Applications of deep learning techniques for automated multiple sclerosis detection using magnetic resonance imaging: A review
Multiple Sclerosis (MS) is a type of brain disease which causes visual, sensory, and motor
problems for people with a detrimental effect on the functioning of the nervous system. In …
problems for people with a detrimental effect on the functioning of the nervous system. In …
Imaging and machine learning techniques for diagnosis of Alzheimer's disease
Alzheimer's disease (AD) is a common health problem in elderly people. There has been
considerable research toward the diagnosis and early detection of this disease in the past …
considerable research toward the diagnosis and early detection of this disease in the past …
A novel unsupervised deep learning model for global and local health condition assessment of structures
A methodology is described for global and local health condition assessment of structural
systems using ambient vibration response of the structure collected by sensors. The model …
systems using ambient vibration response of the structure collected by sensors. The model …
Fourier–Bessel series expansion based empirical wavelet transform for analysis of non-stationary signals
In this paper, a new method has been presented for the time–frequency (TF) representation
of non-stationary signals. The existing empirical wavelet transform (EWT) has been …
of non-stationary signals. The existing empirical wavelet transform (EWT) has been …
Sparse Bayesian learning for obtaining sparsity of EEG frequency bands based feature vectors in motor imagery classification
Effective common spatial pattern (CSP) feature extraction for motor imagery (MI)
electroencephalogram (EEG) recordings usually depends on the filter band selection to a …
electroencephalogram (EEG) recordings usually depends on the filter band selection to a …
Computer-aided diagnosis of depression using EEG signals
The complex, nonlinear and non-stationary electroencephalogram (EEG) signals are very
tedious to interpret visually and highly difficult to extract the significant features from them …
tedious to interpret visually and highly difficult to extract the significant features from them …
A new music-empirical wavelet transform methodology for time–frequency analysis of noisy nonlinear and non-stationary signals
JP Amezquita-Sanchez, H Adeli - Digital Signal Processing, 2015 - Elsevier
The goal of signal processing is to estimate the contained frequencies and extract subtle
changes in the signals. In this paper, a new adaptive multiple signal classification-empirical …
changes in the signals. In this paper, a new adaptive multiple signal classification-empirical …
Brain functional connectivity patterns for emotional state classification in Parkinson's disease patients without dementia
Successful emotional communication is crucial for social interactions and social
relationships. Parkinson's Disease (PD) patients have shown deficits in emotional …
relationships. Parkinson's Disease (PD) patients have shown deficits in emotional …
New method for modal identification of super high‐rise building structures using discretized synchrosqueezed wavelet and Hilbert transforms
Measured signals obtained by sensors during dynamic events such as earthquake, wind,
and wave contain nonlinear, nonstationary, and noisy properties. In this paper, a new …
and wave contain nonlinear, nonstationary, and noisy properties. In this paper, a new …
Synchrosqueezed wavelet transform-fractality model for locating, detecting, and quantifying damage in smart highrise building structures
JP Amezquita-Sanchez, H Adeli - Smart Materials and Structures, 2015 - iopscience.iop.org
A new methodology is presented for (a) detecting,(b) locating, and (c) quantifying the
damage severity in a smart highrise building structure. The methodology consists of three …
damage severity in a smart highrise building structure. The methodology consists of three …