P045068488

Malashree K S et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 5( Version 6), May 2014, pp.84-88
www.ijera.com 84 | P a g e
Confidential Data Hiding Using Wavlet Based Ecg Stegnography
Malashree K S1
,Jagadish K N 2
, Suma.M3
1
Student,M.tech, Electronics and communication Department, The a. i. t College of Engineering,
chikkamagalore, India
2
Student, M.tech, Electronics and communication Department, The B.g.s.i.t College of Engineering, BG nagar,
Mandya,India
3
asst.prof, Electronics and communication Department, The a. i. t College of Engineering, chikkamagalore,
India
ABSTRACT
With the growing number of aging population and a significant portion of that suffering from cardiac diseases, it
is conceivable that remote ECG patient monitoring systems are expected to be widely used as Point-of-Care
(PoC) applications in hospitals around the world. Therefore, huge amount of ECG signal collected by Body
Sensor Networks (BSNs) from remote patients at homes will be transmitted along with other physiological
readings such as blood pressure, temperature, glucose level etc. and diagnosed by those remote patient
monitoring systems. It is utterly important that patient confidentiality is protected while data is being transmitted
over the public network as well as when they are stored in hospital servers used by remote monitoring systems.
In this project, a wavelet based steganography technique has been introduced which combines encryption and
scrambling technique to protect patient confidential data. The proposed method allows ECG signal to hide its
corresponding patient confidential data and other physiological information thus guaranteeing the integration
between ECG and the rest.
Index Terms—ECG, Steganography, Encryption,Wavelet, Wa-termarking, Conﬁdentiality.
I. INTRODUCTION
The number of elderly patients is increasing
dramatically due to the recent medical advancements.
Accordingly, to reduce the medical labour cost, the
use of remote healthcare monitoring systems and
Point-of-Care (PoC) technologies have become
popular . Monitoring patients at their home can
drastically reduce the increasing traffic at hospitals
and medical centres. Moreover, Point-of-Care
solutions can provide more reliability in emergency
services as patient medical information (ex. for
diagnosis) can be sent immediately to doctors and
response or appropriate action can be taken without
delay. However, Remote health care systems are used
in large geographical areas essentially for monitoring
channel used to exchange information. Typically,
patient biological signals and other physiological
readings are collected using body sensors. Next, the
collected signals are sent to the patient PDA device
for further processing or diagnoses. Finally, the
signals and patient confidential information as well as
diagnoses report or any urgent alerts are sent to the
central hospital servers via the Internet. Doctors can
check those biomedical signals and possibly make a
decision in case of an emergency from anywhere
using any device[3]. Using Internet as main
communication channel introduces new security and
privacy threats as well as data integration issues.
According to the Health Insurance Portability and
Accountability Act (HIPAA), information sent
through the Internet should be protected and secured.
HIPAA mandates that while transmitting information
through the internet a patient’s privacy and
confidentiality be protected as follows:
 Patient privacy: It is of crucial importance that a
patient can control who will use his/her
confidential health information, such as name,
address, telephone number, and Medicare
number. As a result, the security protocol should
provide further control on who can access
patient’s data and who cannot.
 Security: The methods of computer software
should guarantee the security of the information
inside the communication channels as well as the
information stored on the hospital server.
Accordingly, it is of crucial importance to
implement a security protocol which will have
powerful communication and storage security.
PROBLEM STATEMENT
Several researchers have proposed various
security protocols to secure patient confidential
information. Techniques used can be categorized into
two subcategories. Firstly, there are techniques that
are based on encryption and cryptographic
algorithms. These techniques are used to secure data
during the communication and storage. As a result,
the final data will be stored in encrypted format. The
disadvantage of using encryption based techniques is
its large computational overhead. Therefore,
RESEARCH ARTICLE OPEN ACCESS

