ABSTRACT
We computed Higuchi's fractal dimension (FD) of resting, eyes closed EEG recorded from 30 scalp locations in 18 male neuroleptic-naïve, recent-onset schizophrenia (NRS) subjects and 15 male healthy control (HC) subjects, who were group-matched for age. Schizophrenia patients showed a diffuse reduction of FD except in the bilateral temporal and occipital regions, with the reduction being most prominent bifrontally. The positive symptom (PS) schizophrenia subjects showed FD values similar to or even higher than HC in the bilateral temporo-occipital regions, along with a co-existent bifrontal FD reduction as noted in the overall sample of NRS. In contrast, this increase in FD values in the bilateral temporo-occipital region was absent in the negative symptom (NS) subgroup. The regional differences in complexity suggested by these findings may reflect the aberrant brain dynamics underlying the pathophysiology of schizophrenia and its symptom dimensions. Higuchi's method of measuring FD directly in the time domain provides an alternative for the more computationally intensive nonlinear methods of estimating EEG complexity.
Subject(s)
Electroencephalography/methods , Fractals , Schizophrenia/diagnosis , Signal Processing, Computer-Assisted , Adolescent , Adult , Age Factors , Case-Control Studies , Eye , Humans , Male , Middle Aged , Rest , Sex Factors , Time Factors , Young AdultABSTRACT
A new nonlinear time domain model is proposed in this paper for signals of cardiovascular origin. An equation of the dynamic nonlinear model has been obtained by considering a masking function, which is modulated by a harmonic series with the baseline drift incorporated into the model. Signal reconstruction using model parameters has established the effectiveness of the model for signal compression. Improvement has been effected by using neural networks for reducing the time for optimizing the initial parameters. An improved adaptive optimization step size algorithm has also been implemented. Results show that the technique is able to provide reasonable compression with low error between the original and reconstructed signals. One of the main advantages of the model is its potential of being used for compression of many different types of biosignals transmitted in parallel. Incorporation of the compression model into a telemedicine system has led to considerable saving in transmission time for patient data.
