Meta-Analysis of Pulse Transition Features in Non-Invasive Blood Pressure Estimation Systems: Bridging Physiology and Engineering Perspectives.

The pulse transition features (PTFs), including pulse arrival time (PAT) and pulse transition time (PTT), hold significant importance in estimating non-invasive blood pressure (NIBP). However, the literature showcases considerable variations in terms of PTFs' correlation with blood pressure (BP), accuracy in NIBP estimation, and the comprehension of the relationship between PTFs and BP. This inconsistency is exemplified by the wide-ranging correlations reported across studies investigating the same feature.

Emulation of Brain Metabolic Activities Based on a Dynamically Controllable Optical Phantom.

This paper presents a dynamic optical phantom for the simulation of metabolic activities in the brain, and a linear equivalent model is built for control voltage versus substance concentration. A solid-solid dynamic optical phantom is realized by using liquid crystal film as a voltage-controlled light intensity regulator on the surface of basic phantom, which uses epoxy resin as matrix material and nanometer carbon powder and titanium dioxide powder as absorption and scattering dopants, respectively. The dynamic phantom could mimic near-infrared spectrum (NIRS) signals with sampling rate up to 10 Hz, and the maximum simulation errors for oxy-hemoglobin and deoxy-hemoglobin concentrations varying in the range of 1 μmol/l are 7.

Lightweight image super-resolution based multi-order gated aggregation network.

Recently, Transformer-based models are taken much focus on solving the task of image super-resolution (SR) due to their ability to achieve better performance. However, these models combined huge computational cost during the computing self-attention mechanism. To solve this problem, we proposed a multi-order gated aggregation super-resolution network (MogaSRN) for low-level vision based on the concept of the MogaNet that is developed for high-level vision.

Robust Arrhythmia Classification Based on QRS Detection and a Compact 1D-CNN for Wearable ECG Devices.

Embedded arrhythmia classification is the first step towards heart diseases prevention in wearable applications. In this paper, a robust arrhythmia classification algorithm, NEO-CCNN, for wearables that can be implemented on a simple microcontroller is proposed. The NEO-CCNN algorithm not only detects QRS complex but also accurately locates R-peak with the help of the proposed adaptive time-dependent thresholding technique, improving the accuracy and sensitivity in arrhythmia classification.

BHI-Net: Brain-Heart Interaction-Based Deep Architectures for Epileptic Seizures and Firing Location Detection.

Automatic detection of epileptic seizures is still a challenging problem due to the intolerance of EEG. Introducing ECG can help with EEG for detecting seizures. However, the existing methods depended on fusing either the extracted features or the classification results of EEG-only and ECG-only with ignoring the interaction between them, so the detection rate did not improve much.