AIMC Topic: Wavelet Analysis
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A Tunable-Q wavelet transform and quadruple symmetric pattern based EEG signal classification method.
Medical hypotheses
Dec 10, 2019
Electroencephalography (EEG) signals have been widely used to diagnose brain diseases for instance epilepsy, Parkinson's Disease (PD), Multiple Skleroz (MS), and many machine learning methods have been proposed to develop automated disease diagnosis ...
Utilizing wavelet deep learning network to classify different states of task-fMRI for verifying activation regions.
The International journal of neuroscience
Dec 4, 2019
We propose a convolutional neural network (CNN) based on wavelet for verifying the activation regions decided with statistical analysis. Because the functional magnetic resonance imaging (fMRI) data contains lots of noises, it is difficult to get th...
Electroencephalogram based communication system for locked in state person using mentally spelled tasks with optimized network model.
Artificial intelligence in medicine
Nov 19, 2019
Due to growth in population, Individual persons with disabilities are increasing daily. To overcome the disability especially in Locked in State (LIS) due to Spinal Cord Injury (SCI), we planned to design four states moving robot from four imagery ta...
Sex Characteristics
Healthy Volunteers
Brain-Computer Interfaces
Wavelet Analysis
Middle Aged
Electroencephalography
Electrodes
Spinal Cord Injuries
Communication Devices for People with Disabilities
Locked-In Syndrome
Female
Reproducibility of Results
Aged
Aged, 80 and over
Young Adult
Adult
Male
Humans
Neural Networks, Computer
Algorithms
Computer Simulation
Improved detection of Parkinsonian resting tremor with feature engineering and Kalman filtering.
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Nov 5, 2019
OBJECTIVE: Accurate and reliable detection of tremor onset in Parkinson's disease (PD) is critical to the success of adaptive deep brain stimulation (aDBS) therapy. Here, we investigated the potential use of feature engineering and machine learning m...
Two stage residual CNN for texture denoising and structure enhancement on low dose CT image.
Computer methods and programs in biomedicine
Oct 8, 2019
BACKGROUND AND OBJECTIVE: X-ray computed tomography (CT) plays an important role in modern medical science. Human health problems caused by CT radiation have attracted the attention of the academic community widely. Reducing radiation dose results in...
EEG-based single-channel authentication systems with optimum electrode placement for different mental activities.
Biomedical journal
Sep 24, 2019
BACKGROUND: Electroencephalogram (EEG) signals of a brain contain a unique pattern for each person and the potential for biometric applications. Authentication and security is a very important issue in our life and brainwave-based authentication is a...
Epileptic Seizure Detection with EEG Textural Features and Imbalanced Classification Based on EasyEnsemble Learning.
International journal of neural systems
Jul 29, 2019
Imbalance data classification is a challenging task in automatic seizure detection from electroencephalogram (EEG) recordings when the durations of non-seizure periods are much longer than those of seizure activities. An imbalanced learning model is ...
Automatic Cataract Classification Using Deep Neural Network With Discrete State Transition.
IEEE transactions on medical imaging
Jul 11, 2019
Cataract is the clouding of lens, which affects vision and it is the leading cause of blindness in the world's population. Accurate and convenient cataract detection and cataract severity evaluation will improve the situation. Automatic cataract dete...
Ventricular ectopic beat detection using a wavelet transform and a convolutional neural network.
Physiological measurement
Jun 4, 2019
OBJECTIVE: Ventricular contractions in healthy individuals normally follow the contractions of atria to facilitate more efficient pump action and cardiac output. With a ventricular ectopic beat (VEB), volume within the ventricles are pumped to the bo...
Texture-Map-Based Branch-Collaborative Network for Oral Cancer Detection.
IEEE transactions on biomedical circuits and systems
May 22, 2019
The paper proposes an innovative deep convolutional neural network (DCNN) combined with texture map for detecting cancerous regions and marking the ROI in a single model automatically. The proposed DCNN model contains two collaborative branches, name...
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