scholarly journals Automated Detection and Removal of Cardiac and Pulse Interferences from Neonatal EEG Signals

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6364
Author(s):  
Gabriella Tamburro ◽  
Pierpaolo Croce ◽  
Filippo Zappasodi ◽  
Silvia Comani
Keyword(s):  
Brain Activity ◽  
Automated Detection ◽  
Automated Classification ◽  
Eeg Signals ◽  
Ecg Signals ◽  
Independent Components ◽  
Eeg Recordings ◽  
Omission Rate ◽  
Consequent Improvement

Electrical cardiac and pulsatile interference is very difficult to remove from electroencephalographic (EEG) signals, especially if recorded in neonates, for which a small number of EEG channels is used. Several methods were proposed, including Blind Source Separation (BSS) methods that required the use of artificial cardiac-related signals to improve the separation of artefactual components. To optimize the separation of cardiac-related artefactual components, we propose a method based on Independent Component Analysis (ICA) that exploits specific features of the real electrocardiographic (ECG) signals that were simultaneously recorded with the neonatal EEG. A total of forty EEG segments from 19-channel neonatal EEG recordings with and without seizures were used to test and validate the performance of our method. We observed a significant reduction in the number of independent components (ICs) containing cardiac-related interferences, with a consequent improvement in the automated classification of the separated ICs. The comparison with the expert labeling of the ICs separately containing electrical cardiac and pulsatile interference led to an accuracy = 0.99, a false omission rate = 0.01 and a sensitivity = 0.93, outperforming existing methods. Furthermore, we verified that true brain activity was preserved in neonatal EEG signals reconstructed after the removal of artefactual ICs, demonstrating the effectiveness of our method and its safe applicability in a clinical context.

Entropy-based automated classification of independent components separated from fMCG

2007 ◽  
Vol 52 (5) ◽  
pp. N87-N97 ◽  
Author(s):  
S Comani ◽  
V Srinivasan ◽  
G Alleva ◽  
G L Romani
Keyword(s):  
Automated Classification ◽  
Independent Components

Classification Algorithms for EEG-Based Brain-Computer Interface

2019 ◽  
pp. 52-73
Author(s):  
Sravanth Kumar Ramakuri ◽  
Chinmay Chakraboirty ◽  
Anudeep Peddi ◽  
Bharat Gupta
Keyword(s):  
Brain Activity ◽  
Biomedical Signal Processing ◽  
Computer Interface ◽  
Eeg Signals ◽  
Large Sets ◽  
Eeg Data ◽  
Important Measurement ◽  
The Brain

In recent years, a vast research is concentrated towards the development of electroencephalography (EEG)-based human-computer interface in order to enhance the quality of life for medical as well as nonmedical applications. The EEG is an important measurement of brain activity and has great potential in helping in the diagnosis and treatment of mental and brain neuro-degenerative diseases and abnormalities. In this chapter, the authors discuss the classification of EEG signals as a key issue in biomedical research for identification and evaluation of the brain activity. Identification of various types of EEG signals is a complicated problem, requiring the analysis of large sets of EEG data. Representative features from a large dataset play an important role in classifying EEG signals in the field of biomedical signal processing. So, to reduce the above problem, this research uses three methods to classify through feature extraction and classification schemes.

FEATURES EXTRACTION METHOD FOR BRAIN-MACHINE COMMUNICATION BASED ON THE EMPIRICAL MODE DECOMPOSITION

2013 ◽  
Vol 25 (06) ◽  
pp. 1350058 ◽  
Author(s):  
Pablo F. Diez ◽  
Vicente A. Mut ◽  
Eric Laciar ◽  
Abel Torres ◽  
Enrique M. Avila Perona
Keyword(s):  
Empirical Mode Decomposition ◽  
Communication System ◽  
Brain Activity ◽  
Features Extraction ◽  
Eeg Signals ◽  
Linear Discriminant ◽  
Mode Decomposition ◽  
Machine Communication ◽  
Mental Tasks

