=Paper=
{{Paper
|id=Vol-2098/paper5
|storemode=property
|title=Optimizing Factors Influencing on Accuracy of
Biometrical Cardiometry
|pdfUrl=https://ceur-ws.org/Vol-2098/paper5.pdf
|volume=Vol-2098
|authors=Marat R. Bogdanov,Aleksander A. Dumchikov,Vadim M. Kartak,Aigul I. Fabarisova
}}
==Optimizing Factors Influencing on Accuracy of
Biometrical Cardiometry==
https://ceur-ws.org/Vol-2098/paper5.pdf
Optimizing Factors Influencing on Accuracy of
Biometrical Cardiometry
Marat R. Bogdanov1,2 , Aleksander A. Dumchikov2 , Vadim M. Kartak1,2 , and
Aigul I. Fabarisova2
1
Ufa State Aviation Technical University, Ufa, Russia
redfoxufa@gmail.com
2
M. Akmullah named after Bashkir State Pedagogical University, Ufa, Russia
kvmail@mail.ru
Abstract. The paper is about some aspects concerning person biomet-
ric identification based on using of electrocardiograms. Signal prepro-
cessing routing is considered in the paper. Classification was carried out
with support vector machines algorithm. Tuning of hyper parameters of
classification is considering.
Keywords: Biometric person identification · Electrocardiogram · Ma-
chine learning · Support vector machines · Hyper parameter tuning
1 Introduction
Various biometric methods of person identification are getting more popular.
Fingerprinting, face, voice and retina recognition are widely used in various secu-
rity systems. The vulnerabilities of traditional methods of biometric identifica-
tion were revealed over time. Researchers are increasingly turning their attention
to such person biometric features as electrocardiograms, electroencephalograms
and DNA [1]. In this paper, we would like to discuss some practical aspects of
person identification using ECG.
2 Motivation and Aim
The problem of person biometric identification concerns classification prob-
lems. To solve it, we have to consider algorithms from some finite set and choose
an algorithm that gives the least error of the forecast [3]. Let’s introduce some
notation.
Let us suppose X is a space of objects.
Copyright c by the paper’s authors. Copying permitted for private and academic purposes.
In: S. Belim et al. (eds.): OPTA-SCL 2018, Omsk, Russia, published at http://ceur-ws.org
62 M. R. Bogdanov et al.
Y is a set of answers.
X l = (xi , yi )li=1 (1)
is a training set, l is a sample size.
yi = y ∗ (xi ), (2)
At = {a : X → Y } (3)
are a models of algorithms, t ∈ T , T is a number of algorithms under consider-
ation.
µt : (X × Y )l → At (4)
are learning methods. It is required to find a method µt with the best generalizing
power.
When finding a method µt , we often have to solve the following subtasks:
– Choice of the best model At (model selection).
– Choice of learning method µt for a given model At (in particular, optimiza-
tion of hyperparameters).
– Features selection:
F = {fj : X → Dj : j = 1, ..., n} (5)
is a set of features. The method of learning µj uses only features J ⊆ F .
To assessment the quality of learning by precedents it’s used:
L(a, x) is a cost function of algorithm a on the object x.
l
1X
Q(a, X l ) = L(a, xi ) (6)
l i=1
is a functional of accuracy a on X. In this case we consider an internal quality
criterion that is measured on the training set X l :
Qµ(X l ) = Q(µ(X l ), X l ) (7)
and an external criterion evaluating the quality of learning on hold-out set X k [2]:
Qµ(X l , X k ) = Q(µ(X l ), X k ). (8)
In the paper presented we will consider such aspects of person biometric iden-
tification as feature selection, model selection, choice of methods (tuning of hy-
perparameters), assessment of the quality of learning.
3 Feature Selection
We used the MGH/MF Waveform Database hosted at physionet.org re-
source [8], [2]. The Massachusetts General Hospital/Marquette Foundation
(MGH/MF) Waveform Database is a comprehensive collection of electronic
Biometrical Cardiometry 63
recordings of hemodynamic and electrocardiographic waveforms of stable and
unstable patients in critical care units, operating rooms, and cardiac catheteri-
zation laboratories. It is the result of a collaboration between physicians, biomed-
ical engineers and nurses at the Massachusetts General Hospital. The database
consists of recordings from 250 patients and represents a broad spectrum of phys-
iologic and pathophysiologic states. Individual recordings vary in length from 12
to 86 minutes, and in most cases are about an hour long [8], [2].
The typical recording includes three ECG leads, arterial pressure, pulmonary
arterial pressure, central venous pressure, respiratory impedance, and airway
CO2 waveforms. The raw sampling rate of 1440 samples per second per signal
was reduced by a factor of two to yield an effective rate of 360 samples per
second per signal relative to real time [8], [2].
