Wavelet-Based Quantization Watermarking for ECG Signals

He, Xialong; Tseng, Kuo-Kun; Huang, Huang; Hsu, Chih‐Yu; Tu, Shu-Yi; Zeng, Fufu; Pan, Jeng‐Shyang

doi:10.1109/cmcsn.2012.119

Cited by 11 publications

(4 citation statements)

References 8 publications

(10 reference statements)

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“…In [7,8], the authors applied a quantization watermarking technique to ECG signals in a wavelet domain. Nevertheless, this method was blind.…”

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

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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.

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“…In [7,8], the authors applied a quantization watermarking technique to ECG signals in a wavelet domain. Nevertheless, this method was blind.…”

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

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“…In Guo and Zhou (2012) and He et al (2012) , a Haar wavelet transform with 7 levels decomposition is adopted to transform the ECG signal, and then the synchronization code combined with watermark are embedded into the low-frequency sub-band of level 7 to have good signal-to-noise ratio (SNR) and bit error rate (BER). However, the quality of all watermarked ECG signals decreases when the embedding strength increases.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Healthcare System Using Patients Confidential Data Communication in Electrocardiogram Signals

Zhao

Hsu

Chen

et al. 2022

Front. Aging Neurosci.

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With the advent of the aging era, healthcare and elderly care have become the focus of medical care, especially the care of the elderly with dementia. Patients’ confidential data hiding is a useful technology for healthcare and patient information privacy. In this study, we implement an intelligent healthcare system using the multiple-coefficient quantization technology in transform domain to hide patients’ confidential data into electrocardiogram (ECG) signals obtained by ECG sensor module. In embedding patients’ confidential data, we first consider a non-linear model for optimizing the quality of the embedded ECG signals. Next, we apply simulated annealing (SA) to solve the non-linear model so as to have good signal-to-noise ratio (SNR), root mean square error (RMSE), and relative RMSE (rRMSE). Accordingly, the distortion of the PQRST complexes and the ECG amplitude is very small so that the embedded confidential data can satisfy the requirements of physiological diagnostics. In end devices, one can receive the ECG signals with the embedded confidential data and without the original ECG signals. Experimental results confirm the effectiveness of our method, which remains high quality for each ECG signal with the embedded confidential data no matter how the quantization size Q is increased.

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“…Authors in [ 17 , 18 ] apply a discrete wavelet transform (DWT) with seven-level decomposition to transform the ECG signal and combine the synchronization code with a watermark embedded in the low-frequency sub-band of level 7 to get better Signal-to-Noise ratio (SNR) and bit error rate (BER). However, the quality of all watermarked ECG signals degrades when increasing the embedding strength.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Healthcare System Using Mathematical Model and Simulated Annealing to Hide Patients Data in the Low-Frequency Amplitude of ECG Signals

Hsu

Chen

Liu

et al. 2022

Sensors

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Healthcare is an important medical topic in recent years. In this study, the novelty we propose is the intelligent healthcare system using an inequality-type optimization mathematical model with signal-to-noise ratio (SNR) and wavelet-domain low-frequency amplitude adjustment techniques to hide patients’ confidential data in their electrocardiogram (ECG) signals. The extraction of the hidden patient information also utilizes the low-frequency amplitude adjustment. The detailed steps of establishing the system are as follows. To integrate confidential patient data into ECG signals, we first propose a nonlinear model to optimize the quality of ECG signals with the embedded patients’ confidential data including patient name, patient birthdate, date of medical treatment, and medical history. Then, we apply Simulated Annealing (SA) to solve the nonlinear model such that the ECG signals with embedded patients’ confidential data have good SNR, good root mean square error (RMSE), and high similarity. In other words, the distortion of the PQRST complexes and the ECG shape caused by the embedded patients’ confidential data is very small, and thus the quality of the embedded ECG signals meets the requirements of physiological diagnostics. In the terminals, one can receive the ECG signals with the embedded patients’ confidential data. In addition, the embedded patients’ confidential data can be received and extracted without the original ECG signals. The experimental results confirm the efficiency that our method maintains a high quality of each ECG signal with the embedded patient confidential data. Moreover, the embedded confidential data shows a good robustness against common attacks.

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Wavelet-Based Quantization Watermarking for ECG Signals

Cited by 11 publications

References 8 publications

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

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

Intelligent Healthcare System Using Patients Confidential Data Communication in Electrocardiogram Signals

Intelligent Healthcare System Using Mathematical Model and Simulated Annealing to Hide Patients Data in the Low-Frequency Amplitude of ECG Signals

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