A new music-empirical wavelet transform methodology for time–frequency analysis of noisy nonlinear and non-stationary signals

Amezquita-Sanchez, Juan P.; Adeli, Hojjat

doi:10.1016/j.dsp.2015.06.013

Cited by 157 publications

(80 citation statements)

References 41 publications

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“…In this approach, the electrical brain activities are recorded in the form of frequencies and analyzed using signal processing techniques (Adeli and Ghosh-Dastidar, 2010;Amezquita-Sanchez and Adeli, 2015) to detect the brain abnormalities. This technique is based on both spatial and temporal resolutions (higher temporal resolution compared with neuroimaging) and can potentially identify dementia even in early stages (Claus et al, 2000;Henderson et al, 2006;Davidson et al, 2007).…”

Section: Neurophysiological Biomarkersmentioning

confidence: 99%

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

Mirzaei

Adeli

2016

Reviews in the Neurosciences

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110

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AbstractAlzheimer’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 decade. The sensitivity of biomarkers and the accuracy of the detection techniques have been defined to be the key to an accurate diagnosis. This paper presents a state-of-the-art review of the research performed on the diagnosis of AD based on imaging and machine learning techniques. Different segmentation and machine learning techniques used for the diagnosis of AD are reviewed including thresholding, supervised and unsupervised learning, probabilistic techniques, Atlas-based approaches, and fusion of different image modalities. More recent and powerful classification techniques such as the enhanced probabilistic neural network of Ahmadlou and Adeli should be investigated with the goal of improving the diagnosis accuracy. A combination of different image modalities can help improve the diagnosis accuracy rate. Research is needed on the combination of modalities to discover multi-modal biomarkers.

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Section: Neurophysiological Biomarkersmentioning

confidence: 99%

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

Mirzaei

Adeli

2016

Reviews in the Neurosciences

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110

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“…In WT, a predefined wavelet basis, called mother wavelet, is used to represent a time-series signal, akin to cosines and sines used as the basis of the Fourier transform (FT) (Adeli and Karim, 2005; Adeli and Ghosh-Dastidar, 2010). A challenge in WT is that mother wavelets often do not share similarities with the signals being processed thus impacting the overall effectiveness of the technique (Amezquita-Sanchez and Adeli, 2015;Gilles, 2013). Researchers rarely justify the selection of the mother wavelet and those who do justify the selection based on superficial similarities with the signal (Rafiee et al, 2009;Shalchyan et al, 2012).…”

Section: Methodsmentioning

confidence: 99%

MUSIC-Expected maximization gaussian mixture methodology for clustering and detection of task-related neuronal firing rates

Ortiz-Rosario

Adeli

Buford

2017

Behavioural Brain Research

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Researchers often rely on simple methods to identify involvement of neurons in a particular motor task. The historical approach has been to inspect large groups of neurons and subjectively separate neurons into groups based on the expertise of the investigator. In cases where neuron populations are small it is reasonable to inspect these neuronal recordings and their firing rates carefully to avoid data omissions. In this paper, a new methodology is presented for automatic objective classification of neurons recorded in association with behavioral tasks into groups. By identifying characteristics of neurons in a particular group, the investigator can then identify functional classes of neurons based on their relationship to the task. The methodology is based on integration of a multiple signal classification (MUSIC) algorithm to extract relevant features from the firing rate and an expectation-maximization Gaussian mixture algorithm (EM-GMM) to cluster the extracted features. The methodology is capable of identifying and clustering similar firing rate profiles automatically based on specific signal features. An empirical wavelet transform (EWT) was used to validate the features found in the MUSIC pseudospectrum and the resulting signal features captured by the methodology. Additionally, this methodology was used to inspect behavioral elements of neurons to physiologically validate the model. This methodology was tested using a set of data collected from awake behaving non-human primates.

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“…The main difference among time-frequency methods is the way they handle the problem of uncertainty. Timefrequency methods have been used in various works for nonstationary signal analysis [24,25] and for seizure detection [26,27].…”

Section: Feature Extractionmentioning

confidence: 99%

A Multistage System for Automatic Detection of Epileptic Spikes

Anh-Dao¹,

Linh-Trung

Nguyen

et al. 2018

REV J. Electron. Commun.

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Abstract-A multistage automatic detection system for epileptic spikes is introduced as an assistant tool for epileptic analysis and diagnosis based on electroencephalogram (EEG). The system consists of four stages: preprocessing, feature extraction, classifier and expert system. Multiple state-of-the-art signal processing and machine learning techniques including wavelet transform, spectral filtering, artificial neural network are utilized in order to improve the ability of the overall system, stage by stage. Compared to other works, our contributions are three-fold: peaks in the EEG recording are categorized into two groups of non-epileptic spikes and possible epileptic spikes by a committee of three perceptrons; appropriate mother wavelet and wavelet scales are selected for the best system performance; and, based on the neurological fact that an epileptic spike is usually followed by a slow wave, a simple expert system is presented to eliminate pseudo-spikes which are closely analogous to true epileptic spikes. Experimental results show that the proposed system is capable of detecting epileptic spikes efficiently.

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A new music-empirical wavelet transform methodology for time–frequency analysis of noisy nonlinear and non-stationary signals

Cited by 157 publications

References 41 publications

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

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

MUSIC-Expected maximization gaussian mixture methodology for clustering and detection of task-related neuronal firing rates

A Multistage System for Automatic Detection of Epileptic Spikes

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