Intelligent Healthcare System Using Patients Confidential Data Communication in Electrocardiogram Signals

Area of Science:

• Biomedical Engineering
• Healthcare Informatics
• Signal Processing

Background:

• The aging population and increasing prevalence of dementia necessitate advanced healthcare solutions.
• Protecting patient confidentiality is paramount in healthcare, especially for sensitive medical data.
• Existing methods for data hiding in physiological signals may compromise signal integrity.

Purpose of the Study:

• To develop an intelligent healthcare system for secure patient data hiding.
• To embed confidential patient data into electrocardiogram (ECG) signals using transform domain techniques.
• To ensure the quality and diagnostic utility of ECG signals after data embedding.

Main Methods:

• Implementation of an intelligent healthcare system utilizing multiple-coefficient quantization in the transform domain.
• Development of a non-linear model to optimize the quality of embedded ECG signals.
• Application of simulated annealing (SA) to solve the non-linear model, optimizing signal-to-noise ratio (SNR), root mean square error (RMSE), and relative RMSE (rRMSE).

Main Results:

• Successfully embedded confidential patient data into ECG signals with minimal distortion to PQRST complexes and amplitude.
• Achieved good SNR, RMSE, and rRMSE, ensuring the physiological diagnostic quality of the modified ECG signals.
• Demonstrated that the method maintains high ECG signal quality even with increased quantization size (Q).

Conclusions:

• The proposed method effectively hides confidential patient data within ECG signals.
• The technique preserves the diagnostic integrity of ECG signals, crucial for patient monitoring.
• This approach offers a promising solution for secure and private data management in intelligent healthcare systems.