encryption based methods are not suitable in
resource-constrained mobile environment.
PROBLEM FORMULATION
I. Many security techniques are based on hiding its
sensitive information inside another insensitive
host data without incurring any increase in the
host data size and huge computational overhead.
These techniques are called steganography
techniques. Steganography is the art of hiding
secret information inside another type of data
called host data. However, steganography
techniques alone will not solve the authentication
problem and cannot give the patients the required
ability to control who can access their personal
information as stated by HIPAA.
II. PROPOSED SYSTEM
Fig. 1 Block diagram of the sender steganography
which includes encryption, wavelet decomposition
and secret data embedding.
III. METHODOLOGY
The sender side of the proposed
steganography technique consists of four integrated
stages as shown in Fig 1. The proposed technique is
designed to ensure secure information hiding with
minimal distortion of the host signal. Moreover, this
technique contains an authentication stage to prevent
unauthorized users from extracting the hidden
information.
A. Stage 1: Encryption
The aim of this stage is to encrypt the
patient confidential information in such a way that
prevents unauthorized persons - who does not have
the shared key- from accessing patient confidential
data. In this stage XOR ciphering technique is used
with an ASCII coded shared key which will play the
role of the security key. XOR ciphering is selected
because of its simplicity. As a result, XOR ciphering
can be easily implemented inside a mobile device.
Fig 2 shows an example of what information could be
stored inside the ECG signal .
RSA ALGORITHM:
This algorithm is based on the difficulty of
factorizing large numbers that have 2 and only 2
factors (Prime numbers). The system works on a
public and private key system. The public key is
made available to everyone. With this key a user can
encrypt data but cannot decrypt it, the only person
whocan decrypt it is the one who possesses the
private key. It is theoretically possible but extremely
difficult to generate the private key from the
publickey, this makes the RSA algorithm a very
popular choice in data encryption.
Fig2 RSA algorithm and result
B. Stage 2: Wavelet Decomposition
Wavelet transform is a process that can
decompose the given signal into coefficients
representing frequency components of the signal at a
given time. Wavelet transform can be defined as
shown in time domain with frequency domain in one
transform. In most applications discrete signals are
used. Therefore, Discrete Wavelet Transform (DWT)
must be used instead of continuous wavelet
transform. DWT decomposition can be performed by
applying wavelet transform to the signal using band
filters. The result of the band filtering operation will

be two different signals, one will be related to the
high frequency componentsand the other related to
the low frequency components of the original signal.
If this process is repeated multiple times, then it is
called multi-level packet wavelet decomposition.
Discrete
Wavelet transform can be defined
Where W(i, j) represents the DWT coefficients. i and
j arethe scale and shift transform parameters, and
ij(n) is the wavelet basis time function with finite
energy and fast decay. The wavelet function can be
defined
Fig.3 5-level wavelet decomposition tree showing 32
sub-bands of ECG host signal and the secret data will
be hidden inside the coefficients of the sub-bands
In this project, 32-level wavelet packet
decomposition has been applied to the host signal.
Accordingly, 4 sub-bands resulted from this
decomposition process as shown in Fig 2. In each
decomposition iteration the original signal is divided
into two signals. Moreover, the frequency spectrum is
distributed on these two signals. Therefore, one of the
resulting signals will represent the high frequency
component and the other one represents the low
frequency component. Most of the important features
of the ECG signal are related to the low frequency
signal. Therefore, this signal is called the
approximation signal (A). On the other hand, the high
frequency signal represents mostly the noise part of
the ECG signal and is called detail signal (D).
Fig 3 dwt sub bands
GENERAL DWT SUB BAND AND ECG
LL SUB BAND HL SUB BAND
LH SUB BAND HH SUB BAND
Fig 4 results of LL,LH.HL.HH sub bands from dwt
C. Stage 3: The embedding operation
At this stage the proposed technique will use
a special security implementation to ensure high data
security. In this technique a scrambling operation is
performed using two parameters. First is the shared
key known to both the sender and the receiver.
Second is the scrambling matrix, which is stored
inside both the transmitter and the receiver. Each
transmitter/ receiver pair has a unique scrambling
matrix defined by
where S represents wavelet function. S and
P are positive integers representing transform
parameters. C represents the coefficients which is a
function of scale and position parameters. Wavelet
transform is a powerful tool to combine Where S is a
128 × 32 scrambling matrix. s is a number between 1
and 32. While building the matrix we make sure that
the following conditions are met:
• The same row must not contain duplicate elements
• Rows must not be duplicates.
The detailed block diagram for the data
embedding process is shown in Fig . The embedding
LL HL
LH HH

