. Silva, and A. Fred. Unveiling the biometric potential of Finger-Based ECG signals. Computational Intelligence andNeuroscience, 2011, 2011.
[5] PLUX. bioplux research. http://www.plux.info/biopluxresearch.
[6] T. Shen, W. Tompkins, and Y. Hu. One-lead QRS for identity verification. In Proceedings of the Second Joint EMBS/BMES Conference,pages 62–63, 2002.
[7] H Silva, H Gamboa, and A Fred. One lead ECG based personal
identification with feature subspace ensembles. In 5th International(c) ROC
Conference on Machine Learning and Data Mining, pages 770–783,
Figure 3: Equal Error Rate (EER) as a function of m, the number of seg2007.
ments used to compute the mean; FAR and FRR curves and ROC curve [8] H. Silva, A. Lourenço, R. L. Lourenço, P. L. Leite, D. Coutinho, andin an authentication scenario (for m=5).
A. Fred. "study and evaluation of a single differential sensor design
based on electro-textile electrodes for ecg biometrics applications".
In Proc IEEE Sensors, Limerick, Ireland, October 2011. IEEE.
Figure 3(a) shows the Equal Error Rate (EER) for authentication varying the number of segments used to compute the mean wave: if m = 1, one
individual heartbeat waveforms is used, a mean EER of 9.39% ± 0.19 isattained, which decreases to 2.75% ± 0.29 when m = 5. These correspondrespectively to approximately 1s and 5s of acquired signals. Figure 3(b)