Bernoulli's Chaotic Map-Based 2D ECG Image Steganography

2019 ◽  
pp. 208-241 ◽  
Author(s):  
Anukul Pandey ◽  
Barjinder Singh Saini ◽  
Butta Singh ◽  
Neetu Sood
Keyword(s):  
Signal Processing ◽  
Chaotic Map ◽  
Image Steganography ◽  
Cover Image ◽  
Ecg Signal ◽  
Mean Square ◽  
Secret Key ◽  
Mean Square Difference ◽  
Electrocardiogram Ecg ◽  
Ecg Steganography

Signal processing technology comprehends fundamental theory and implementations for processing data. The processed data is stored in different formats. The mechanism of electrocardiogram (ECG) steganography hides the secret information in the spatial or transformed domain. Patient information is embedded into the ECG signal without sacrificing the significant ECG signal quality. The chapter contributes to ECG steganography by investigating the Bernoulli's chaotic map for 2D ECG image steganography. The methodology adopted is 1) convert ECG signal into the 2D cover image, 2) the cover image is loaded to steganography encoder, and 3) secret key is shared with the steganography decoder. The proposed ECG steganography technique stores 1.5KB data inside ECG signal of 60 seconds at 360 samples/s, with percentage root mean square difference of less than 1%. This advanced 2D ECG steganography finds applications in real-world use which includes telemedicine or telecardiology.

scholarly journals A Trigonometric Projection Method for Overcoming High Intensity Heart Rate Caused Waveform Deformation in Electrocardiogram Biometrics

2021 ◽  
Author(s):  
Tsu-Wang Shen ◽  
Shan-Chun Chang
Keyword(s):  
Heart Rate ◽  
High Intensity ◽  
Correlation Coefficients ◽  
Calibration Method ◽  
Mean Square ◽  
Biometric Technology ◽  
Overall Performance ◽  
Electrocardiogram Signal ◽  
Mean Square Difference ◽  
Electrocardiogram Ecg

Abstract Purpose Although electrocardiogram (ECG) has been proven as a biometric for human identification, applying biometric technology remains challenging with diverse heart rate circumstances in which high intensity heart rate caused waveform deformation may not be known in advance when ECG templates are registered. Methods A calibration method that calculates the ratio of the length of an unidentified electrocardiogram signal to the length of an electrocardiogram template is proposed in this paper. Next, the R peak is used as an axis anchor point of a trigonometric projection (TP) to attain the displacement value. Finally, the unidentified ECG signal is calibrated according to the generated trigonometric value, which corresponds to the trigonometric projection degree of the ratio and the attained displacement measurement. Results The results reveal that the proposed method provides superior overall performance compared with that of the conventional downsampling method, based on the percentage root mean square difference (PRD), correlation coefficients, and mean square error (MSE). Conclusion The curve fitting equation directly maps from the heart rate levels to the TP degree without prior registration information. The proposed ECG calibration method offers a more robust system against heart rate interference when conducting ECG identification.

scholarly journals Denoising the ECG Signal Using Ensemble Empirical Mode Decomposition

2021 ◽  
Vol 11 (5) ◽  
pp. 7536-7541
Author(s):  
W. Mohguen ◽  
S. Bouguezel
Keyword(s):  
Empirical Mode Decomposition ◽  
Additive White Gaussian Noise ◽  
Ensemble Empirical Mode Decomposition ◽  
Ecg Signal ◽  
Mean Square ◽  
Intrinsic Mode Functions ◽  
Denoising Method ◽  
Mode Decomposition ◽  
Mean Square Difference ◽  
Thresholding Function