stage starts with converting the shared key into
ASCII codes,therefore each character is represented
by a number from 1 to 128. For each character code
the scrambling sequence fetcher will read the
corresponding row from the scrambling matrix.
Anexample of a fetched row can be shown in Fig 3.
Fig.5 Block diagram of the receiver steganography
which includes wavelet decomposition, extraction
and decryption
The embedding operation performs the data hiding
process in the wavelet coefficients according to the
sub-band sequence from the fetched row., the
embedding process will start by reading the current
wavelet coefficient in sub-band 32 and changing its
LSB bits. Then, it will read the current wavelet
coefficient in sub-band 22 and changing its LSB bits,
and so on. On the other hand, the steganography level
is determined according to the level vector which
contains the information about how many LSB bits
will be changed for each sub-band. For example if
the data is embedded in sub-band 32 then 6 bits will
be changed per sample, while if it is embedded into
wavelet coefficient in sub-band 1 then 5 LSB bits
will be changed.
D. Stage 4: Inverse wavelet re-composition
The resultant watermarked 32 sub-bands are
recomposed using inverse wavelet packet re-
composition. The result of this operation is the new
watermarked ECG signal. The inverse wavelet
process will convert the signal to the time domain
instead of combined time and frequency domain.
Therefore, the newly reconstructed watermarked
ECG signal will be very similar to the original
unwatermarked ECG signal. The detailed embedding
algorithm is shown in Algorithm 1. The algorithm
starts by initializing the required variables. Next, the
coefficient matrix will be shifted and scaled to ensure
that all coefficients values are integers. Then, the
algorithm will select a node out of 32 nodes in each
row of the coefficient matrix. The selection process is
based on the value read from the scrambling matrix
and the key. The algorithm will be repeated until the
end of the coefficient matrix is reached. Finally, the
coefficient matrix will be shifted again and re-scaled
to return its original range and inverse wavelet
transform is applied to produce the watermarked
ECG signal.
Fig. 4 Block diagram showing the detailed
construction of the watermark Embedding operation
FLOWCHART
Fig Flow chart of embedded operation and
scrambling matrix opertion
RESULTS:
INPUT:

AFTER ENCRYPTION:
OUTPUT
RECOVER AND DECRYPATED DATA
IV. CONCLUSION AND FUTURE WORK
A novel steganography algorithm is
proposed to hide patient information as well as
diagnostics information inside ECG signal. This
technique will provide a secured communication and
confidentiality in a Point-of-Care system. A 5-level
wavelet decomposition is applied. A scrambling
matrix is used to find the correct embedding
sequence based on the user defined key.
Steganography levels (i.e. number of bits to hide in
the coefficients of each sub-band) are determined for
each sub-band by experimental methods. In this
paper we tested the diagnoses quality distortion. It is
found that the resultant watermarked ECG can be
used for diagnoses andthe hidden data can be totally
extracted.
REFERENCES
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Technology in Biomedicine,, vol. 11, no. 6,
pp. 619–627, 2007. patient record using
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[3] A. Ibaida, I. Khalil, and F. Sufi, ―Cardiac
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Processing (ISSNIP), 2009. IEEE, 2010, pp.
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[4] W. Lee and C. Lee, ―A cryptographic key
management solution for hipaa
privacy/security regulations,‖ IEEE
Transactions on Information Technology in
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2008.[5] K. Malasri and L. Wang,
―Addressing security in medical sensor
networks,‖ in Proceedings.

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