When preprocessing stage we used a biopsy python library by John Reid. The
package enables the development of Pattern Recognition and Machine Learning
work flows for the analysis of biosignals including ECG [5]. Using biopsy we
extracted first lead from electrocardiogram and performed a low pass filter for
reducing of redundancy. After applying of low-pass filter R-peaks was extracted
from ECG-signal using a wfdb python library by Chen Xie and Julien Dubiel [6].
The software allow extract peaks and QRS -cycles from electrocardiograms. We
choice amplitude and temporal features of Q,R and S -peaks ( Qx , Qy , Rx , Ry ,
Sx , Sy ). In total we had 6 features. Feature table together label class vector
were randomly splitted into training set and testing set in the ratio of 75:25 for
further cross validation. We learned a classifier on training set and performed
measuring of classifying accuracy on testing set.
4 Model Selection
We used a Support Vector Machines (SVM) algorithm for classification. Sup-
port Vector Machines are based on the concept of decision planes that define
decision boundaries. A decision plane is one that separates between a set of ob-
jects having different class memberships. SVM is primarily a classier method that
performs classification tasks by constructing hyperplanes in a multidimensional
space that separates cases of different class labels. SVM supports both regres-
sion and classification tasks and can handle multiple continuous and categorical
variables [4].
To construct an optimal hyperplane, SVM employs an iterative training al-
gorithm, which is used to minimize an error function. According to the form of
the error function, SVM models can be classified into four distinct groups:
– Classification SVM Type 1 (also known as C-SVM classification)
– Classification SVM Type 2 (also known as nu-SVM classification)
– Regression SVM Type 1 (also known as epsilon-SVM regression)
– Regression SVM Type 2 (also known as nu-SVM regression)
We used a Classification SVM Type 1 (also known as C-SVM classification)
model.
64 M. R. Bogdanov et al.
5 Classification SVM Type 1
For this type of SVM, training involves the minimization of the error function:
N
1 T X
w w+C ξi (9)
2 i=1
subject to the constraints:
yi (wT φ(xi ) + b) ≥ 1 − ξi ≥ 0, i = 1, ..., N, (10)
where C is the capacity constant, w is the vector of coefficients, b is a constant,
and ξi represents parameters for handling nonseparable data (inputs). The index
i labels the N training cases. Note that y ∈ ±1 represents the class labels and
xi represents the independent variables. The kernel φ is used to transform data
from the input (independent) to the feature space. It should be noted that the
larger the C, the more the error is penalized. Thus, C should be chosen with
care to avoid overfitting.
6 Kernel Functions
Xi · Xj Linear
(γXi · Xj + C)d P olynomial
K(Xi , Xj ) = 2 , (11)
exp(−γ|Xi − Xj | ) RBF
tanh(γXi · Xj + C) Sigmoid
where K(Xi , Xj ) = φ(Xi ) · φ(Xj ) that is, the kernel function, represents a
dot product of input data points mapped into the higher dimensional feature
space by transformation φ.
7 Gamma is an Adjustable Parameter of Certain Kernel
Functions
The RBF is by far the most popular choice of kernel types used in Support
Vector Machines. This is mainly because of their localized and finite responses
across the entire range of the real x-axis [7].
Support vector machine classifier supported by sklearn python library uses as
default following hyper parameters: C=1.0, kernel=’rbf’, gamma=’auto’. When
using of default parameters while performing of classification of electrocardio-
grams we had accuracy score equal to 0.93. We tuned hyper parameters of clas-
sification with Grid Search procedure varying C parameter in range of [1, 10,
100, 1000], kernel in range of [’linear, ’rbf”], and gamma in range of [1e-3, 1e-4].
After performing of tuning we had the following best parameters set: ’kernel’:
’rbf’, ’C’: 10, ’gamma’: 0.001. Using these parameters we had accuracy score
equal to 0.99.
Biometrical Cardiometry 65
8 Results and Discussion
During the preprocessing of electrocardiograms we extracted first leads of
signal and performed low-pass filter for reducing redundancy. Then we extracted
cardiac cycles from the leads and extracted Q, R and S peaks from cardiac cycles.
Using amplitude and temporal features of peaks we composed a feature table
containing 6 features (Qx , Qy , Rx , Ry , Sx , Sy ) and class labels vector y. Further
we randomly splitted a feature table and class labels vector into training set
and testing set on ration of 75:25 for further cross-validation. Training set was
used for learning a classifier and testing set was used for assessment of quality
of learning. SVM classifier supported by sklearn python library using default
options show accuracy score equal to 0.93. We found the best hyper parameters
are following: ’kernel’: ’rbf’, ’C’: 10, ’gamma’: 0.001. Using these parameters we
could improved accuracy score up to 0.99.
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