Investigation of the noise effect on fractal dimension of EEG in schizophrenia patients using wavelet and SSA-based approaches

Abstract

ObjectivesComplexity measures have been enormously used in schizophrenia patients to estimate brain dynamics. However, the conflicting results in terms of both increased and reduced complexity values have been reported in these studies depending on the patients’ clinical status or symptom severity or medication and age status. The objective of this study is to investigate the nonlinear brain dynamics of chronic, medicated schizophrenia patients and healthy control subjects using Katz's fractal dimension (FD). Moreover, in order to determine noise effect on complexity of EEG data, a noise elimination method based on wavelet and singular spectrum analysis (SSA) were assessed. MethodsTwenty-two schizophrenia patients and twenty-two age- and gender-matched control subjects underwent a resting state EEG examination with 120s. The discrete wavelet transform (DWT) was applied for EEG decomposition. Using a SSA approach, noise was removed and EEG reconstructed by inverse wavelet transform. The brain complexity of participants was investigated and compared using Katz's FD obtained from original and preprocessed EEG data. ResultsLower complexity values were found in schizophrenia patients. However, this difference was only statistically significant for each channel in preprocessed, noiseless EEG data. The most significant complexity differences between patients and controls were obtained in left frontal and parietal regions of the brain. ConclusionOur findings demonstrate that the utilizing of complexity measures with preprocessing approaches on EEG data to analyze schizophrenics’ brain dynamics might be a useful and discriminative tool for diagnostic purposes. Therefore, we expect that nonlinear analysis will give us more valuable results for understanding of schizophrenics’ brain.