Parkinson's disease: Cause factors, measurable indicators, and early diagnosis
S Bhat, UR Acharya, Y Hagiwara, N Dadmehr… - Computers in biology …, 2018 - Elsevier
Parkinson's disease (PD) is a neurodegenerative disease of the central nervous system
caused due to the loss of dopaminergic neurons. It is classified under movement disorder as …
caused due to the loss of dopaminergic neurons. It is classified under movement disorder as …
Artificial intelligence techniques for automated diagnosis of neurological disorders
Background: Authors have been advocating the research ideology that a computer-aided
diagnosis (CAD) system trained using lots of patient data and physiological signals and …
diagnosis (CAD) system trained using lots of patient data and physiological signals and …
A deep learning approach for Parkinson's disease diagnosis from EEG signals
An automated detection system for Parkinson's disease (PD) employing the convolutional
neural network (CNN) is proposed in this study. PD is characterized by the gradual …
neural network (CNN) is proposed in this study. PD is characterized by the gradual …
Detection of Parkinson's disease from EEG signals using discrete wavelet transform, different entropy measures, and machine learning techniques
Early detection of Parkinson's disease (PD) is very important in clinical diagnosis for
preventing disease development. In this study, we present efficient discrete wavelet …
preventing disease development. In this study, we present efficient discrete wavelet …
PDCNNet: An automatic framework for the detection of Parkinson's disease using EEG signals
Parkinson's disease (PD) is a neurodegenerative ailment which causes changes in the
neuronal, behavioral, and physiological structures. During the early stages of PD, these …
neuronal, behavioral, and physiological structures. During the early stages of PD, these …
Linear predictive coding distinguishes spectral EEG features of Parkinson's disease
Objective We have developed and validated a novel EEG-based signal processing
approach to distinguish PD and control patients: Linear-predictive-coding EEG Algorithm for …
approach to distinguish PD and control patients: Linear-predictive-coding EEG Algorithm for …
GaborPDNet: Gabor transformation and deep neural network for Parkinson's disease detection using EEG signals
Parkinson's disease (PD) is globally the most common neurodegenerative movement
disorder. It is characterized by a loss of dopaminergic neurons in the substantia nigra of the …
disorder. It is characterized by a loss of dopaminergic neurons in the substantia nigra of the …
Materials chemistry of neural interface technologies and recent advances in three-dimensional systems
Advances in materials chemistry and engineering serve as the basis for multifunctional
neural interfaces that span length scales from individual neurons to neural networks, neural …
neural interfaces that span length scales from individual neurons to neural networks, neural …
EEG analysis of Parkinson's disease using time–frequency analysis and deep learning
R Zhang, J Jia, R Zhang - Biomedical Signal Processing and Control, 2022 - Elsevier
This study proposed two EEG analysis methods for diagnosis and monitoring of Parkinson's
disease. By combining time–frequency analysis with deep learning, tunable Q-factor wavelet …
disease. By combining time–frequency analysis with deep learning, tunable Q-factor wavelet …
EEG-Based Parkinson's Disease Recognition Via Attention-based Sparse Graph Convolutional Neural Network
Parkinson's disease (PD) is a complicated neurological ailment that affects both the physical
and mental wellness of elderly individuals which makes it problematic to diagnose in its …
and mental wellness of elderly individuals which makes it problematic to diagnose in its …