Parkinson's Disease

Voice analysis in Parkinson's disease - a systematic literature review.

BACKGROUND AND AIM: Parkinson's disease is a neurodegenerative disease. It is often diagnosed at an advanced stage, which can influence the control over the illness. Therefore, the possibility of diagnosing Parkinson's disease at an earlier stage, and possibly prognosticate it, could be an advantage. Given this, a literature review that covers current studies in the field is relevant.

Freezing of gait detection: The effect of sensor type, position, activities, datasets, and machine learning model.

BackgroundFreezing of gait (FoG) is a complex, frequent, and disabling motor symptom of Parkinson's disease (PD). Wearable technology has the potential to improve FoG assessment by providing objective, quantitative, and continuous monitoring.ObjectiveThis study aims to develop a robust FoG detection algorithm that can be embedded in a simple and unobtrusive wearable sensor system and can lead to a...

A Novel Fusion Framework Combining Graph Embedding Class-Based Convolutional Recurrent Attention Network with Brown Bear Optimization Algorithm for EEG-Based Parkinson's Disease Recognition.

Parkinson's disease recognition (PDR) involves identifying Parkinson's disease using clinical evaluations, imaging studies, and biomarkers, focusing o...

A comprehensive review of neurotransmitter modulation via artificial intelligence: A new frontier in personalized neurobiochemistry.

The deployment of artificial intelligence (AI) is revolutionizing neuropharmacology and drug development, allowing the modulation of neurotransmitter ...

The Role of Machine Learning in Cognitive Impairment in Parkinson Disease: Systematic Review and Meta-Analysis.

BACKGROUND: Parkinson disease (PD) is a common neurodegenerative disease characterized by both motor and nonmotor symptoms. Cognitive impairment often...

Enhancing parkinson disease detection through feature based deep learning with autoencoders and neural networks.

Parkinson's disease is a neurodegenerative disorder that is associated with aging, leading to the progressive deterioration of certain regions of the ...

The More, the Better? Evaluating the Role of EEG Preprocessing for Deep Learning Applications.

The last decade has witnessed a notable surge in deep learning applications for electroencephalography (EEG) data analysis, showing promising improvem...

Item Response Modeling and Artificial Neural Network for Differentiation of Parkinson's Patients and Subjects Without Evidence of Dopaminergic Deficit.

Approximately 15% of patients suspected of having Parkinson's disease (PD) present dopamine active transporter (DaT) scans without evidence of dopamin...

Hybrid ladybug Hawk optimization-enabled deep learning for multimodal Parkinson's disease classification using voice signals and hand-drawn images.

PD is a progressive neurodegenerative disorder that leads to gradual motor impairments. Early detection is critical for slowing the disease's progress...

Deep learning models for improving Parkinson's disease management regarding disease stage, motor disability and quality of life.

BACKGROUND AND OBJECTIVE: Motor diagnosis, monitoring and management of Parkinson's disease (PD) focuses mainly on observational methods and, clinical...

Deep Learning-Based Diagnostic Model for Parkinson's Disease Using Handwritten Spiral and Wave Images.

OBJECTIVE: To develop and validate a deep neural network (DNN) model for diagnosing Parkinson's Disease (PD) using handwritten spiral and wave images,...

Pre-trained convolutional neural networks identify Parkinson's disease from spectrogram images of voice samples.

Machine learning approaches including deep learning models have shown promising performance in the automatic detection of Parkinson's disease. These a...

Interpretable modality-specific and interactive graph convolutional network on brain functional and structural connectomes.

Both brain functional connectivity (FC) and structural connectivity (SC) provide distinct neural mechanisms for cognition and neurological disease. In...

Comprehensive clinical scale-based machine learning model for predicting subthalamic nucleus deep brain stimulation outcomes in Parkinson's disease.

Parkinson's Disease (PD) is a growing burden with varied clinical manifestations and responses to Subthalamic Nucleus Deep Brain Stimulation (STN-DBS)...

Deep brain stimulation of the nucleus basalis of Meynert in neurodegenerative diseases with cognitive impairment: An update on evidence and mechanisms.

Current pharmacotherapy for neurodegenerative diseases is limited to providing symptomatic relief, instead of slowing or reversing disease progression...

Electroencephalogram (EEG) Based Fuzzy Logic and Spiking Neural Networks (FLSNN) for Advanced Multiple Neurological Disorder Diagnosis.

Neurological disorders are a major global health concern that have a substantial impact on death rates and quality of life. accurately identifying a n...

Parkinson's disease tremor prediction towards real-time suppression: A self-attention deep temporal convolutional network approach.

Accurate prediction of Parkinson's disease tremor (PDT) is crucial for developing assistive technologies; however, this is challenging due to the nonl...

Interpretation of basal nuclei in brain dopamine transporter scans using a deep convolutional neural network.

OBJECTIVE: Functional imaging using the dopamine transporter (DAT) as a biomarker has proven effective in assessing dopaminergic neuron degeneration i...

Sway frequencies may predict postural instability in Parkinson's disease: a novel convolutional neural network approach.

BACKGROUND: Postural instability greatly reduces quality of life in people with Parkinson's disease (PD). Early and objective detection of postural im...

Efficient Neural Network Classification of Parkinson's Disease and Schizophrenia Using Resting-State EEG Data.

Timely identification of Parkinson's disease and schizophrenia is crucial for the effective management and enhancement of patients' quality of life. T...