Bernstein polynomials and Bezier curves: a novel modeling approach to secure ECG data transmission

In the digital era, protecting sensitive medical data, including Electrocardiogram (ECG) signals, is of utmost importance. Maintaining the confidentiality, integrity, and authenticity of ECG signals while safeguarding their privacy is a critical challenge. This paper proposes a novel approach to safeguard ECG waveforms by utilizing polynomial modeling techniques followed by encryption. Polynomial functions, specifically Bernstein and Bezier-Bernstein polynomials, are employed for accurate approximation of the ECG signal, accounting for noise, interference, and channel distortions. The coefficients obtained from polynomial modeling are encrypted using the standard AES and Fernet algorithms. Decryption allows authorized access to the ECG signals, facilitating methodical waveform reconstruction. The proposed methodology’s performance is evaluated using various metrics to assess its effectiveness in protecting sensitive medical data. Metrics include Root Mean Square Error, Peak Signal to Noise Ratio, Correlation Coefficient, and Structural Similarity Index. The proposed method effectively safeguards ECG waveforms by combining polynomial modeling and encryption techniques. The approach offers a robust solution to protect sensitive medical data in the digital realm.