Motion artifact contamination may adversely affect the interpretation of biological signals. The development of algorithms to detect, identify, quantify, and mitigate motion artifact is typically performed using a ground truth signal contaminated with previously recorded motion artifact, or simulated motion artifact. The diversity of available motion artifact recordings is limited, and the rationales for existing models of motion artifact are poorly described. In this paper we developed an autoregressive (AR) model of motion artifact based on data collected from 6 subjects walking at slow, medium, and fast paces. The AR model was evaluated for its ability to generate diverse data that replicated the properties of the experimental data. The simulated motion artifact data was successful at learning key time domain and frequency domain properties, including the mean, variance, and power spectrum of the data, but was ineffective for imitating the morphology and probability distribution of the motion artifact data (kurtosis % error of 100.9-103.6%). More sophisticated models of motion artifact may be necessary to develop simulations of motion artifact.Vibroarthrographic (VAG) signals are sounds or vibrations caused when a knee joint is flexed or stretched. VAG signal collection is noninvasive and can be performed using an accelerometer or microphone attached to the skin. However, the sensor attached to the skin will move with the soft tissue caused by flexion and extension, causing the baseline of the VAG signal to drift. We call these interferences soft tissue movement artifacts (STMAs). In this study, an algorithm is proposed to filter out STMAs. We compare the proposed method's results with noises collected by an accelerometer. The noise reduction effect is evaluated, revealing an 11.85% increase in the peak signal-to-noise ratio and a 28.18% increase in signal-to-noise ratio compared with the case in which STMA noise was not removed.Clinical Relevance-This study focuses on a proposed post-processing method that can remove soft tissue movement artifacts that cause baseline wander and could thus improve the accuracy of clinical applications of VAG signals.Artifact removal is important for EEG signal processing because artifacts adversely affect analysis results. To preserve normal EEG signal, several methods based on independent component analysis (ICA) have been studied and artifacts are removed by discarding independent components (ICs) classified as artifacts. In this study, a method using Bayesian deep learning and attention module is presented to improve performance of the classifier for ICs. Probability value is computed through the method to predict if a component is artifact and to treat ambiguous inputs. The attention module achieves increasing classification accuracy and shows the map of the region where the classifier concentrates on.The analysis of the Nystagmus waveforms from eye-tracking records is crucial for the clinical interpretation of this pathological movement. A major issue to automatize this analysis is the presence of natural eye movements and eye blink artefacts that are mixed with the signal of interest. We propose a method based on Convolutional Dictionary Learning that is able to automatically highlight the Nystagmus waveforms, separating the natural motion from the pathological movements. We show on simulated signals that our method can indeed improve the pattern recovery rate and provide clinical examples to illustrate how this algorithm performs.Photoplethysmography is a non-invasive and easy to administer optical method used primarily to mea-sure blood oxygen saturation, but also used widely to estimate and measure various other physiological parameters. ⁄is paper reviews several physiological parameter estimations that have been done with just this waveform signal, i.e. heart rate, lipid profiling by morphological PPG analysis, blood glucose, ankle brachial pressure, and respiratory rate. Additional physiological estimations which use additional input measurements are reviewed in Part 2 of this paper. The different methods and signal processing techniques based on the principle of operation are discussed in this review. ⁄e validity of each of these optical measurement techniques are reviewed where the results were compared with the results obtained using the gold reference standards. Future research considerations for non-invasive wearable devices for physiological parameter measurements are also highlighted in this review which could be helpful for future research.Synthesis of accurate, personalize photoplethysmogram (PPG) signal is important to interpret, analyze and predict cardiovascular disease progression. Generative models like Generative Adversarial Networks (GANs) can be used for signal synthesis, however, they are difficult to map to the underlying pathophysiological conditions. Hence, we propose a PPG synthesis strategy that has been designed using a cardiovascular system, modeled through the hemodynamic principle. The modeled architecture is composed of a two-chambered heart along with the systemic-pulmonic blood circulation and a baroreflex auto-regulation mechanism to control the arterial blood pressure. The comprehensive PPG signal is synthesized from the cardiac pressure-flow dynamics.  https://www.selleckchem.com/products/sy-5609.html  In order to tune the modeled cardiac parameters with respect to a measured PPG data, a novel feature extraction strategy has been employed along with the particle swarm optimization heuristics. Our results demonstrate that the synthesized PPG is accurately followed the morphological changes of the ground truth (GT) signal with an RMSE of 0.003 occurring due to the Coronary Artery Disease (CAD) which is caused by an obstruction in the artery.Photoplethysmography (PPG) is a non-invasive, low-cost optical technique used to assess the cardiovascular system. In recent years, PPG-based heart rate measurement has gained significant attention due to its popularity in wearable devices, as well as its practicality relative to electrocardiography (ECG). Studies comparing the dynamics of ECG- and PPG-based heart rate measures have found small differences between these two modalities; differences related to the physiological processes behind each technique. In this work, we analyzed the spectral coherence and the signal-to-noise ratio between isolated PPG pulses and the raw PPG signal in order to (i) determine the optimal filter to enhance pulse detection from raw PPG for improved heart rate estimation, and (ii) characterize the spectral content of the PPG pulse. The proposed methods were evaluated on 27000 pulses from a PPG database acquired from 42 participants (adults and children). The results showed that the optimal bandpass filter to enhance PPG from the adult group was 0.