A brain-machine interface (BMI) is a communication system that translates human brain activity into commands, and then these commands are conveyed to a machine or a computer. It is proposes a technique for features extraction from electroencephalographic (EEG) signals and afterward, their classification on different mental tasks. The empirical mode decomposition (EMD) is a method capable of processing non-stationary and nonlinear signals, as the EEG. The EMD was applied on EEG signals of seven subjects performing five mental tasks. Six features were computed, namely, root mean square (RMS), variance, Shannon entropy, Lempel–Ziv complexity value, and central and maximum frequencies. In order to reduce the dimensionality of the feature vector, the Wilks' lambda (WL) parameter was used for the selection of the most important variables. The classification of mental tasks was performed using linear discriminant analysis (LDA) and neural networks (NN). Using this method, the average classification over all subjects in database is 91 ± 5% and 87 ± 5% using LDA and NN, respectively. Bit rate was ranging from 0.24 bits/trial up to 0.84 bits/trial. The proposed method allows achieving higher performances in the classification of mental tasks than other traditional methods using the same database. This represents an improvement in the brain-machine communication system.

scholarly journals Automated Faint Galaxy Counts and Galactic Evolution

1980 ◽  
Vol 92 ◽  
pp. 1-8
Author(s):  
J. Anthony Tyson ◽  
John F. Jarvis
Keyword(s):  
Galaxy Evolution ◽  
Automated Detection ◽  
Galactic Evolution ◽  
Automated Classification ◽  
Faint Object ◽  
Statistical Studies ◽  
Automated Classifier ◽  
Analysis System ◽  
Number Counts

Detection and classification of faint images by eye has traditionally encountered systematic errors faintwards of 20th mag on Schmidt plates and 22nd mag on 4-meter plates. Automated classification of Schmidt plate images has pushed the classification limit to 22 mag (Kibblewhite, et al., 1975). Automated detection and classification of faint 4-meter limit plate images has recently led to statistical studies of galaxy numbers and clustering at redshifts where cosmology and galactic evolution dominate over local effects. Here we report on some aspects of the FOCAS (Faint Object Classification and Analysis System) automated classifier (Tyson and Jarvis, 1979) and compare our results of number counts in SA57 with those of Kron, 1979. Differential galaxy counts in six high latitude fields and evidence for galaxy evolution are briefly discussed.

Automated Classification of Focal and Non-Focal EEG Signals Based on Bivariate Empirical Mode Decomposition

2018 ◽  
pp. 13-33 ◽  
Author(s):  
Rajeev Sharma ◽  
Ram Bilas Pachori
Keyword(s):  
Empirical Mode Decomposition ◽  
Support Vector ◽  
Automated Classification ◽  
Intrinsic Mode Functions ◽  
Eeg Signals ◽  
Mode Decomposition ◽  
Electroencephalogram Eeg ◽  
Aided Diagnosis ◽  
Bivariate Empirical Mode Decomposition

The chapter presents a new approach of computer aided diagnosis of focal electroencephalogram (EEG) signals by applying bivariate empirical mode decomposition (BEMD). Firstly, the focal and non-focal EEG signals are decomposed using the BEMD, which results in intrinsic mode functions (IMFs) corresponding to each signal. Secondly, bivariate bandwidths namely, amplitude bandwidth, precession bandwidth, and deformation bandwidth are computed for each obtained IMF. Interquartile range (IQR) values of bivariate bandwidths of IMFs are employed as the features for classification. In order to perform classification least squares support vector machine (LS-SVM) is used. The results of the experiment suggest that the computed bivariate bandwidths are significantly useful to discriminate focal EEG signals. The resultant classification accuracy obtained using proposed methodology, applied on the Bern-Barcelona EEG database, is 84.01%. The obtained results are encouraging and the proposed methodology can be helpful for identification of epileptogenic focus.

scholarly journals Automated Classification of Mental Arithmetic Tasks Using Recurrent Neural Network and Entropy Features Obtained from Multi-Channel EEG Signals

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1079
Author(s):  
Abhishek Varshney ◽  
Samit Kumar Ghosh ◽  
Sibasankar Padhy ◽  
Rajesh Kumar Tripathy ◽  
U. Rajendra Acharya
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Classification Accuracy ◽  
Mental Arithmetic ◽  
Cognitive Workload ◽  
Automated Classification ◽  
Eeg Signals ◽  
Arithmetic Task ◽  
Mental Arithmetic Task

