Fractal Analysis of EEG Signals in the Brain of Epileptic Rats, with and without Biocompatible Implanted Neuroreservoirs

被引:16
作者
Lopez, T. [1 ,2 ]
Martinez-Gonzalez, C. L. [3 ]
Manjarrez, J. [1 ]
Plascencia, N. [1 ]
Balankin, A. S. [1 ]
机构
[1] Natl Inst Neurol & Neurosurg MVS, Nanotechnol Mat Lab, Mexico City 14269, DF, Mexico
[2] Univ Autonoma Metropolitina, Mexico City 14387, DF, Mexico
[3] IPN, Mexico City 07738, DF, Mexico
来源
ELECTROMECHANICAL AND SYSTEMS ENGINEERING | 2009年 / 15卷
关键词
Epilepsy; EEG; fractal; kindling; sol-gel titania; nanomedicine; nanotechnology; SEIZURE PREDICTION; DRUG-DELIVERY; LONG;
D O I
10.4028/www.scientific.net/AMM.15.127
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
Current epilepsy rates in Mexico are 4% (SERSAME-Health Ministry), of which 80% correspond to Temporal Lobe Epilepsy (TLE). Antiepileptic drug administration is systemic, meaning that 90% of the active agent is lost between administration and delivery to the epileptic focus in the brain. Severe toxic secondary effects may occur as a result. The present study is aimed at developing an alternative antiepileptic drug delivery system. In this study, a sol-gel nanostructured titania device, in which valproic acid (VPA) has been encapsulated. This is a nanoparticulate device, which is biocompatible with brain tissue. Stereotactic surgery was used to implant the reservoirs in the temporal lobe of Wistar rats, using chemical kindling, which was used to induce epilepsy. The reservoir was designed to release the drug at a constant rate over a period of at least one year. A functional study was performed on the efficiency of drug delivery in order to evaluate the effect on spontaneous and induced neuron electrical activity. A new discovery, which is presented here, shows that in the case of damaged brain tissue, as is the case in epilepsy, the accumulation of red globules, oxygen transportation results in the formation of calcium carbonate crystals which surround the epileptic focus. Because these crystals have a specific polarization, we propose to characterize their influence on the EEG using statistical methods. The electrical activity was measured by electroencephalography using 5 healthy rats without and 5 rats with an implanted VPA/device. Cerebral signals describe the complex behavior of the brain dynamics as a function of time. Fractal algorithms are sensitive to fluctuations and lead to the analysis and characterization of this kind of complex phenomena. A systematic study of these EEG's was made in order to observe the variation of signals during seizures and on the controlled rate of release of VPA. We have estimated the Hurst exponent (H) to measure long range-dependence. Preliminary results show that for the control group, signal behavior is persistent (H>0.5), while for the epileptic group antipersistency was observed (H<0.5), with variations due seizure stages. During the protection period using VPA, preliminary results show that values tend to reach original behavior, as the crisis is stabilized.
引用
收藏
页码:127 / +
页数:3
相关论文
共 50 条
  • [31] Epileptic Seizure Prediction using Power Analysis in Beta Band of EEG Signals
    Sharma, Aarti
    2015 INTERNATIONAL CONFERENCE ON SOFT COMPUTING TECHNIQUES AND IMPLEMENTATIONS (ICSCTI), 2015,
  • [32] AUTOMATED IDENTIFICATION OF EPILEPTIC AND ALCOHOLIC EEG SIGNALS USING RECURRENCE QUANTIFICATION ANALYSIS
    Ng, Ee Ping
    Lim, Teik-Cheng
    Chattopadhyay, Subhagata
    Bairy, Muralidhar
    JOURNAL OF MECHANICS IN MEDICINE AND BIOLOGY, 2012, 12 (05)
  • [33] EPILEPTIC SEIZURE DETECTION IN EEG SIGNALS USING MULTIFRACTAL ANALYSIS AND WAVELET TRANSFORM
    Uthayakumar, R.
    Easwaramoorthy, D.
    FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY, 2013, 21 (02)
  • [34] Detection of Epileptic Seizure from EEG Signals by Using Recurrence Quantification Analysis
    Kutlu, Funda
    Kose, Cemal
    2014 22ND SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU), 2014, : 1387 - 1390
  • [35] Study of the EEG Signals of Human Brain for the Analysis of Emotions
    Panat, Ashish R.
    Patil, Anita S.
    ADVANCES IN COMPUTING AND INFORMATION TECHNOLOGY, VOL 2, 2013, 177 : 659 - +
  • [36] Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition
    Pachori, Ram Bilas
    Bajaj, Varun
    COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2011, 104 (03) : 373 - 381
  • [37] Evaluation of Window Size in Classification of Epileptic Short-Term EEG Signals Using a Brain Computer Interface Software
    Tzimourta, Katerina D.
    Astrakas, Loukas G.
    Gianni, Anna Maria
    Tzallas, Alexandros T.
    Giannakeas, Nikolaos
    Paliokas, Ioannis
    Tsalikakis, Dimitrios G.
    Tsipouras, Markos G.
    ENGINEERING TECHNOLOGY & APPLIED SCIENCE RESEARCH, 2018, 8 (04) : 3093 - 3097
  • [38] Entropy and Fractal Analysis of EEG Signals for Early Detection of Alzheimer's Dementia
    Hadiyoso, Sugondo
    Wijayanto, Inung
    Humairani, Annisa
    TRAITEMENT DU SIGNAL, 2023, 40 (04) : 1673 - 1679
  • [39] Multiscale Fractal Analysis on EEG Signals for Music-Induced Emotion Recognition
    Avramidis, Kleanthis
    Zlatintsi, Athanasia
    Garoufis, Christos
    Maragos, Petros
    29TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2021), 2021, : 1316 - 1320
  • [40] Discriminating the Different Human Brain States with EEG Signals using Fractal Dimension: A Nonlinear Approach
    Ahmad, Rana Fayyaz
    Malik, Aamir Saeed
    Kamel, Nidal
    Amin, Hafeezullah
    Zafar, Raheel
    Qayyum, Abdul
    Reza, Faruque
    2014 IEEE INTERNATIONAL CONFERENCE ON SMART INSTRUMENTATION, MEASUREMENT AND APPLICATIONS (ICSIMA), 2014,