Watermarking of ECG signals compressed using Fourier decomposition method

Tripathi, Prashant Mani; Kumar, Ashish; Komaragiri, Rama; Kumar, Manjeet

doi:10.1007/s11042-021-11492-w

Cited by 14 publications

(4 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The FDM algorithm comprises the LTH (Low to High) method, which involves searching from low to high frequencies, and the HTL (High to Low) method, which involves searching from high to low frequencies [23][24][25]. Essentially, the effectiveness of these two methods is indistinguishable, as both perform searches across the entire sampling frequency spectrum.…”

Section: Improved Fourier Decomposition Methodsmentioning

confidence: 99%

Research on Feature Extraction and Fault Diagnosis Method for Rolling Bearing Vibration Signals Based on Improved FDM-SVD and CYCBD

Yang

2024

Symmetry

View full text Add to dashboard Cite

In mechanical equipment, rolling bearing components are constantly exposed to intricate and diverse environmental conditions, rendering them vulnerable to wear, performance degradation, and potential malfunctions. To precisely extract and discern rolling bearing vibration signals amidst intricate noise interference, this paper introduces a fault feature extraction and diagnosis methodology that seamlessly integrates an improved Fourier decomposition method (FDM), singular value decomposition (SVD), and maximum second-order cyclostationary blind convolution (CYCBD). Initially, the FDM is employed to meticulously decompose the bearing fault signals into numerous signal components. Subsequently, a comprehensive weighted screening criterion is formulated, aiming to strike a balance between multiple indicators, thereby enabling the selective screening and reconstruction of pertinent signal components. Furthermore, SVD and CYCBD techniques are introduced to carry out intricate processing and envelope demodulation analysis of the reconstructed signals. Through rigorous simulation experiments and practical rolling bearing fault diagnosis tests, the method’s noteworthy effectiveness in suppressing noise interference, enhancing fault feature information, and efficiently extracting fault features is unequivocally demonstrated. Furthermore, compared to traditional time–frequency analysis methods such as EMD, EEMD, ITD, and VMD, as well as traditional deconvolution methods like MED, OMEDA, and MCKD, this method exhibits significant advantages, providing an effective solution for diagnosing rolling bearing faults in environments with strong background noise.

show abstract

Section: Improved Fourier Decomposition Methodsmentioning

confidence: 99%

Research on Feature Extraction and Fault Diagnosis Method for Rolling Bearing Vibration Signals Based on Improved FDM-SVD and CYCBD

Yang

2024

Symmetry

View full text Add to dashboard Cite

show abstract

“…Fourier decomposition method (FDM) [27] provides us with an algorithm, based on Fourier representation, for nonlinear and non-stationary data. FDM generates a small set of Fourier intrinsic band-limited functions (FIBFs) [28]. FIBF must fulfill the following criteria:…”

Section: ) Preprocessingmentioning

confidence: 99%

Recent Trends in EEG-Based Motor Imagery Signal Analysis and Recognition: A Comprehensive Review

Sharma

Singhal

et al. 2023

IEEE Access

View full text Add to dashboard Cite

The electroencephalogram (EEG) motor imagery (MI) signals are the widespread paradigms in the brain-computer interface (BCI). Its significant applications in the gaming, robotics, and medical fields drew our attention to perform a detailed analysis. However, the problem is ill-posed as these signals are highly nonlinear, unpredictable, and noisy, hence making it exceedingly hard to be analyzed adequately. This paper provides a first-of-its-kind comprehensive review of conventional signal processing and deep learning techniques for BCI MI signal analysis. The review comprises extensive works carried out in the domain in the recent past, highlighting the current challenges of the problem. A new categorization of the existing approaches has been presented for better clarification. An all-inclusive description of the signal processing techniques has been corroborated by relevant works in the area. Moreover, architectures of various standard deep learning algorithms along with their merits and demerits are also explicated to assist the readers. The tabular representations of the numerical results are also readily provided. This work also presents the open research problems and future directions.

show abstract

“…It is a new method to make the hidden message robust against image-processing attacks. In [17], the authors integrated the watermarking and compression in Fourier domain for electrocardiogram (ECG). In [18], authors proposed an ECG watermarking scheme based on redundant discrete wavelet transform (RDWT) and singular value decomposition (SVD).…”

Section: Introductionmentioning

confidence: 99%

“…Table 1 lists the domain, solution, fining, and gap for these references. [11][12][13][14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Patient Confidential Data Hiding and Transmission System Using Amplitude Quantization in the Frequency Domain of ECG Signals

Chen,

Ye,

et al. 2023

Sensors

View full text Add to dashboard Cite

The transform domain provides a useful tool in the field of confidential data hiding and protection. In order to protect and transmit patients’ information and competence, this study develops an amplitude quantization system in a transform domain by hiding patients’ information in an electrocardiogram (ECG). In this system, we first consider a non-linear model with a hiding state switch to enhance the quality of the hidden ECG signals. Next, we utilize particle swarm optimization (PSO) to solve the non-linear model so as to have a good signal-to-noise ratio (SNR), root mean square error (RMSE), and relative root mean square error (rRMSE). Accordingly, the distortion of the shape in each ECG signal is tiny, while the hidden information can fulfill the needs of physiological diagnostics. The extraction of hidden information is reversely similar to a hiding procedure without primary ECG signals. Preliminary outcomes confirm the effectiveness of our proposed method, especially an Amplitude Similarity of almost 1, an Interval RMSE of almost 0, and SNRs all above 30.

show abstract

Watermarking of ECG signals compressed using Fourier decomposition method

Cited by 14 publications

References 35 publications

Research on Feature Extraction and Fault Diagnosis Method for Rolling Bearing Vibration Signals Based on Improved FDM-SVD and CYCBD

Research on Feature Extraction and Fault Diagnosis Method for Rolling Bearing Vibration Signals Based on Improved FDM-SVD and CYCBD

Recent Trends in EEG-Based Motor Imagery Signal Analysis and Recognition: A Comprehensive Review

Patient Confidential Data Hiding and Transmission System Using Amplitude Quantization in the Frequency Domain of ECG Signals

Contact Info

Product

Resources

About