Seizures

Transcriptomic analysis and machine learning have identified shared diagnostic genes and a possible mechanism linking bipolar disorder and epilepsy.

It is well known that bipolar disorder (BD) and epilepsy (EP) are common neurological diseases. The objective of this study was to screen for potential biomarkers applicable to the diagnosis of EP and BD. The gene expression profiles from both the BD and EP datasets were sourced from the Gene Expression Omnibus database. To pinpoint the core shared genes, we conducted differential expression analy...

Inferring preoperative cognitive function from intraoperative electroencephalography in elderly patients using machine learning.

To develop and evaluate machine learning (ML) models that infer preoperative cognitive function from intraoperative electroencephalography (EEG). This was a retrospective ML study that used a training dataset derived from the MINDDS study (306 patients, USA), and an external testing dataset from the Electroencephalographic Biomarker to Predict Acute Post-Operatory Cognitive Dysfunction study (92 p...

Differential quadruple pattern: A new EEG signal classification framework.

EEG signals are the letters of the brain and reflect neural activity. Abnormal EEG patterns indicate brain disorders such as epilepsy. Recently, machi...

Time-frequency-spatial channel attention network for semantic decoding: an exploratory EEG study.

Semantic decoding is a crucial approach for investigating the neural mechanisms underlying language processing and representation. Informed by brain-c...

Cognitive reinforcement: capturing tacit knowledge and enhancing expertise with a biofeedback interface for visual attention.

Objective.Tacit or implicit knowledge refers to know-how that experts possess but often cannot articulate, codify, or explicitly transfer to others. T...

STAND-Net: A Spiking Temporal Attention autoeNcoDer Network for Efficient EEG Artifact Removal.

Electroencephalography (EEG)-based brain computer interface (BCI) systems hold significant promise across diverse applications; however, their perform...

Adaptive Segmentation of EEG for Machine Learning Applications.

OBJECTIVE: Electroencephalography (EEG) data is derived by sampling continuous neurological time series signals. In order to prepare EEG signals for m...

Interpretable EEG biomarkers for neurological disease models in mice using bag-of-waves classifiers.

OBJECTIVE: Electroencephalograms (EEGs) are time-series records of the electrical potential from collective neural activity in the brain. EEG waveform...

A CNN-transformer fusion for EEG-based discrimination of Alzheimer's and frontotemporal dementia.

Dementia is a progressive neurodegenerative disorder that severely impacts cognitive functions and daily living, especially in aging populations. Amon...

WGB-GLFI: A Novel Graph-Based Global-Local Feature Interaction Framework for Automated Seizure Detection.

Epilepsy detection faces significant challenges due to unpredictable seizures, ranging from brief awareness lapses to severe convulsions, posing risks...

Multimodal Wearable Sensor-Based Stress Detection: Machine Learning Pipeline with Systematic Feature Selection and Key Biomarker Insights.

The increasing awareness of stress-related health impacts has driven demand for accurate, non-invasive stress detection methods, particularly those le...

Cannabidiol inhibits both human KV7.1 and KV7.1/KCNE1 channels through distinct sites.

Several essential physiological systems express voltage-gated potassium channels within the KV7 family (comprising KV7.1-7.5), sometimes also co-assem...

A novel framework for cognitive state identification using resting-state EEG.

Recent advancements in cognitive impairment research have led to significant progress. Electroencephalography (EEG)-based cognitive state identificati...

The research progress of wearable digital health technologies in epilepsy management.

Epilepsy is one of the most common neurological disorders, characterized by recurrent, unpredictable seizures. Due to the unpredictability of seizures...

Automatic EEG artifact detection using a local-global feature fusion network in time and time-frequency domains.

BACKGROUND: Video electroencephalographies (VEEGs) are often affected by artifacts, which can diminish clinicians' efficiency in interpreting VEEG dat...

Predicting seizure-free outcomes in people with treatment-resistant epilepsy: A machine learning approach.

BACKGROUND: Epilepsy surgery is an important intervention for treatment-resistant epilepsy, butthe ability to predict long-term seizure freedom post-s...

Automatic sleep scoring for real-time monitoring and stimulation in individuals with and without sleep apnea.

Digital therapeutics, enabled by advanced machine learning algorithms and medical wearable devices, offer a promising approach to streamline diagnosti...

DoC-Informer: Automated Discrimination of Disorders of Consciousness under Adaptive EEG Settings.

OBJECTIVE: Disorders of consciousness (DoC) diagnosis critically depends on accurate state discrimination to guide treatment and prognosis. Current EE...

Neuroimaging-Based Deep Learning Applications for Lesion Detection and Predicting the Outcome Following Epilepsy Surgery.

Neuroimaging studies are essential for evaluating patients with drug-resistant focal epilepsy and determining their candidacy for epilepsy surgery. Th...

Task-evoked brain network dynamics underlying cognitive control using source-localized EEG and machine learning.

OBJECTIVE: Functional connectivity (FC) coordinates brain activity during cognitive tasks, yet the influence of demographic variables and health facto...