mEEG: A system for electroencephalogram data management and analysis

Electroencephalography (EEG) is a technique for the acquisition of electrical brain signals. In recent years the increase of information acquired from signal analysis has genereted a large amount of data; therefore, the development of tools for analysis has become necessary. In this paper, the mEEG prototype for EEG data managing is presented. It offers a user-friendly communication solution to exchange data between physicians and biomedical engineers. Features can be used for: (i) perform a fast diagnoses; (ii) show reports about clinical information; (iii) store and retrieve neurological data.

[1]  Rudra Pratap,et al.  Getting started with MATLAB : a quick introduction for scientists and engineers : version 6 , 1998 .

[2]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[3]  Abdulhamit Subasi,et al.  Classification of EEG signals using neural network and logistic regression , 2005, Comput. Methods Programs Biomed..

[4]  U. Rajendra Acharya,et al.  EEG Signal Analysis: A Survey , 2010, Journal of Medical Systems.

[5]  E. Bullmore,et al.  Human brain networks in health and disease , 2009, Current opinion in neurology.

[6]  N H Lovell,et al.  Signal quality measures for pulse oximetry through waveform morphology analysis , 2011, Physiological measurement.

[7]  Begoña Garcia-Zapirain,et al.  EEG artifact removal—state-of-the-art and guidelines , 2015, Journal of neural engineering.

[8]  Subhojit Ghosh,et al.  Statistical Wavelet Features, PCA, and SVM Based Approach for EEG Signals Classification , 2015 .

[9]  M. Caruso,et al.  A numerical analysis of the aortic blood flow pattern during pulsed cardiopulmonary bypass , 2015, Computer methods in biomechanics and biomedical engineering.

[10]  Max Bramer.  The PHP Language: Types of Statement , 2015 .

[11]  Anindya Bijoy Das,et al.  Classification of EEG signals using normal inverse Gaussian parameters in the dual-tree complex wavelet transform domain for seizure detection , 2016, Signal Image Video Process..

[12]  M. Chalk,et al.  Neural oscillations as a signature of efficient coding in the presence of synaptic delays , 2015, bioRxiv.

[13]  Yong Zhang,et al.  Classification of EEG Signals Based on Autoregressive Model and Wavelet Packet Decomposition , 2017, Neural Processing Letters.

[14]  R. B. Pachori,et al.  Tunable-Q Wavelet Transform Based Multiscale Entropy Measure for Automated Classification of Epileptic EEG Signals , 2017 .

[15]  U. Rajendra Acharya,et al.  Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals , 2017, Comput. Biol. Medicine.

[16]  Timothy M. Shepherd,et al.  Creutzfeldt-Jakob Disease , 2018 .

[17]  Binildas A. Christudas.  MySQL , 2019, Practical Microservices Architectural Patterns.