Computation of Nonlinear Parameters for EEG Signals

In this text, we will compute nonlinear parameters for electroencephalogram (EEG) signals and perform analysis and filtering of electrocardiogram (ECG) signals, along with baseline drift removal. Additionally, we will conduct correlation analysis between EEG and ECG signals to explore their interrelationships. We will employ advanced algorithms and techniques to enhance the accuracy and reliability of data processing. Through these analytical procedures, we aim to better understand the characteristics of EEG and ECG signals and extract valuable information from them. This information holds significant importance for research and diagnosis in related fields, contributing to the advancement of relevant scientific studies.

From a code implementation perspective, the computation of EEG nonlinear parameters typically involves algorithms such as entropy measures (e.g., sample entropy, approximate entropy) and Lyapunov exponents. ECG signal analysis may include QRS complex detection using Pan-Tompkins algorithm and digital filtering techniques like Butterworth filters for noise removal. Baseline drift correction can be implemented using polynomial fitting or high-pass filtering approaches. Correlation analysis between signals can be performed using cross-correlation functions or coherence analysis in the frequency domain.