In this paper, a novel electrocardiogram (ECG) denoising method based on the Ensemble Empirical Mode Decomposition (EEMD) is proposed by introducing a modified customized thresholding function. The basic principle of this method is to decompose the noisy ECG signal into a series of Intrinsic Mode Functions (IMFs) using the EEMD algorithm. Moreover, a modified customized thresholding function was adopted for reducing the noise from the ECG signal and preserve the QRS complexes. The denoised signal was reconstructed using all thresholded IMFs. Real ECG signals having different Additive White Gaussian Noise (AWGN) levels were employed from the MIT-BIH database to evaluate the performance of the proposed method. For this purpose, output SNR (SNRout), Mean Square Error (MSE), and Percentage Root mean square Difference (PRD) parameters were used at different input SNRs (SNRin). The simulation results showed that the proposed method provided significant improvements over existing denoising methods.

scholarly journals Adaptive Electrocardiogram Steganography Based on 2D Approach with Predetermined Rules

2020 ◽  
Vol 8 (1) ◽  
Author(s):  
Ching Yu Yang ◽  
Chi-Ming Lai ◽  
Hung-Chang Lin ◽  
Ting-Ying Lin ◽  
Ruei-Long Lu
Keyword(s):  
Data Hiding ◽  
Gaussian Noise ◽  
Decision Rules ◽  
Ecg Signal ◽  
Two Dimensional ◽  
Hiding Capacity ◽  
Real Time Applications ◽  
Electrocardiogram Ecg ◽  
Ecg Steganography ◽  
Better Than

Based on two-dimensional (2D) bit-embedding/-extraction approach, we propose a simple data hiding for electrocardiogram (ECG) signal. The patient’s sensitive (diagnostic) data can be efficiently hidden into 2D ECG host via the proposed decision rules. The performance of the proposed method using various sizes of the host bundles was demonstrated. Simulations have confirmed that the average SNR of the proposed method with a host bundle of size 3 ´ 3 is superior to that of existing techniques, while our payload is competitive to theirs. In addition, our method with a host bundle of size 2 ´  2 generated the best SNR values, while that with a host bundle of size 4 ´  4 provided the largest payload among the compared methods. Moreover, the proposed method provides robustness performance better than existing ECG steganography. Namely, our method provides high hiding capacity and robust against the attacks such as cropping, inversion, scaling, translation, truncation, and Gaussian noise-addition attacks. Since the proposed method is simple, it can be employed in real-time applications such as portable biometric devices.

A Non-Linear Approach to ECG Signal Processing using Morphological Filters

2013 ◽  
Vol 3 (3) ◽  
pp. 46-59 ◽  
Author(s):  
Vikrant Bhateja ◽  
Rishendra Verma ◽  
Rini Mehrotra ◽  
Shabana Urooj
Keyword(s):  
Signal Processing ◽  
Noise Removal ◽  
High Signal ◽  
Ecg Signal ◽  
Processing Scheme ◽  
Baseline Drift ◽  
Ecg Signals ◽  
Non Linear ◽  
Ecg Signal Processing ◽  
Electrocardiogram Ecg

Analysis of the Electrocardiogram (ECG) signals is the pre-requisite for the clinical diagnosis of cardiovascular diseases. ECG signal is degraded by artifacts such as baseline drift and noises which appear during the acquisition phase. The effect of impulse and Gaussian noises is randomly distributed whereas baseline drift generally affects the baseline of the ECG signal; these artifacts induce interference in the diagnosis of cardio diseases. The influence of these artifacts on the ECG signals needs to be removed by suitable ECG signal processing scheme. This paper proposes combination of non linear morphological operators for the noise and baseline drift removal. Non flat structuring elements of varying dimensions are employed with morphological filtering to achieve low distortion as well as good noise removal. Simulation outcomes illustrate noteworthy improvement in baseline drift yielding lower values of MSE and PRD; on the other hand high signal to noise ratios depicts suppression of impulse and Gaussian noises.

scholarly journals An ECG Signal De-Noising Approach Based on Wavelet Energy and Sub-Band Smoothing Filter

