Neurology

CwA-T: A Channelwise AutoEncoder with Transformer for EEG Abnormality Detection

Electroencephalogram (EEG) signals are critical for detecting abnormal brain activity, but their high dimensionality and complexity pose significant challenges for effective analysis. In this paper, we propose CwA-T, a novel framework that combines a channelwise CNN-based autoencoder with a single-head transformer classifier for efficient EEG abnormality detection. The channelwise autoencoder co...

Accurate and Efficient Algorithm for Detection of Alzheimer Disability Based on Deep Learning.

BACKGROUND/AIMS: Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that severely affects cognitive functions and memory. Early detection is crucial for timely intervention and improved patient outcomes. However, traditional diagnostic tools, such as MRI and PET scans, are costly and less accessible. This study aims to develop an automated, cost-effective digital diagnostic appro...

Identification of Epileptic Spasms (ESES) Phases Using EEG Signals: A Vision Transformer Approach

This work introduces a new approach to the Epileptic Spasms (ESES) detection based on the EEG signals using Vision Transformers (ViT). Classic ESES ...

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search

Creation and curation of knowledge graphs can accelerate disease discovery and analysis in real-world data. While disease ontologies aid in biologic...

Efficacy of Temporal Interference Electrical Stimulation for Spinal Cord Injury Rehabilitation: A Case Series

Spinal cord injury (SCI) is a debilitating condition that often results in significant motor and sensory deficits, impacting the quality of life. Cu...

EEG-GMACN: Interpretable EEG Graph Mutual Attention Convolutional Network

Electroencephalogram (EEG) is a valuable technique to record brain electrical activity through electrodes placed on the scalp. Analyzing EEG signals...

Detecting Daily Living Gait Amid Huntington's Disease Chorea using a Foundation Deep Learning Model

Wearable sensors offer a non-invasive way to collect physical activity (PA) data, with walking as a key component. Existing models often struggle to...

segcsvd: A Convolutional Neural Network-Based Tool for Quantifying White Matter Hyperintensities in Heterogeneous Patient Cohorts.

White matter hyperintensities (WMH) of presumed vascular origin are a magnetic resonance imaging (MRI)-based biomarker of cerebral small vessel diseas...

Detecting Activities of Daily Living in Egocentric Video to Contextualize Hand Use at Home in Outpatient Neurorehabilitation Settings

Wearable egocentric cameras and machine learning have the potential to provide clinicians with a more nuanced understanding of patient hand use at h...

CognitionCapturer: Decoding Visual Stimuli From Human EEG Signal With Multimodal Information

Electroencephalogram (EEG) signals have attracted significant attention from researchers due to their non-invasive nature and high temporal sensitiv...

Data Integration with Fusion Searchlight: Classifying Brain States from Resting-state fMRI

Resting-state fMRI captures spontaneous neural activity characterized by complex spatiotemporal dynamics. Various metrics, such as local and global ...

Detecting Cognitive Impairment and Psychological Well-being among Older Adults Using Facial, Acoustic, Linguistic, and Cardiovascular Patterns Derived from Remote Conversations

The aging society urgently requires scalable methods to monitor cognitive decline and identify social and psychological factors indicative of dement...

A comprehensive interpretable machine learning framework for Mild Cognitive Impairment and Alzheimer's disease diagnosis

An interpretable machine learning (ML) framework is introduced to enhance the diagnosis of Mild Cognitive Impairment (MCI) and Alzheimer's disease (...

Motor Imagery Classification for Asynchronous EEG-Based Brain-Computer Interfaces

Motor imagery (MI) based brain-computer interfaces (BCIs) enable the direct control of external devices through the imagined movements of various bo...

LV-CadeNet: Long View Feature Convolution-Attention Fusion Encoder-Decoder Network for Clinical MEG Spike Detection

It is widely acknowledged that the epileptic foci can be pinpointed by source localizing interictal epileptic discharges (IEDs) via Magnetoencephalo...

Graph convolutional networks enable fast hemorrhagic stroke monitoring with electrical impedance tomography

Objective: To develop a fast image reconstruction method for stroke monitoring with electrical impedance tomography with image quality comparable to...

Comparative Analysis of Deep Learning Approaches for Harmful Brain Activity Detection Using EEG

The classification of harmful brain activities, such as seizures and periodic discharges, play a vital role in neurocritical care, enabling timely d...

SKIPNet: Spatial Attention Skip Connections for Enhanced Brain Tumor Classification

Early detection of brain tumors through magnetic resonance imaging (MRI) is essential for timely treatment, yet access to diagnostic facilities rema...

Investigation of in vitro neuronal activity processing using a CMOS-integrated ZrO2-based memristive crossbar

The influence of the epileptiform neuronal activity on the response of a CMOS-integrated ZrO2-based memristive crossbar and its conductivity was stu...

CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding

Electroencephalography (EEG) is a non-invasive technique to measure and record brain electrical activity, widely used in various BCI and healthcare ...