T. Lugovaya
Department of Applied Mathematics and Computer Science, Electrotechnical University "LETI", Saint-Petersburg, Russian Federation

This material originally appeared in

Lugovaya T. Biometric human identification based on ECG. [Master's thesis] Dept of Applied Mathematics and Computer Science, Electrotechnical University "LETI", St Petersburg, Russian Federation; 2005.

Please cite this publication when referencing this material.


This research investigates the feasibility of using the electrocardiogram (ECG) as a new biometric for human identification. It is well known that the shapes of the ECG waveforms depend on human heart anatomic features and are different for different persons. But it is unclear whether such differences can be used to identify different individuals. This research demonstrates that it is possible to identify a specific person in a predetermined group using a one-lead ECG. A one-lead ECG is a one-dimensional, low-frequency signal that can be recorded from electrodes on the hands. ECG fragments containing QRS complexes, P and T waves extracted from the ECG are processed by principal component analysis and classified using linear discriminant analysis. Using this method on a predetermined group of 90 subjects, the experimental results showed that the rate of correct identification was 96%.


Biometric technologies are among fast-developing fields of information security, gradually entering into all spheres of human activity. Today only three biometric methods have proved their efficiency, namely, identification based on fingerprints, iris or retina, and face. Hand geometry, voice, writing and typing dynamics, etc. are also useful, depending on the purpose and range of application.

This research aims to develop an identification system based on ECG (figure 1). ECG is assumed as an almost unique human characteristic because morphology and amplitudes of registered cardiac complexes are governed by multiple individual factors, in particular, by formation and position of the heart, presence and nature of pathologies, etc.

[example of ECG]

Figure 1. Example of ECG with standard notations.

Identification system

The system feasibility was discussed in [1, 2]. This study suggests other data interpretation and classification techniques, with the system tested on a higher level of live input data. The respective findings are compared below (table 2).

The identification system uses a classical scheme including data preprocessing, formation of input data space, transition to reduced feature space, ECG cycle classification and ECG record identification.

For usability, it is necessary to be able to collect the ECG easily and quickly. The data collected for this study comprise the ECG-ID Database, consisting of 320 single-lead ECG recordings from 90 subjects, each 20 seconds long, sampled at 500 Hz with 12-bit precision. Since single-lead ECGs vary significantly within an individual depending on the lead (the locations of the electrodes used to observe the ECG), the choice of lead is important. Lead I is the potential difference between the left and right hands (LA - RA). It was chosen because it is easily measured and it is not sensitive to minor variations in electrode locations.

Data preprocessing

The raw ECG is often rather noisy and contains distortions of various origins. Both high and low frequency noise components are present (figure 3).

[power-line noise]
A. ECG with power-line noise
[high-frequency noise]
B. ECG with high-frequency noise
[power-line and high-frequency noise]
C. ECG with both power-line and high-frequency noise
[isoline drift]
D. ECG with baseline drift
Figure 2. Examples of noisy ECGs.

Frequency-selective signal filtering was implemented using a set of adaptive bandstop and low-pass filters (figure 3).

[signal filtering 1] [signal filtering 2]
Figure 3. Examples of frequency-selective signal filtering results.

Baseline drift correction was implemented using multilevel one-dimensional wavelet analysis. The original signal was decomposed at level 9 using biorthogonal wavelets. The signal reconstructed using final approximation coefficients is assumed to be the drifting baseline, which is subtracted from the original signal (figure 4). This method shows good results in both cases of clear and rather noisy ECG signals.

[wavelet drift correction] [wavelet drift correction noisy]
Figure 4. Examples of baseline drift correction results.

Initial feature space

This section focuses on initial feature space formation. Obviously information on cardiac performance is basically held in the cardiac cycle fragment containing the QRS complex and P- and T-waves (referred to here as the PQRST-fragment). Therefore the process begins with extraction of a set of R-peak synchronized PQRST-fragments (figure 5). The PQRST-fragment length is fixed at 0.5 sec (250 samples).

[PQRST-fragment extraction] [PQRST-fragment extraction]
Figure 5. R-peak detection and PQRST-fragment extraction.

PQRST fragments are used as informative features. Therefore extracted PQRST fragments are processed to enhance their similarity as follows:
1. Correcting PQRST fragment mutual "vertical" shift due to residual baseline drift:
[vertical shift correction]
2. Culling distorted (due to breathing or motion artifacts) and pathological PQRST-fragments:
[atypical PQRST-fragments]
3. Correcting PQRST-fragments depending on heart rate using Bazett's formula:
[heart rate influence] [heart rate correction]

Thus in the initial feature space (dimension N=250) the ECG appears as a set of PQRST-fragments with each seen as a separate pattern at subsequent system stages, to be interpreted and classified independently.

Feature space reduction

The feature space is reduced using Principal Component Analysis (PCA) so that its dimension can be reduced to 30 (with the Kaiser criterion) or even to 10 (with the scree test).

Alternately, the feature space can be reduced with a wavelet transform (WT) providing the same space reduction but with slightly poorer final identification.

Classification and Identification

The resulting PQRST-fragment patterns are then classified in reduced feature space using linear discriminant analysis.

At the final stage, the ECG record identification is based on PQRST-fragment classification results.


Experimental studies involved 90 human volunteers. ECG records were made in the sitting position. Heart rate and physical and emotional state were not limited. The collected data set contains 320 records, of which 200 records were assigned to the training set and 120 records to the test set. Differentiation between the training and test sets aimed to provide for maximum performance complexity, i.e., maximum difference between records in different sets both in monitoring time and human physical state.

Averaged results of series of experiments on PQRST-fragment classification and ECG record identification with different feature space reduction methods are tabulated in Table 1. As shown there, the best results were obtained using 30 principal components, yielding 96% correct ECG identification in the test set.

Reduction technique Number of features PQRST-fragments classification, % ECG identification, %
Training set Test set Test set
PCA 10 99 85 89
WT 9 98 79 82
PCA 30 99 91 96
WT 34 99 88 91
Table 1. Experimental results.

Additionally, results from [1, 2] and this research are compared in Table 2.

Research Class count Identification results, %
[1] 20 98
[2] 9 95
this 90 96
Table 2. Results comparison.

As a result of this research a recognition system was developed to solve the problem of biometric human identification based on ECG on a sufficiently large set of input data. The findings represent primary arguments for using ECG as a biometric characteristic in various biometric access control problems, opening up a brand new perspective for the study of biometric technologies, with potential applications in security and modern amenity systems.


[1] Biel L, Pettersson O, Philipson L, Wide P. ECG analysis: a new approach in human identification. IEEE Transactions on Instrumentation and Measurement 2001 June; 50(3):808-812.

[2] Yi WJ, Park KS, Jeong DU. Personal identification from ECG measured without body surface electrodes using probabilistic neural networks. Proc 2003 World Congress on Medical Physics and Biomedical Engineering, Sydney, Australia, 2003 August.