The automated classification of cognitive workload tasks based on the analysis of multi-channel EEG signals is vital for human–computer interface (HCI) applications. In this paper, we propose a computerized approach for categorizing mental-arithmetic-based cognitive workload tasks using multi-channel electroencephalogram (EEG) signals. The approach evaluates various entropy features, such as the approximation entropy, sample entropy, permutation entropy, dispersion entropy, and slope entropy, from each channel of the EEG signal. These features were fed to various recurrent neural network (RNN) models, such as long-short term memory (LSTM), bidirectional LSTM (BLSTM), and gated recurrent unit (GRU), for the automated classification of mental-arithmetic-based cognitive workload tasks. Two cognitive workload classification strategies (bad mental arithmetic calculation (BMAC) vs. good mental arithmetic calculation (GMAC); and before mental arithmetic calculation (BFMAC) vs. during mental arithmetic calculation (DMAC)) are considered in this work. The approach was evaluated using the publicly available mental arithmetic task-based EEG database. The results reveal that our proposed approach obtained classification accuracy values of 99.81%, 99.43%, and 99.81%, using the LSTM, BLSTM, and GRU-based RNN classifiers, respectively for the BMAC vs. GMAC cognitive workload classification strategy using all entropy features and a 10-fold cross-validation (CV) technique. The slope entropy features combined with each RNN-based model obtained higher classification accuracy compared with other entropy features for the classification of the BMAC vs. GMAC task. We obtained the average classification accuracy values of 99.39%, 99.44%, and 99.63% for the classification of the BFMAC vs. DMAC tasks, using the LSTM, BLSTM, and GRU classifiers with all entropy features and a hold-out CV scheme. Our developed automated mental arithmetic task system is ready to be tested with more databases for real-world applications.

scholarly journals Classificação Automatizada do Uso e Cobertura do Solo a Partir de Imagens Landsat (Automated Classification of Use and Land Cover from Landsat Images)

2013 ◽  
Vol 6 (1) ◽  
pp. 058
Author(s):  
Gilberto Fialho Moreira
Keyword(s):  
Land Cover ◽  
Satellite Images ◽  
New Technologies ◽  
Automated Detection ◽  
Thematic Mapper ◽  
Automated Classification ◽  
Landsat Images ◽  
Classification Procedures ◽  
Operational Simplicity

As discussões atuais acerca dos problemas ambientais têm exigido o domínio de ferramentas e novas metodologias que garantam a melhor gestão do espaço natural. A avaliação da cobertura do solo tem requerido das agências de monitoramento ambiental investimentos em tecnologias que proporcionem resultados ágeis e precisos, de forma a potencializar as possibilidades de proteção do meio ambiente. Neste contexto, o objetivo deste estudo foi avaliar novas tecnologias e metodologias de detecção automatizada da cobertura vegetal a partir de imagens orbitais. O trabalho foi desenvolvido no município de Araponga/MG, utilizando procedimentos de classificação de imagens pelos métodos da Máxima Verossimilhança (MAXVER) e por Redes Neurais Artificiais (RNA) aplicados em seis bandas de uma imagem LandSat 5TM (Thematic Mapper) 2005. No estudo, buscou-se discriminar as seguintes tipologias: Floresta Estacional Semidecídua, Floresta Ombrófila, Campo de Altitude, Pastagem, Café e Eucalipto. Para fins de comparação, foram utilizados resultados do Inventário Florestal de Minas Gerais de 2005, cuja classificação foi efetuada pelo procedimento das Árvores de Decisão. Os resultados obtidos indicaram divergências entre as metodologias, ainda que na executada via MAXVER, o índice de validação geral utilizado tenha revelado uma classificação considerada ótima. As classificações efetuadas não apresentaram resultados adequados para as coberturas vegetais eucalipto e café. Embora a metodologia RNA venha se despontando como uma das mais adequadas para a classificação de imagens, a complexidade e o tempo demandado na preparação dos materiais, bem como os procedimentos de tentativa e erro requeridos, dificultam ou mesmo restringem sua utilização, principalmente comercial. Em função dos resultados alcançados no presente estudo, aos quais pode ser associada à simplicidade operacional, a classificação via MAXVER destaca-se como uma opção mais adequada para a detecção da cobertura vegetal em estudos ambientais.  AbstractThe current discussions about environmental problems have required knowledgment new tools and methodologies to ensure better management of natural ambient. Land cover evaluation has required from environmental monitoring agencies investments in technology that provide accurate and quick results to maximize the opportunities for environmental protection. In this context, the objective of this study was to evaluate new technologies and methodologies for land cover automated detection from satellite images. The study was carried in Araponga/MG (Brazil) county, using the images classification procedures of Maximum Likelihood (MAXVER) method and Artificial Neural Networks (RNA). Associated with the operational simplicity, the MAXVER classification stands out as a proper option for the detection of vegetation cover in environmental studies.

Sign in / Sign up

Export Citation Format

Share Document