Neurology

Conditional generative adversarial networks applied to EEG data can inform about the inter-relation of antagonistic behaviors on a neural level.

Goal-directed actions frequently require a balance between antagonistic processes (e.g., executing and inhibiting a response), often showing an interdependency concerning what constitutes goal-directed behavior. While an inter-dependency of antagonistic actions is well described at a behavioral level, a possible inter-dependency of underlying processes at a neuronal level is still enigmatic. Howev...

Characterizing Brain Signals for Epileptic Pre-ictal Signal Classification.

Epilepsy is a kind of neurological disorder characterized by recurrent epileptic seizures. While it is crucial to characterize pre-ictal brain electrical activities, the problem to this day still remains computationally challenging. Using brain signal acquisition and advances in deep learning technology, we aim to classify pre-ictal signals and characterize the brain waveforms of patients with epi...

A Federated Mining Approach on Predicting Diabetes-Related Complications: Demonstration Using Real-World Clinical Data.

Chronic diabetes can lead to microvascular complications, including diabetic eye disease, diabetic kidney disease, and diabetic neuropathy. However, t...

Predicting Motor Responsiveness to Deep Brain Stimulation with Machine Learning.

Deep brain stimulation is a complex movement disorder intervention that requires highly invasive brain surgery. Clinicians struggle to predict how pat...

An EEG Classification-Based Method for Single-Trial N170 Latency Detection and Estimation.

Event-related potentials (ERPs) can reflect the high-level thinking activities of the brain. In ERP analysis, the superposition and averaging method i...

Machine learning classification of schizophrenia patients and healthy controls using diverse neuroanatomical markers and Ensemble methods.

Schizophrenia is a major psychiatric disorder that imposes enormous clinical burden on patients and their caregivers. Determining classification bioma...

A Hybrid Arm-Hand Rehabilitation Robot With EMG-Based Admittance Controller.

Reach-and-grasp is one of the most fundamental activities in daily life, while few rehabilitation robots provide integrated and active training of the...

Detection of microscopic glaucoma through fundus images using deep transfer learning approach.

Glaucoma disease in humans can lead to blindness if it progresses to the point where it affects the oculus' optic nerve head. It is not easily detecte...

Automatic classification of nerve discharge rhythms based on sparse auto-encoder and time series feature.

BACKGROUND: Nerve discharge is the carrier of information transmission, which can reveal the basic rules of various nerve activities. Recognition of t...

Towards a universal and privacy preserving EEG-based authentication system.

EEG-based authentication has gained much interest in recent years. However, despite its growing appeal, there are still various challenges to their pr...

A deep learning-based toolbox for Automated Limb Motion Analysis (ALMA) in murine models of neurological disorders.

In neuroscience research, the refined analysis of rodent locomotion is complex and cumbersome, and access to the technique is limited because of the n...

Epileptic Seizure Detection with Hybrid Time-Frequency EEG Input: A Deep Learning Approach.

The precise detection of epileptic seizure helps to prevent the serious consequences of seizures. As the electroencephalogram (EEG) reflects the brain...

Research on Emotion Recognition of EEG Signal Based on Convolutional Neural Networks and High-Order Cross-Analysis.

Emotion recognition means the automatic identification of a human's emotional state by obtaining his/her physiological or nonphysiological signals. Th...

Antemortem detection of Parkinson's disease pathology in peripheral biopsies using artificial intelligence.

The diagnosis of Parkinson's disease (PD) is challenging at all stages due to variable symptomatology, comorbidities, and mimicking conditions. Postmo...

Predictive classification of Alzheimer's disease using brain imaging and genetic data.

For now, Alzheimer's disease (AD) is incurable. But if it can be diagnosed early, the correct treatment can be used to delay the disease. Most of the ...

Artificial intelligence in positioning between mandibular third molar and inferior alveolar nerve on panoramic radiography.

Determining the exact positional relationship between mandibular third molar (M3) and inferior alveolar nerve (IAN) is important for surgical extracti...

Machine Learning Techniques in Blood Pressure Management During the Acute Phase of Ischemic Stroke.

BACKGROUND AND PURPOSE: Elevated blood pressure (BP) in acute ischemic stroke is common. A raised BP is related to mortality and disability, yet exces...

To Study the Role of Perfusion Index as a Predictor of Hypotension during Spinal Anesthesia in Lower Segment Cesarean Section - A Prospective Observational Study.

BACKGROUND AND AIMS: Spinal anesthesia has become the choice of neuraxial anesthesia for cesarean delivery. Hypotension, the most common side effect o...

Hammerstein-Wiener Multimodel Approach for Fast and Efficient Muscle Force Estimation from EMG Signals.

This paper develops a novel approach to characterise muscle force from electromyography (EMG) signals, which are the electric activities generated by ...

Abnormal Degree Centrality as a Potential Imaging Biomarker for Right Temporal Lobe Epilepsy: A Resting-state Functional Magnetic Resonance Imaging Study and Support Vector Machine Analysis.

Previous studies have reported altered neuroimaging features in right temporal lobe epilepsy (rTLE). However, the alterations in degree centrality (DC...