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
  • [41] Time-variant connectivity analysis between epileptic EEG signals and between EEG-envelopes and HRV
    Piper, Diana
    Ungureanu, Mihaela
    Strungaru, Rodica
    Schiecke, Karin
    Pester, Britta
    Leistritz, Lutz
    Witte, Herbert
    Feucht, Martha
    Benninger, Franz
    2013 E-HEALTH AND BIOENGINEERING CONFERENCE (EHB), 2013,
  • [42] FPGA Implementation for Epileptic Seizure Detection using Amplitude and Frequency Analysis of EEG Signals
    Selvathi, D.
    Selvaraj, Henry
    2017 25TH INTERNATIONAL CONFERENCE ON SYSTEMS ENGINEERING (ICSENG), 2017, : 183 - 192
  • [43] An efficient method for identification of epileptic seizures from EEG signals using Fourier analysis
    Virender Kumar Mehla
    Amit Singhal
    Pushpendra Singh
    Ram Bilas Pachori
    Physical and Engineering Sciences in Medicine, 2021, 44 : 443 - 456
  • [44] An efficient method for identification of epileptic seizures from EEG signals using Fourier analysis
    Mehla, Virender Kumar
    Singhal, Amit
    Singh, Pushpendra
    Pachori, Ram Bilas
    PHYSICAL AND ENGINEERING SCIENCES IN MEDICINE, 2021, 44 (02) : 443 - 456
  • [45] Multiclass Epileptic Seizure Classification Using Time-Frequency Analysis of EEG Signals
    Acharjee, Partha Pratim
    Shahnaz, Celia
    2012 7TH INTERNATIONAL CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING (ICECE), 2012,
  • [46] Analysis of α Wave in Normal and Epileptic EEG Signals Based on Symbol-Relative Entropy
    Zhu, Wenyong
    Dai, Jiafei
    Li, Jin
    Wang, Jun
    Hou, Fengzhen
    2018 11TH INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING, BIOMEDICAL ENGINEERING AND INFORMATICS (CISP-BMEI 2018), 2018,
  • [47] Machine Learning Approach for Epileptic Seizure Detection Using Wavelet Analysis of EEG Signals
    Kumar, Abhishek
    Kolekar, Maheshkumar H.
    2014 INTERNATIONAL CONFERENCE ON MEDICAL IMAGING, M-HEALTH & EMERGING COMMUNICATION SYSTEMS (MEDCOM), 2015, : 412 - 416
  • [48] Multi-channel analysis of the EEG signals and statistic particularities for epileptic seizure forecast
    Gallois, P
    Forzy, G
    Morineaux, T
    Peyrodie, L
    SECOND JOINT EMBS-BMES CONFERENCE 2002, VOLS 1-3, CONFERENCE PROCEEDINGS: BIOENGINEERING - INTEGRATIVE METHODOLOGIES, NEW TECHNOLOGIES, 2002, : 208 - 215
  • [49] Detection of epileptic seizure using EEG signals analysis based on deep learning techniques
    Abdulwahhab, Ali H.
    Abdulaal, Alaa Hussein
    Al-Ghrairi, Assad H. Thary
    Mohammed, Ali Abdulwahhab
    Valizadeh, Morteza
    CHAOS SOLITONS & FRACTALS, 2024, 181
  • [50] Changes in Dynamical Characteristics of Epileptic EEG in Rats using Recurrence Quantification Analysis
    Rabbi, Ahmed F.
    Jaiswal, Manoj K.
    Lei, Saobo
    Fazel-Rezai, Reza
    2011 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2011, : 2562 - 2565