09-09-2016, 02:56 PM
1454159158-18EANALYSIS.pdf (Size: 1.05 MB / Downloads: 10)
ABSTRACT: Phonocardiogram (PCG) signals as a biometric is a new and novel method for user identification. This
paper examines the applicability of the biometric properties of PCG signals, which can thus be included among the
physiological signs used by an automatic identification system. Use of PCG signals for user recognition is a highly
reliable method because heart sounds are produced by internal organs and cannot be forged easily as compared to other
recognition systems. Mel frequency Cepstral Coefficients {MFCCs} has been used for feature extraction and then these
feature vectors are classified to recognize a person, using Support Vector Machine (SVM) as classifier. The
performance of SVM for linear kernel function was analyzed and discussed as well.
I.INTRODUCTION
In recent years, it has become very important to identify a user in applications such as personnel security, defence,
finance, airport, hospital and many other important areas [8]. So, it has become mandatory to use a reliable and robust
authentication and identification system to identify a user. Infact, performance-based biometric systems where by a
person is automatically recognized by him performing a pre-defined task using his own biometrics, are preferred over
knowledge-based (e.g., password) or possession-based (e.g., key) access control methods. As a result, conventional
biometrics systems like fingerprint, iris, face and voice that provide recognition based on an individual behavioural
and/or physiological characteristics are becoming more popular [1,7,8,13]. Human heart sounds are natural signals,
which have been applied in the doctor’s auscultation for health monitoring and diagnosis for thousands of years. In the
past, study of heart sounds focus mainly on the heart rate variability. In the last 4 years, many researchers have
investigated the possibility of using heart sounds as physiological traits for biometric recognition [2, 4, 5, 6, 9, 15, 16].
The human PCG-based biometric offers several desirable properties. First, the PCG is not strongly permanent and are
difficult to forge and therefore reduces falsification. Moreover, heart sounds are relatively easy to obtain and also
inherently provides assurance of subject liveness.
Phonocardiography is the sound or vibration of the heart when it pumps blood. In this paper, we use the heart sounds
(PCG signals) as a biometric for user identification. Use of phonocardiogram signals has many advantages over other
biometrics based on the following properties of heart sounds [12]:
1. Universal: Each living human being has a pumping heart.
2. Measurable: PCG signals can be digitally captured and measured using an electronic stethoscope.
3. Vulnerability: Unlike other biometric technologies, heart sounds cannot be copied or reproduced easily as it is based
on intrinsic dynamic signals acquired from the body. Heart sounds cannot be taken without the consent of the person.
Moreover, to reproduce the heart sounds, an anatomy of heart as well its surroundings has to be created as heart sounds
depends on the anatomy of the body.
4. Uniqueness: Heart sounds depend on the physical state of an individual's health, age, size, weight, height, structure
of the heart as well as the genetic factors. The heart sounds of two persons having the same type of heart diseases also
vary.
5. Simplicity: Moreover, heart sounds are easy to obtain, by placing a stethoscope on the chest.
The main advantages of heart sounds are, so far, the High Universality and the Low Circumvention. The first point is
undeniable and objectively true. If our body does not produce the heart sound, it means that we are not alive and so any
task of authentication or live verification would be possible. This property is shared with all the biometric traits that
depend on organs whose functioning is critical for our life, like the brain. The main drawbacks of heart-sounds
biometry are probably the Low Performance and, above all, its overall immaturity as a biometric trait. Of course, heartsounds
biometry is a new technique, and as such many of its current drawbacks will probably be addressed and
resolved in future research work.
II. REVIEW OF RELATED WORKS
In the last years, different research groups have been studying the possibility of using heart sounds for biometric
recognition. In this section, we will briefly describe their methods.
