Adaptive Signal Enhancement Unit for EEG Analysis in Remote Patient Care Monitoring Systems

In this paper we propose an efcient process of physiological artifact elimination methodology from brain waves(BW),which are also commonly known as electroencephalogram(EEG)signal.In a clinical environment during the acquisition of BW several artifacts contaminates the actual BW component.This leads to inaccurate and ambiguous diagnosis.As the statistical nature of the EEG signal is more non-stationery,adaptive ltering is the more promising method for the process of artifact elimination.In clinical conditions,the conventional adaptive techniques require many numbers of computational operations and leads to data samples overlapping and instability of the algorithm used.This causes delay in diagnosis and decision making.To overcome this problem in our work we propose to set a threshold value to diminish the problem of round off error.The resultant adaptive algorithm based on this strategy is Non-linear Least mean square(NL2MS)algorithm.Again,to improve this algorithm in terms of ltering capability we perform data normalization,using this algorithm several hybrid versions are developed to improve ltering and reduce computational operations.Using the method,a new signal enhancement unit(SEU)is realized and performance of various hybrid versions of algorithms examined using real EEG signals recorded from the subject.The ability of the proposed schemes is measured in terms of convergence,enhancement and multiplications required.Among various SEUs,the MCN2L 2MS algorithm achieves 14.6734,12.8732,10.9257,15.7790 dB during the artifact removal of RA,EMG,CSA and EBA components with only two multiplications.Hence,this algorithm seems to be better candidate for artifact elimination.

Withdrawal seizures vs on-medication seizures:an intracranial EEG recording case report

Background It has long been an interesting question of whether withdrawal seizures in epileptic patients differ from habitual seizures in terms of semiology and electrophysiology.Case presentation Here,we addressed this issue in a 40 year-old woman with drug-resistant focal epilepsy monitored by presurgical intracranial EEG.As a part of this routine pre-operative investigation,anti-seizure medications(ASMs)were halted;as a result,multiple withdrawal seizures were recorded before ASM readministration.During 4 days of invasive monitoring,we noticed three different phases in seizure organization:Acute withdrawal seizure(AWS):The first recorded seizure 10h after the implantation;the stabilized withdrawal seizures(SWS):seven habitual seizures recorded from 24h post implantation to readministration of ASMs;and the Non-withdrawal seizures(NWS):ten seizures recorded 24h after readministration of ASMs.AWS and SWS had the same semiology and same epileptic network,but the propagation time from the temporal pole to the para-hippocampal gyrus(PHG)and hippocampus ranged from no latency in AWS to up to 50 s in SWS.NWS were electrographic seizures,without any apparent clinical manifestation.Seizure onset in this type of seizure,as in the first two types,was in the temporal pole.However,NWS could last up to 3 min without involving the PHG or hippocampus.Conclusions We concluded that in acute withdrawal seizures the propagation time of epileptic activity is significantly reduced without affecting ictal organization network or semiology.Furthermore,ASM in this case had a remarkable influence on propagation rather than initiation of epileptic activity.