LPC-SonoNet: A Lightweight Network Based on SonoNet and Light Pyramid Convolution for Fetal Ultrasound Standard Plane Detection.

The detection of fetal ultrasound standard planes (FUSPs) is important for the diagnosis of fetal malformation and the prevention of perinatal death. As a promising deep-learning technique in FUSP detection, SonoNet's network parameters have a large size. In this paper, we introduced a light pyramid convolution (LPC) block into SonoNet and proposed LPC-SonoNet with reduced network parameters for FUSP detection.

Quantum machine learning enhanced laser speckle analysis for precise speed prediction.

Laser speckle contrast imaging (LSCI) is an optical technique used to assess blood flow perfusion by modeling changes in speckle intensity, but it is generally limited to qualitative analysis due to difficulties in absolute quantification. Three-dimensional convolutional neural networks (3D CNNs) enhance the quantitative performance of LSCI by excelling at extracting spatiotemporal features from speckle data. However, excessive downsampling techniques can lead to significant information loss.

Ultrasound normalized cumulative residual entropy imaging: Theory, methodology, and application.

Background And Objective: Ultrasound information entropy imaging is an emerging quantitative ultrasound technique for characterizing local tissue scatterer concentrations and arrangements. However, the commonly used ultrasound Shannon entropy imaging based on histogram-derived discrete probability estimation suffers from the drawbacks of histogram settings dependence and unknown estimator performance. In this paper, we introduced the information-theoretic cumulative residual entropy (CRE) defined in a continuous distribution of cumulative distribution functions as a new entropy measure of ultrasound backscatter envelope uncertainty or complexity, and proposed ultrasound CRE imaging for tissue characterization.

SQI-DOANet: electroencephalogram-based deep neural network for estimating signal quality index and depth of anaesthesia.

. Monitoring the depth of anaesthesia (DOA) during surgery is of critical importance. However, during surgery electroencephalography (EEG) is usually subject to various disturbances that affect the accuracy of DOA.

Ultrasound Entropy Imaging Based on the Kernel Density Estimation: A New Approach to Hepatic Steatosis Characterization.

In this paper, we present the kernel density estimation (KDE)-based parallelized ultrasound entropy imaging and apply it for hepatic steatosis characterization. A KDE technique was used to estimate the probability density function (PDF) of ultrasound backscattered signals. The estimated PDF was utilized to estimate the Shannon entropy to construct parametric images.

MBR-Net: A multi-branch residual network based on ultrasound backscattered signals for characterizing pediatric hepatic steatosis.

The evaluation of pediatric hepatic steatosis and early detection of fatty liver in children are of critical importance. In this paper, a deep learning model based on the convolutional neural network (CNN) of ultrasound backscattered signals, multi-branch residual network (MBR-Net), was proposed for characterizing pediatric hepatic steatosis. The MBR-Net was composed of three convolutional branches.

Evaluation of Hepatic Fibrosis Using Ultrasound Backscattered Radiofrequency Signals and One-Dimensional Convolutional Neural Networks.

The early detection of hepatic fibrosis is of critical importance. Ultrasound backscattered radiofrequency signals from the liver contain abundant information about its microstructure. We proposed a method for characterizing human hepatic fibrosis using one-dimensional convolutional neural networks (CNNs) based on ultrasound backscattered signals.

MRASleepNet: a multi-resolution attention network for sleep stage classification using single-channel EEG.

. Computerized classification of sleep stages based on single-lead electroencephalography (EEG) signals is important, but still challenging. In this paper, we proposed a deep neural network called MRASleepNet for automatic sleep stage classification using single-channel EEG signals.

MAEF-Net: Multi-attention efficient feature fusion network for left ventricular segmentation and quantitative analysis in two-dimensional echocardiography.

The segmentation of cardiac chambers and the quantification of clinical functional metrics in dynamic echocardiography are the keys to the clinical diagnosis of heart disease. Identifying the end-diastolic frames (EDFs) and end-systolic frames (ESFs) and manually segmenting the left ventricle in the echocardiographic cardiac cycle before obtaining the left ventricular ejection fraction (LVEF) is a time-consuming and tedious task for clinicians. In this work, we proposed a deep learning-based fully automated echocardiographic analysis method.

Optimizing a left and right visual field biphasic stimulation paradigm for SSVEP-based BCIs with hairless region behind the ear.

Steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) has the characteristics of fast communication speed, high stability, and wide applicability, thus it has been widely studied. With the rapid development in paradigm, algorithm, and system design, SSVEP-BCI is gradually applied in clinical and real-life scenarios. In order to improve the ease of use of the SSVEP-BCI system, many studies have been focusing on developing it on the hairless area, but due to the lack of redesigning the stimulation paradigm to better adapt to the new area, the electroencephalography response in the hairless area is worse than occipital region.

IRN-MLSQR: An improved iterative reweight norm approach to the inverse problem of electrocardiography incorporating factorization-free preconditioned LSQR.

The inverse problem of electrocardiography (ECG) of computing epicardial potentials from body surface potentials, is an ill-posed problem and needs to be solved by regularization techniques. The L2-norm regularization can cause considerable smoothing of the solution, while the L1-norm scheme promotes a solution with sharp boundaries/gradients between piecewise smooth regions, so L1-norm is widely used in the ECG inverse problem. However, large amount of computation and long computation time are needed in the L1-norm scheme.

A Wearable Electrocardiogram Telemonitoring System for Atrial Fibrillation Detection.

In this paper we proposed a wearable electrocardiogram (ECG) telemonitoring system for atrial fibrillation (AF) detection based on a smartphone and cloud computing. A wearable ECG patch was designed to collect ECG signals and send the signals to an Android smartphone via Bluetooth. An Android APP was developed to display the ECG waveforms in real time and transmit every 30 s ECG data to a remote cloud server.

