Parkinson's Disease

Compact Bone Surgery Robot With a High-Resolution and High-Rigidity Remote Center of Motion Mechanism.

OBJECTIVE: Two important and difficult tasks during a bone drilling procedure are guiding the orientation of the drilling axis toward the target and maintaining the orientation against the drilling force. To accomplish these tasks, a remote center of motion (RCM) mechanism is adopted to align the orientation of the drilling axis without changing the entry point. However, existing RCM mechanisms do...

Complexity Measures of Voice Recordings as a Discriminative Tool for Parkinson's Disease.

In this paper, we have investigated the differences in the voices of Parkinson's disease (PD) and age-matched control (CO) subjects when uttering three phonemes using two complexity measures: fractal dimension (FD) and normalised mutual information (NMI). Three sustained phonetic voice recordings, /a/, /u/ and /m/, from 22 CO (mean age = 66.91) and 24 PD (mean age = 71.83) participants were analys...

Identification of distinct blood-based biomarkers in early stage of Parkinson's disease.

Parkinson's disease (PD) is a slowly progressive geriatric disease, which can be one of the leading causes of serious socioeconomic burden in the agin...

A Tunable-Q wavelet transform and quadruple symmetric pattern based EEG signal classification method.

Electroencephalography (EEG) signals have been widely used to diagnose brain diseases for instance epilepsy, Parkinson's Disease (PD), Multiple Sklero...

Comparison of Walking Protocols and Gait Assessment Systems for Machine Learning-Based Classification of Parkinson's Disease.

Early diagnosis of Parkinson's diseases (PD) is challenging; applying machine learning (ML) models to gait characteristics may support the classificat...

Cognitive signature of brain FDG PET based on deep learning: domain transfer from Alzheimer's disease to Parkinson's disease.

PURPOSE: Although functional brain imaging has been used for the early and objective assessment of cognitive dysfunction, there is a lack of generaliz...

A machine learning algorithm successfully screens for Parkinson's in web users.

OBJECTIVE: To develop, apply, and evaluate, a novel web-based classifier for screening for Parkinson disease among a large cohort of search engine use...

Automated Parkinson's disease recognition based on statistical pooling method using acoustic features.

Parkinson's disease is one of the mostly seen neurological disease. It affects to nervous system and hinders people's vital activities. The majority o...

Improved detection of Parkinsonian resting tremor with feature engineering and Kalman filtering.

OBJECTIVE: Accurate and reliable detection of tremor onset in Parkinson's disease (PD) is critical to the success of adaptive deep brain stimulation (...

Quantitative susceptibility mapping based hybrid feature extraction for diagnosis of Parkinson's disease.

Parkinson's disease is the second most common neurodegenerative disease in the elderly after Alzheimer's disease. The aetiology and pathogenesis of Pa...

Machine-learning-derived rules set excludes risk of Parkinson's disease in patients with olfactory or gustatory symptoms with high accuracy.

BACKGROUND: Chemosensory loss is a symptom of Parkinson's disease starting already at preclinical stages. Their appearance without an identifiable eti...

Hash Transformation and Machine Learning-Based Decision-Making Classifier Improved the Accuracy Rate of Automated Parkinson's Disease Screening.

Digitalized hand-drawn pattern is a noninvasive and reproducible assistive manner to obtain hand actions and motions for evaluating functional tremors...

Integrated robotics platform with haptic control differentiates subjects with Parkinson's disease from controls and quantifies the motor effects of levodopa.

BACKGROUND: The use of integrated robotic technology to quantify the spectrum of motor symptoms of Parkinson's Disease (PD) has the potential to facil...

Online identification of functional regions in deep brain stimulation based on an unsupervised random forest with feature selection.

OBJECTIVE: The identification of functional regions, in particular the subthalamic nucleus, through microelectrode recording (MER) is the key step in ...

Investigating Risk Factors and Predicting Complications in Deep Brain Stimulation Surgery with Machine Learning Algorithms.

BACKGROUND: Deep brain stimulation (DBS) surgery is an option for patients experiencing medically resistant neurologic symptoms. DBS complications are...

A Dual-Modal Attention-Enhanced Deep Learning Network for Quantification of Parkinson's Disease Characteristics.

It is well known that most patients with Parkinson's disease (PD) have different degree of movement disorders, such as shuffling, festination and akin...

Automated Detection of Parkinson's Disease Based on Multiple Types of Sustained Phonations Using Linear Discriminant Analysis and Genetically Optimized Neural Network.

OBJECTIVE: Parkinson's disease (PD) is a serious neurodegenerative disorder. It is reported that most of PD patients have voice impairments. But these...

Bag of Samplings for computer-assisted Parkinson's disease diagnosis based on Recurrent Neural Networks.

Parkinson's Disease (PD) is a clinical syndrome that affects millions of people worldwide. Although considered as a non-lethal disease, PD shortens th...

Multilevel Features for Sensor-Based Assessment of Motor Fluctuation in Parkinson's Disease Subjects.

Motor fluctuations are a frequent complication in patients with Parkinson's disease (PD) where the response to medication fluctuates between ON states...

Phasic dopamine release identification using convolutional neural network.

Dopamine has a major behavioral impact related to drug dependence, learning and memory functions, as well as pathologies such as schizophrenia and Par...