[12] was one of the first works in the field of heart-sounds biometry. In this paper, the authors obtained good
recognition performance using the HTK Speech Recognition toolkit, investigating the performance of the system using
different feature extraction algorithms (MFCC, LFBC), different classification schemes (Vector Quantization (VQ) and
Gaussian Mixture Models (GMM)) and investigating the impact of the frame size and of the training/test length. After
testing many combinations of those parameters, they conclude that, on their database, the most performing system is
composed of LFBC features (60 cepstra + log energy + 256ms frames with no overlap), GMM-4 classification, 30s of
training/ test length. The authors of [2], one of which worked on [12], take the idea of finding a good and representative
feature set for heart sounds even further, exploring 7 sets of features: temporal shape, spectral shape, cepstral
coefficients, harmonic features, rhythmic features, cardiac features and the GMM supervector. They then feed all those
features to a feature selection method called RFE-SVM and use two feature selection strategies (optimal and suboptimal)
to find the best set of features among the ones they considered. The results was expressed in terms of Equal
Error Rate (EER), are better for the automatically selected feature sets with respect to the EERs computed over each
individual feature set. In [9], the authors describe an experimental system where the signal is first downsampled and is
processed using the Discrete Wavelet Transform, using the Daubechies-6 wavelet, and the D4 and D5 sub-bands (34 to
138 Hz) are then selected for further processing. After normalization and framing step, the authors then extract some
energy parameters from the signal, and they find that the Shannon energy envelogram is the feature that gives the best
performance. The authors of [16] investigate the usage of both the ECG and PCG for biometric recognition but we will
focus only on the part of their work that is related to PCG. The heart sounds are processed using the Daubechies-5
wavelet, up to the 5th scale, and retaining only coefficients from the 3rd, 4th and 5th scales. They then use two energy
thresholds (low and high), to select which coefficients should be used for further stages. The remaining frames are then
processed using the Short-Term Fourier Transform (STFT), the Mel-Frequency filterbank and Linear Discriminant
Analysis (LDA) for dimensionality reduction. The decision is made using the Euclidean distance from the feature
vector obtained in this way and the template stored in the database. They test the PCG-based system on a database of
21 people and their combined PCG-ECG systems have better performance. The authors of [15] filter the signal using
the DWT; then they extract different features: auto-correlation, cross-correlation and cepstra. They then test the
identities of people in their database using two classifiers: Mean Square Error (MSE) and k-Nearest Neighbor (kNN).
On their database, the kNN classifier performs better than the MSE one. In the march of the PCG recognition’s
progress, the proposed methodology for PCG recognition in thi paper is presented in the next section.
III. PHYSIOLOGY OF THE HEART SOUND
The human heart is a pump of four-chamber, the two upper chambers called atria are for the collection of blood from
the veins and two lower chambers called ventricles are for pumping out the blood to the arteries,
This two sets of valves control the blood flow: the AV-valves (mitral and tricuspid) between the atria and the
ventricles, and the semi lunar valves (aortic and pulmonary) between the ventricles and the arteries from the heart
.These valves periodically close and open to permit blood flow in only one direction. The mechanical activity of the
heart including the blood flow, vibrations of the chamber walls and opening and closing of the valves are the major
reasons for generation of the PCGs. Two sounds are normally produced as blood flows through the heart valves during
each cardiac cycle (see Fig. 1(b)). The first heart sound S1, is a low, slightly prolonged “lub”, caused by vibrations set
up by the sudden closure of the mitral and tricuspid valves as the ventricles contract and pump blood into the aorta and
pulmonary artery at the start of the ventricular systole. The second sound S2 is a shorter, high-pitched “dup”, caused
when the ventricles stop ejecting, relax and allow the aortic and pulmonary valves to close just after the end of the
ventricular systole. They are the “lubb-dupp” sounds that are thought of as the heartbeat. S1 lasts for an average period
of 100ms−200ms and its frequency components lie in the range of 25Hz−45Hz. S2 lasts about 0.12s, with a frequency
of 50Hz which is typically shorter than S1 in terms of duration and higher in terms of frequency.
IV. FEATURE EXTRACTION: MFCC
Feature extraction is a special form of dimension reduction, which transforms the input data into the set features. Heart
sound is an acoustic signal and many techniques used nowadays for human recognition tasks borrow speech
recognition techniques. The best and popular choice for feature extraction of acoustic signals is the Mel Frequency
Cepstral Coefficients (MFCC) which maps the signal onto a Mel-Scale which is non-linear and mimics the human
hearing. MFCC system is still superior to Cepstral Coefficients despite linear filter-banks in the lower frequency range.
The idea of using Mel Frequency Cepstral Coefficients (MFCC) as the feature set for a PCG biometric system comes
from the success of MFCC for speaker identification [17] and because PCG and speech are both acoustic signals.
MFCC is based on human hearing perceptions which cannot perceive frequencies over 1Khz. In other words, in MFCC
is based on known variation of the human ear’s critical bandwidth with frequency [3, 10].MFCC has two types of filter
which are spaced linearly at low frequency below 1000 Hz and logarithmic spacing above 1000Hz. Mel-frequency
cepstrum coefficients (MFCC), which are the result of a cosine transform of the real logarithm of the short-term
MFCCs are provide more efficient. It includes Mel-frequency wrap-ping and Cepstrum calculation. The overall process
of the MFCC [