Physiological Noise: Definition, Estimation, and Characterization in Complex Biomedical Signals

Background: Nonlinear physiological systems exhibit complex dynamics driven by intrinsic dynamical noise. In cases where there is no specific knowledge or assumption about system dynamics, such as in physiological systems, it is not possible to formally estimate noise. Aim: We introduce a formal method to estimate the power of dynamical noise, referred to as physiological noise, in a closed form, without specific knowledge of the system dynamics. Methodology: Assuming that noise can be modeled as a sequence of independent, identically distributed (IID) random variables on a probability space, we demonstrate that physiological noise can be estimated through a nonlinear entropy profile. We estimated noise from synthetic maps that included autoregressive, logistic, and Pomeau-Manneville systems under various conditions. Noise estimation is performed on 70 heart rate variability series from healthy and pathological subjects, and 32 electroencephalographic (EEG) healthy series. Results: Our results showed that the proposed model-free method can discern different noise levels without any prior knowledge of the system dynamics. Physiological noise accounts for around 11% of the overall power observed in EEG signals and approximately 32% to 65% of the power related to heartbeat dynamics. Cardiovascular noise increases in pathological conditions compared to healthy dynamics, and cortical brain noise increases during mental arithmetic computations over the prefrontal and occipital regions. Brain noise is differently distributed across cortical regions. Conclusion: Physiological noise is very part neurobiological dynamics and can be measured using the proposed framework in any biomedical series

Estimation of Dynamical Noise Power in Unknown Systems

Noise can be modeled as a sequence of random variables defined on a probability space that may be added to a given dynamical system $T$, which is a map on a phase space. In the non-trivial case of dynamical noise $\lbrace \varepsilon _{n}\rbrace _{n}$, where $\varepsilon _{n}$ follows a Gaussian distribution $\mathcal {N}(0,\sigma ^{2})$ and the system output is $x_{n} = T(x_{n-1};x_{0})+\varepsilon _{n}$, without any specific knowledge or assumption about $T$, the quantitative estimation of the noise power $\sigma ^{2}$ is a challenge. Here, we introduce a formal method based on the nonlinear entropy profile to estimate the dynamical noise power $\sigma ^{2}$ without requiring knowledge of the specific $T$ function. We tested the correctness of the proposed method using time series generated from Logistic maps and Pomeau-Manneville systems under different conditions. Our results demonstrate that the proposed estimation algorithm can properly discern different noise levels without any a priori information

Stochastic brain dynamics exhibits differential regional distribution and maturation-related changes

Functional magnetic resonance imaging (fMRI) is a powerful non-invasive method for studying brain function by analyzing blood oxygenation level-dependent (BOLD) signals. These signals arise from intricate interplays of deterministic and stochastic biological elements. Quantifying the stochastic part is challenging due to its reliance on assumptions about the deterministic segment. We present a methodological framework to estimate intrinsic stochastic brain dynamics in fMRI data without assuming deterministic dynamics. Our approach utilizes Approximate Entropy and its behavior in noisy series to identify and characterize dynamical noise in unobservable fMRI dynamics. Applied to extensive fMRI datasets (645 Cam-CAN, 1086 Human Connectome Project subjects), we explore lifelong maturation of intrinsic brain noise. Findings indicate 10% to 60% of fMRI signal power is due to intrinsic stochastic brain elements, varying by age. These components demonstrate a physiological role of neural noise which shows a distinct distributions across brain regions and increase linearly during maturation

FLOW-THROUGH ANALYSIS OF GLUTATHIONE IN HUMAN ERYTHROCYTES WITH AN AMPEROMETRIC BIOSENSOR

A flow-through system for the determination of glutathione was developed using the enzyme glutathione oxidase covalently immobilized on an Immobilon AV membrane assembled on a Clark type oxygen electrode. A calibration curve for glutathione, was obtained in the range 10(-5) - 10(-3) mol/L with a detection limit of 5 . 10(-6) mol/L and a relative standard deviation of 4.5%.[...

Electrochemical probe for polyamines detection in biological fluids

The enzyme polyamine oxidase from maize has been purified and covalently immobilized onto polymeric supports to assemble a polyamine electrochemical biosensor. The enzyme has been used in conjunction with either an O-2 or a H2O2 electrode. The analytical behaviour of this enzyme electrode has been studied with respect to sensitivity, selectivity, optimum pH and buffer, response time and lifetime. Recovery studies performed on urine samples demonstrated that the polyamine biosensor developed can be successfully used to measure polyamines in biological fluids.[...

AMPEROMETRIC GLUTATHIONE ELECTRODES

Amperometric glutathione sensors based on hydrogen peroxide or Clark-type oxygen electrodes, coupled with a novel enzyme glutathione oxidase, have been assembled and analytically evaluated. Calibration curves were linear in the range 5 x 10(-6) to 10(-3) m for the H2O2-based probe and 10(-5) to 2 x 10(-4) M for the oxygen probe. When the glutathione analysis was carried out using the O2-based probe, a flow-through system was used to maintain better control over the oxygen content of the sample. Temperature, pH and selectivity studies were carried out and optimized. Stability studies of the enzyme electrode showed an enzyme lifetime of one month, with more than 200 analyses performed. The precision of both sensors was tested by making 20 measurements of glutathione in phosphate buffer, pH 6.5. Results showed relative standard deviations of 4-6% and 4.1%, respectively. The response time for both electrodes was one or two minutes, depending on the blocking membrane used. The analysis of glutathione in human erythrocytes has been carried out using the 02-based probe and the flow-through system. Results were in agreement with those reported in the literature.[...