Advances in Processing and Pattern Analysis of Biological by Will Gersch (auth.), Isak Gath, Gideon F. Inbar (eds.) PDF

By Will Gersch (auth.), Isak Gath, Gideon F. Inbar (eds.)

ISBN-10: 1475790988

ISBN-13: 9781475790986

ISBN-10: 1475791003

ISBN-13: 9781475791006

In fresh years there was quick growth within the improvement of sign processing as a rule, and extra in particular within the software of sign processing and trend research to organic indications. thoughts, equivalent to parametric and nonparametric spectral estimation, better order spectral estimation, time-frequency equipment, wavelet rework, and identifi­ cation of nonlinear platforms utilizing chaos idea, were effectively used to explain easy mechanisms of physiological and psychological approaches. equally, organic indications recorded in the course of day-by-day clinical perform for medical diagnostic methods, reminiscent of electroen­ cephalograms (EEG), evoked potentials (EP), electromyograms (EMG) and electrocardio­ grams (ECG), have drastically benefitted from advances in sign processing. with a purpose to replace researchers, graduate scholars, and clinicians, at the most recent advancements within the box, a global Symposium on Processing and development research of organic signs was once held on the Technion-Israel Institute of know-how, in the course of March 1995. This booklet comprises 27 papers added in the course of the symposium. The publication follows the 5 classes of the symposium. the 1st part, Processing and development research of standard and Pathological EEG, bills for many of the most modern advancements within the sector of EEG processing, particularly: time various parametric modeling; non-linear dynamic modeling of the EEG utilizing chaos thought; Markov research; hold up estimation utilizing adaptive least-squares filtering; and purposes to the research of epileptic EEG, EEG recorded from psychiatric sufferers, and sleep EEG.

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Tractor D 2, is defined, according to a theorem of Takens ( 1981) for small rand large m and N, by the following relation: C(r,m) ocr D (3) Thus, D 2 can be estimated by the slope oflog C(r,m) versus log r. For the calculations, we modified the standard Grassberger-Procaccia algorithm ( 1983) by choosing the delays k 1 as follows: k0 = 0; k 1= WT, where the window W is a small integer (in our case we choose W = 3) and Tis the first zero-crossing of the auto-correlation function; k2 , k 1 , ....

L1:. x:... ~ 1lx orig1nal signal surrogate signal Figure 4. D 2 analysis of an epoch (indicated by means of double-headed arrow) o f inlracranially recorded EEG signals (ATL =anterior temporal left, MTL =mid tempora l left, PTL =posterior temporal left, AML =amygdala left, HCL = hippocampus left, R =analogous sites from the right hemisphere). Epileptiform activ ity is present in the HCR signal in the shape of individual interictal spikes before the epoch indicated. Duri ng t his epoch, the HCR activity becomes continuously epileptiform indicat ing seizure onset.

By contrast, in an effort to produce operationally meaningful parameters, Isaksson et al. ( 1981) demonstrated an ARMA model which decomposed an EEG signal into a smoothed spectrum divided into the EEG frequency bands traditionally used in clinical neurophysiology. , 1985; Tsoi, 1987, 1988) since. Lopes da Silva eta!. (1974) applied a threshold criterion to the forward prediction error estimates to detect the spikes in an EEG, and Bohlin (1977), among Markovian Analysis of EEG Signal Dynamics in Obsessive-Compulsive Disorder 37 others, replicated these results using a Kalman filter implementation of an AR model which was able to estimate the spike waveform.

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Advances in Processing and Pattern Analysis of Biological Signals by Will Gersch (auth.), Isak Gath, Gideon F. Inbar (eds.)


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