Journal ArticleDOI
Early detection of Alzheimer’s disease from EEG signals using Hjorth parameters
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TLDR
In this paper, the Hjorth parameters were used along with other common features to improve the AD detection accuracy from EEG signals in early stages, and different signal decomposition methods including filtering into brain frequency bands, discrete wavelet transform (DWT) and empirical mode decomposition (EMD) were evaluated.About:
This article is published in Biomedical Signal Processing and Control.The article was published on 2021-03-01. It has received 39 citations till now. The article focuses on the topics: Hjorth parameters.read more
Citations
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Journal ArticleDOI
Alzheimer's Disease and Frontotemporal Dementia: A Robust Classification Method of EEG Signals and a Comparison of Validation Methods.
Andreas Miltiadous,Katerina D. Tzimourta,Nikolaos Giannakeas,Markos G. Tsipouras,Theodora Afrantou,Panagiotis Ioannidis,Alexandros T. Tzallas +6 more
TL;DR: In this paper, six supervised machine learning techniques were compared on categorizing processed EEG signals of AD and frontotemporal dementia (FTD) cases, to provide an insight for future methods on early dementia diagnosis.
Journal ArticleDOI
Time–frequency signal processing: Today and future
Aydin Akan,Ozlem Karabiber Cura +1 more
TL;DR: Joint time–frequency methods developed and applied to the analysis and representation of non-stationary signals have successfully been utilized in the estimation of some parameters related to the analyzed signals.
Journal ArticleDOI
Motor Imagery Classification Based on a Recurrent-Convolutional Architecture to Control a Hexapod Robot
Tat’y Mwata-Velu,Jose Ruiz-Pinales,Horacio Rostro-Gonzalez,Mario Alberto Ibarra-Manzano,Jorge M. Cruz-Duarte,Juan Gabriel Avina-Cervantes +5 more
TL;DR: Numerical results support that the motor imagery EEG signals can be successfully applied in BCI systems to control mobile robots and related applications such as intelligent vehicles.
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Automated Identification of Sleep Disorder Types Using Triplet Half-Band Filter and Ensemble Machine Learning Techniques with EEG Signals
TL;DR: The proposed method using electroencephalogram (EEG) signals for the automated identification of six sleep disorders is simple, fast, efficient, and may reduce the challenges faced by medical practitioners during the diagnosis of various sleep disorders accurately in less time at sleep clinics and homes.
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Smart-Data-Driven System for Alzheimer Disease Detection through Electroencephalographic Signals
TL;DR: The proposed system with the ability of differentiate each disease stage by means of Electroencephalographic Signals outperforms previous studies with the same database by 2% in binary comparison MCI vs. ADM and central and parietal brain regions revealed abnormal activity as AD progresses.
References
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Journal ArticleDOI
The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
Norden E. Huang,Zheng Shen,Steven R. Long,Man-Li C. Wu,Hsing H. Shih,Quanan Zheng,Nai-Chyuan Yen,C. C. Tung,Henry H. Liu +8 more
TL;DR: In this paper, a new method for analysing nonlinear and nonstationary data has been developed, which is the key part of the method is the empirical mode decomposition method with which any complicated data set can be decoded.
Book
Ten lectures on wavelets
TL;DR: This paper presents a meta-analyses of the wavelet transforms of Coxeter’s inequality and its applications to multiresolutional analysis and orthonormal bases.
On empirical mode decomposition and its algorithms
TL;DR: Empirical Mode Decomposition is presented, and issues related to its effective implementation are discussed, and an interpretation of the method in terms of adaptive constant-Q filter banks is supported.
Journal ArticleDOI
The STARTEC Decision Support Tool for Better Tradeoffs between Food Safety, Quality, Nutrition, and Costs in Production of Advanced Ready-to-Eat Foods
Taran Skjerdal,Andras Gefferth,Miroslav Spajic,Edurne Gaston Estanga,Alessandra De Cesare,Silvia Vitali,Frederique Pasquali,Federica Bovo,Gerardo Manfreda,Rocco Mancusi,Marcello Trevisiani,Girum Tadesse Tessema,Tone Mathisen Fagereng,Lena Haugland Moen,Lars Lyshaug,Anastasios Koidis,Gonzalo Delgado-Pando,Alexandros Ch. Stratakos,Marco Boeri,Cecilie From,Hyat Syed,Mirko Muccioli,Roberto Mulazzani,Catherine Halbert +23 more
TL;DR: Compared to other decision support tools, the STARTEC-tool is product-specific and multidisciplinary and includes interpretation and targeted recommendations for end-users.
Journal ArticleDOI
An on-line transformation of EEG scalp potentials into orthogonal source derivations
TL;DR: A new type of EEG derivation has been investigated, which detects source activity as it appears at the surface level of the scalp, and is realized in the 10-20 system of electrode placement basically as an analogue superposition of four bipolar derivations, forming a star-like configuration around each electrode.