Detection of atrial fibrillation from ECG recordings using decision tree ensemble with multi-level features.

Objective: Detecting atrial fibrillation (AF) from electrocardiogram (ECG) recordings remains a challenging task. In this paper, a new AF detection method was proposed to classify the ECG recordings into one of four classes: Normal rhythm, AF, Other rhythm, and Noisy recordings.

Approach: The proposed method comprised preprocessing, feature extraction, and classification.

Regression models for near-infrared measurement of subcutaneous adipose tissue thickness.

Obesity is often associated with the risks of diabetes and cardiovascular disease, and there is a need to measure subcutaneous adipose tissue (SAT) thickness for acquiring the distribution of body fat. The present study aimed to develop and evaluate different model-based methods for SAT thickness measurement using an SATmeter developed in our laboratory. Near-infrared signals backscattered from the body surfaces from 40 subjects at 20 body sites each were recorded.

Predicting healthy older adult's brain age based on structural connectivity networks using artificial neural networks.

Brain ageing is followed by changes of the connectivity of white matter (WM) and changes of the grey matter (GM) concentration. Neurodegenerative disease is more vulnerable to an accelerated brain ageing, which is associated with prospective cognitive decline and disease severity. Accurate detection of accelerated ageing based on brain network analysis has a great potential for early interventions designed to hinder atypical brain changes.

Research on an anti-motion interference algorithm of blood oxygen saturation based on AC and DC analysis.

Background: There is an urgent need for blood oxygen saturation (SpO2) tests when participants are ambulatory, as in daily activity monitoring, sleep monitoring, or even athletes' cardiovascular function tests. In such situations, measuring equipment needs to be wearable. This restricts the processor volume, and the corresponding algorithm should be microprocessor compatible.

Space distribution of EEG responses to hanoi-moving visual and auditory stimulation with Fourier Independent Component Analysis.

Background And Objective: The relationship between EEG source signals and action-related visual and auditory stimulation is still not well-understood. The objective of this study was to identify EEG source signals and their associated action-related visual and auditory responses, especially independent components of EEG.

Methods: A hand-moving-Hanoi video paradigm was used to study neural correlates of the action-related visual and auditory information processing determined by mu rhythm (8-12 Hz) in 16 healthy young subjects.

Intensity inhomogeneity correction using N3 on mouse brain magnetic resonance microscopy.

Background And Purpose: Small animal neuroimaging using magnetic resonance microscopy (MRM) has evolved significantly from understanding of imaging physics to widely use today as an important tool in computational neuroanatomy, while how to get optimal inhomogeneity correction for inhomogeneous mouse brain MRM has been given less attention.

Methods: This present study investigates the ability of fine-tuning the nonparametric nonuniform intensity normalization (N3) technique to get optimal inhomogeneity correction of mouse brain MRM. Six mice were scanned on a 7-T scanner with a phased array surface coil of four elements.

Event-related desynchronization (ERD) in the alpha band during a hand mental rotation task.

Recent studies have demonstrated that mentally rotating the hands involves participants engaging in motor imagery processing. However, far less is known about the possible neurophysiological basis of such processing. To contribute to a better understanding of hand mental rotation processing, event-related spectral perturbation (ERSP) methods were applied to electroencephalography (EEG) data collected from participants mentally rotating their hands.

A study of the existing problems of estimating the information transfer rate in online brain-computer interfaces.

Objective: Today, the brain-computer interface (BCI) community lacks a standard method to evaluate an online BCI's performance. Even the most commonly used metric, the information transfer rate (ITR), is often reported differently, even incorrectly, in many papers, which is not conducive to BCI research. This paper aims to point out many of the existing problems and give some suggestions and methods to overcome these problems.

Learning without consciously knowing: evidence from event-related potentials in sequence learning.

This paper investigated how implicit and explicit knowledge is reflected in event-related potentials (ERPs) in sequence learning. ERPs were recorded during a serial reaction time task. The results showed that there were greater RT benefits for standard compared with deviant stimuli later than early on, indicating sequence learning.

A high-speed BCI based on code modulation VEP.

Recently, electroencephalogram-based brain-computer interfaces (BCIs) have attracted much attention in the fields of neural engineering and rehabilitation due to their noninvasiveness. However, the low communication speed of current BCI systems greatly limits their practical application. In this paper, we present a high-speed BCI based on code modulation of visual evoked potentials (c-VEP).

A coded VEP method to measure interhemispheric transfer time (IHTT).

Interhemispheric transfer time (IHTT) is an important parameter for research on the information conduction time across the corpus callosum between the two hemispheres. There are several traditional methods used to estimate the IHTT, including the reaction time (RT) method, the evoked potential (EP) method and the measure based on the transcranial magnetic stimulation (TMS). The present study proposes a novel coded VEP method to estimate the IHTT based on the specific properties of the m-sequence.

A half-field stimulation pattern for SSVEP-based brain-computer interface.

A novel stimulation pattern has been designed for brain-computer interface (BCI) using steady-state visual evoked potential (SSVEP) signals. Each target is composed of two flickers placed on right-and-left visual fields. The user is expected to concentrate his or her sight on the fixation point which is located in the middle of the two flickers modulated at specific frequencies respectively.

An online multi-channel SSVEP-based brain-computer interface using a canonical correlation analysis method.

In recent years, there has been increasing interest in using steady-state visual evoked potential (SSVEP) in brain-computer interface (BCI) systems. However, several aspects of current SSVEP-based BCI systems need improvement, specifically in relation to speed, user variation and ease of use. With these improvements in mind, this paper presents an online multi-channel SSVEP-based BCI system using a canonical correlation analysis (CCA) method for extraction of frequency information associated with the SSVEP.