Neurology

Modelling prognostic trajectories of cognitive decline due to Alzheimer's disease.

Alzheimer's disease (AD) is characterised by a dynamic process of neurocognitive changes from normal cognition to mild cognitive impairment (MCI) and progression to dementia. However, not all individuals with MCI develop dementia. Predicting whether individuals with MCI will decline (i.e. progressive MCI) or remain stable (i.e. stable MCI) is impeded by patient heterogeneity due to comorbidities t...

EEG based multi-class seizure type classification using convolutional neural network and transfer learning.

Recognition of epileptic seizure type is essential for the neurosurgeon to understand the cortical connectivity of the brain. Though automated early recognition of seizures from normal electroencephalogram (EEG) was existing, no attempts have been made towards the classification of variants of seizures. Therefore, this study attempts to classify seven variants of seizures with non-seizure EEG thro...

Directed EEG neural network analysis by LAPPS (p≤1) Penalized sparse Granger approach.

The conventional multivariate Granger Analysis (GA) of directed interactions has been widely applied in brain network construction based on EEG record...

Machine learning validation of EEG+tACS artefact removal.

OBJECTIVE: Electroencephalography (EEG) recorded during transcranial alternating current simulation (tACS) is highly desirable in order to investigate...

Big Data Analytics and Sensor-Enhanced Activity Management to Improve Effectiveness and Efficiency of Outpatient Medical Rehabilitation.

Numerous societal trends are compelling a transition from inpatient to outpatient venues of care for medical rehabilitation. While there are advantage...

Improvement of classification performance of Parkinson's disease using shape features for machine learning on dopamine transporter single photon emission computed tomography.

OBJECTIVE: To assess the classification performance between Parkinson's disease (PD) and normal control (NC) when semi-quantitative indicators and sha...

Shaping technologies for older adults with and without dementia: Reflections on ethics and preferences.

As a result of several years of European funding, progressive introduction of assistive technologies in our society has provided many researchers and ...

Control and Ownership of Neuroprosthetic Speech.

Implantable brain-computer interfaces (BCIs) are being developed to restore speech capacity for those who are unable to speak. Patients with locked-in...

A Novel Deep Learning Approach with a 3D Convolutional Ladder Network for Differential Diagnosis of Idiopathic Normal Pressure Hydrocephalus and Alzheimer's Disease.

PURPOSE: Idiopathic normal pressure hydrocephalus (iNPH) and Alzheimer's disease (AD) are geriatric diseases and common causes of dementia. Recently, ...

Identifying epilepsy psychiatric comorbidities with machine learning.

OBJECTIVE: People with epilepsy are at increased risk for mental health comorbidities. Machine-learning methods based on spoken language can detect su...

A Multi-Omics Interpretable Machine Learning Model Reveals Modes of Action of Small Molecules.

High-throughput screening and gene signature analyses frequently identify lead therapeutic compounds with unknown modes of action (MoAs), and the resu...

Prognostic factors of Rapid symptoms progression in patients with newly diagnosed parkinson's disease.

Tracking symptoms progression in the early stages of Parkinson's disease (PD) is a laborious endeavor as the disease can be expressed with vastly diff...

Intra- and Inter-subject Variability in EEG-Based Sensorimotor Brain Computer Interface: A Review.

Brain computer interfaces (BCI) for the rehabilitation of motor impairments exploit sensorimotor rhythms (SMR) in the electroencephalogram (EEG). Howe...

Plasma and Serum Alpha-Synuclein as a Biomarker of Diagnosis in Patients With Parkinson's Disease.

Parkinson's disease (PD) is the second most common neurodegenerative disease, and α-synuclein plays a critical role in the pathogenesis of PD. Studie...

A survey on machine and statistical learning for longitudinal analysis of neuroimaging data in Alzheimer's disease.

BACKGROUND AND OBJECTIVES: Recently, longitudinal studies of Alzheimer's disease have gathered a substantial amount of neuroimaging data. New methods ...

MNT-DeepSL: Median nerve tracking from carpal tunnel ultrasound images with deep similarity learning and analysis on continuous wrist motions.

Carpal tunnel syndrome (CTS) is a clinical disease that caused by the compression of median nerve within carpal tunnel. Traditional examining for CTS ...

Self-calibrated brain network estimation and joint non-convex multi-task learning for identification of early Alzheimer's disease.

Detection of early stages of Alzheimer's disease (AD) (i.e., mild cognitive impairment (MCI)) is important to maximize the chances to delay or prevent...

Ambivert degree identifies crucial brain functional hubs and improves detection of Alzheimer's Disease and Autism Spectrum Disorder.

Functional modules in the human brain support its drive for specialization whereas brain hubs act as focal points for information integration. Brain h...

Adaptive Ankle Resistance from a Wearable Robotic Device to Improve Muscle Recruitment in Cerebral Palsy.

Individuals with cerebral palsy can have weak and poorly coordinated ankle plantar flexor muscles that contribute to inefficient walking patterns. Pre...

Competitive Learning in a Spiking Neural Network: Towards an Intelligent Pattern Classifier.

One of the modern trends in the design of human-machine interfaces (HMI) is to involve the so called spiking neuron networks (SNNs) in signal processi...