2019 ◽  
Vol 9 (22) ◽  
pp. 4968 ◽  
Author(s):  
Dengyong Zhang ◽  
Shanshan Wang ◽  
Feng Li ◽  
Jin Wang ◽  
Arun Kumar Sangaiah ◽  
...  
Keyword(s):  
Cardiac Disease ◽  
Signal To Noise Ratio ◽  
Signal Quality ◽  
Ecg Signal ◽  
Mean Square ◽  
Wavelet Coefficients ◽  
Ecg Signals ◽  
Smoothing Filter ◽  
Wavelet Energy ◽  
Mean Square Difference

Electrocardiographic (ECG) signal is essential to diagnose and analyse cardiac disease. However, ECG signals are susceptible to be contaminated with various noises, which affect the application value of ECG signals. In this paper, we propose an ECG signal de-noising method using wavelet energy and a sub-band smoothing filter. Unlike the traditional wavelet threshold de-noising method, which carries out threshold processing for all wavelet coefficients, the wavelet coefficients that require threshold de-noising are selected according to the wavelet energy and other wavelet coefficients remain unchanged in the proposed method. Moreover, The sub-band smoothing filter is adopted to further de-noise the ECG signal and improve the ECG signal quality. The ECG signals of the standard MIT-BIH database are adopted to verify the proposed method using MATLAB software. The performance of the proposed approach is assessed using Signal-To-Noise ratio (SNR), Mean Square Error (MSE) and percent root mean square difference (PRD). The experimental results illustrate that the proposed method can effectively remove noise from the noisy ECG signals in comparison to the existing methods.

scholarly journals Removal of Structured Noise and Base Line Wander From ECG Signals via LMS Adaptive and Fixed Notch Filter

2018 ◽  
Vol 3 (8) ◽  
pp. 12
Author(s):  
Kawser Ahammed
Keyword(s):  
Signal To Noise Ratio ◽  
Notch Filter ◽  
Ecg Signal ◽  
Base Line ◽  
Mean Square ◽  
Least Mean Square ◽  
Performance Study ◽  
Comparative Performance ◽  
Simulation Results ◽  
Mean Square Difference

This research clearly demonstrates the comparative performance study of Least Mean Square (LMS) adaptive and fixed Notch filter in terms of simulation results and different performance parameters (mean square error, signal to noise ratio and percentage root mean square difference) for removing structured noise (50 Hz line interference and its harmonics) and baseline wandering from electrocardiogram (ECG) signal. The ECG samples collected from the PhysioNet ECG-ID database are corrupted by adding structured noise and base line wandering noise. The simulation results and numerical performance analysis of this research clearly show that LMS adaptive filter can remove noise efficiently from ECG signal than fixed notch filter

Secret Key-Based Image Steganography in Spatial Domain

2021 ◽  
pp. 2250014
Author(s):  
Rajashree Gajabe ◽  
Syed Taqi Ali
Keyword(s):  
Image Quality ◽  
Secure Communication ◽  
Image Steganography ◽  
Cover Image ◽  
Grayscale Image ◽  
Secret Key ◽  
Stego Image ◽  
Digital World ◽  
Day By Day

Day by day, the requirement for secure communication among users is rising in a digital world, to protect the message from the undesirable users. Steganography is a methodology that satisfies the user’s necessity of secure communication by inserting a message into different formats. This paper proposes a secret key-based image steganography to secure the message by concealing the grayscale image inside a cover image. The proposed technique shares the 20 characters long secret key between two clients where the initial eight characters of a secret key are utilized for bit permutation of characters and pixels while the last 12 characters of secret key decide the encryption keys and position of pixels of a grayscale image into the cover. The grayscale image undergoes operation such as encryption and chaotic baker followed by its hiding in a cover to form a stego image. The execution of the proposed strategy is performed on Matlab 2018. It shows that the proposed approach manages to store the maximum message of size 16[Formula: see text]KB into the cover of size [Formula: see text]. The image quality of stego images has been evaluated using PSNR, MSE. For a full payload of 16[Formula: see text]KB, PSNR is around 51[Formula: see text]dB to 53[Formula: see text]dB which is greater than satisfactory PSNR.

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