Results 1 - 10 of 120

Removing Electroencephalographic Artifacts: Comparison between ICA and PCA

by Tzyy-Ping Jung, Colin Humphries, Te-won Lee, Scott Makeig, Martin J. Mckeown, Vicente Iragui, Terrence J. Sejnowski , 1998
"... Pervasive electroencephalographic (EEG) artifacts associated with blinks, eye-movements, muscle noise, cardiac signals, and line noise poses a major challenge for EEG interpretation and analysis. Here, we propose a generally applicable method for removing a wide variety of artifacts from EEG records ..."
Abstract - Cited by 240 (22 self) - Add to MetaCart
Pervasive electroencephalographic (EEG) artifacts associated with blinks, eye-movements, muscle noise, cardiac signals, and line noise poses a major challenge for EEG interpretation and analysis. Here, we propose a generally applicable method for removing a wide variety of artifacts from EEG

Diagnosis of alzheimers disease from EEG signals: Where are we standing

by Justin Dauwels, François Vialatte, Andrzej Cichocki - Current Alzheimer Research
"... This paper reviews recent progress in the diagnosis of Alzheimer’s disease (AD) from electroencephalograms (EEG). Three major effects of AD on EEG have been observed: slowing of the EEG, reduced complexity of the EEG signals, and perturbations in EEG synchrony. In recent years, a variety of sophisti ..."
Abstract - Cited by 24 (11 self) - Add to MetaCart
This paper reviews recent progress in the diagnosis of Alzheimer’s disease (AD) from electroencephalograms (EEG). Three major effects of AD on EEG have been observed: slowing of the EEG, reduced complexity of the EEG signals, and perturbations in EEG synchrony. In recent years, a variety

Analysis of EEG signals using data mining approach

by V V Ramalingam , S Ganesh , V Sugumaran - Int. J. Comput. Eng. Technol , 2012
"... ABSTRACT Application of data mining in medical domain is constantly increasing for diagnosis of disease. It has been used by many researchers for analysis of ECG signals for interpreting heart related problems. Decoding brains activities have been a challenging task for researchers for a long time. ..."
Abstract - Cited by 2 (0 self) - Add to MetaCart
ABSTRACT Application of data mining in medical domain is constantly increasing for diagnosis of disease. It has been used by many researchers for analysis of ECG signals for interpreting heart related problems. Decoding brains activities have been a challenging task for researchers for a long time

On the Early Diagnosis of Alzheimer’s Disease from EEG Signals: A Mini-Review

by Justin Dauwels, François-benoît Vialatte, Andrzej Cichocki
"... Abstract. In recent years, various computational approaches have been proposed to diagnose Alzheimer’s disease (AD) from EEG recordings. In this paper, we review some of those approaches, and discuss their limitations and potential. 1 ..."
Abstract - Cited by 1 (1 self) - Add to MetaCart
Abstract. In recent years, various computational approaches have been proposed to diagnose Alzheimer’s disease (AD) from EEG recordings. In this paper, we review some of those approaches, and discuss their limitations and potential. 1

Optimization of EEG Frequency Bands for Improved Diagnosis of Alzheimer Disease

by Mohamed Elgendi, Student Member, Francois Vialatte, Andrzej Cichocki, Jaesung Jeong, Justin Dauwels
"... Abstract—Many clinical studies have shown that electroencephalograms (EEG) of Alzheimer patients (AD) often have an abnormal power spectrum. In this paper a frequency band analysis of AD EEG signals is presented, with the aim of improving the diagnosis of AD from EEG signals. Relative power in diffe ..."
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Abstract—Many clinical studies have shown that electroencephalograms (EEG) of Alzheimer patients (AD) often have an abnormal power spectrum. In this paper a frequency band analysis of AD EEG signals is presented, with the aim of improving the diagnosis of AD from EEG signals. Relative power

EEG-Based Emotion Recognition Using Deep Learning Network with Principal Component Based Covariate Shift Adaptation

by Suwicha Jirayucharoensak , Setha Pan-Ngum , Pasin Israsena
"... Automatic emotion recognition is one of the most challenging tasks. To detect emotion from nonstationary EEG signals, a sophisticated learning algorithm that can represent high-level abstraction is required. This study proposes the utilization of a deep learning network (DLN) to discover unknown fe ..."
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Automatic emotion recognition is one of the most challenging tasks. To detect emotion from nonstationary EEG signals, a sophisticated learning algorithm that can represent high-level abstraction is required. This study proposes the utilization of a deep learning network (DLN) to discover unknown

Research Article EEG-Based Emotion Recognition Using Deep Learning Network with Principal Component Based Covariate Shift Adaptation

by Suwicha Jirayucharoensak, Setha Pan-ngum, Pasin Israsena
"... Copyright © 2014 Suwicha Jirayucharoensak et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Automatic emotion recognition is one ..."
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is one of the most challenging tasks. To detect emotion from nonstationary EEG signals, a sophisticated learning algorithm that can represent high-level abstraction is required. This study proposes the utilization of a deep learning network (DLN) to discover unknown feature correlation between input

Machine Learning in Electrocardiogram Diagnosis

by Abdel-badeeh M. Salem, Kenneth Revett, El-sayed A. El-dahshan
"... Abstract — The electrocardiogram (ECG) is a measure of the electrical activity of the heart. Since its introduction in 1887 by Waller, it has been used as a clinical tool for evaluating heart function. A number of cardiovascular diseases (CVDs) (arrhythmia, atrial fibrillation, atrioventricular (AV) ..."
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approaches to ECG classification, evaluating them in terms of the features they employ, the type(s) of CVD(s) to which they are applied, and their classification accuracy. Indexd Terms—Heart disease, Electrocardiogram, Classification, Machine learning.

Diagnosis of Alzheimer’s Disease from EEG by Means of Synchrony Measures in Optimized Frequency Bands

by Esteve Gallego-jutglà, Mohamed Elgendi, Francois Vialatte, Jordi Solé-casals, Charles Latchoumane, Jaesung Jeong, Justin Dauwels
"... Abstract—Several clinical studies have reported that EEG synchrony is affected by Alzheimer’s disease (AD). In this paper a frequency band analysis of AD EEG signals is presented, with the aim of improving the diagnosis of AD using EEG signals. In this paper, multiple synchrony measures are assessed ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
Abstract—Several clinical studies have reported that EEG synchrony is affected by Alzheimer’s disease (AD). In this paper a frequency band analysis of AD EEG signals is presented, with the aim of improving the diagnosis of AD using EEG signals. In this paper, multiple synchrony measures

Pd disease state assessment in naturalistic environments using deep learning

by Nils Y. Hammerla, James M. Fisher, Peter Andras, Richard Walker, Northumbria Healthcare , 2015
"... Management of Parkinson’s Disease (PD) could be im-proved significantly if reliable, objective information about fluctuations in disease severity can be obtained in ecologically valid surroundings such as the private home. Although automatic assessment in PD has been studied extensively, so far no a ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
to differentiate dis-ease state in data collected in naturalistic settings. Based on a large data-set collected from 34 people with PD we illustrate that deep learning outperforms other ap-proaches in generalisation performance, despite the un-reliable labelling characteristic for this problem setting, and how
Results 1 - 10 of 120