Applications of deep learning techniques for automated multiple sclerosis detection using magnetic resonance imaging: A review

A Shoeibi, M Khodatars, M Jafari, P Moridian… - Computers in Biology …, 2021 - Elsevier
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 …

Imaging and machine learning techniques for diagnosis of Alzheimer's disease

G Mirzaei, A Adeli, H Adeli - Reviews in the Neurosciences, 2016 - degruyter.com
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 …

A novel unsupervised deep learning model for global and local health condition assessment of structures

MH Rafiei, H Adeli - Engineering Structures, 2018 - Elsevier
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 …

Fourier–Bessel series expansion based empirical wavelet transform for analysis of non-stationary signals

A Bhattacharyya, L Singh, RB Pachori - Digital Signal Processing, 2018 - Elsevier
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 …

Sparse Bayesian learning for obtaining sparsity of EEG frequency bands based feature vectors in motor imagery classification

Y Zhang, Y Wang, J Jin, X Wang - International journal of neural …, 2017 - World Scientific
Effective common spatial pattern (CSP) feature extraction for motor imagery (MI)
electroencephalogram (EEG) recordings usually depends on the filter band selection to a …

Computer-aided diagnosis of depression using EEG signals

UR Acharya, VK Sudarshan, H Adeli, J Santhosh… - European …, 2015 - karger.com
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 …

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 …

Brain functional connectivity patterns for emotional state classification in Parkinson's disease patients without dementia

R Yuvaraj, M Murugappan, UR Acharya, H Adeli… - Behavioural brain …, 2016 - Elsevier
Successful emotional communication is crucial for social interactions and social
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

Z Li, HS Park, H Adeli - The Structural Design of Tall and …, 2017 - Wiley Online Library
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 …

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 …