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

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.

Wavelet-Domain Information-Hiding Technology with High-Quality Audio Signals on MEMS Sensors.

Due to the rapid development of sensor technology and the popularity of the Internet, not only has the amount of digital information transmission skyrocketed, but also its acquisition and dissemination has become easier. The study mainly investigates audio security issues with data compression for private data transmission on the Internet or MEMS (micro-electro-mechanical systems) audio sensor digital microphones. Imperceptibility, embedding capacity, and robustness are three main requirements for audio information-hiding techniques.

Intelligent Healthcare System Using Patients Confidential Data Communication in Electrocardiogram Signals.

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.

Electrocardiograph Identification Using Hybrid Quantization Sparse Matrix and Multi-Dimensional Approaches.

Electrocardiograph (ECG) technology is vital for biometric security, and blood oxygen is essential for human survival. In this study, ECG signals and blood oxygen levels are combined to increase the accuracy and efficiency of human identification and verification. The proposed scheme maps the combined biometric information to a matrix and quantifies it as a sparse matrix for reorganizational purposes.

ECG Sensor Card with Evolving RBP Algorithms for Human Verification.

It is known that cardiac and respiratory rhythms in electrocardiograms (ECGs) are highly nonlinear and non-stationary. As a result, most traditional time-domain algorithms are inadequate for characterizing the complex dynamics of the ECG. This paper proposes a new ECG sensor card and a statistical-based ECG algorithm, with the aid of a reduced binary pattern (RBP), with the aim of achieving faster ECG human identity recognition with high accuracy.

Hiding patients confidential datainthe ECG signal viaa transform-domain quantization scheme.

Watermarking is the most widely used technology in the field of copyright and biological information protection. In this paper, we use quantization based digital watermark encryption technology on the Electrocardiogram (ECG) to protect patient rights and information. Three transform domains, DWT, DCT, and DFT are adopted to implement the quantization based watermarking technique.