Laser Spectral Analysis of Biomedical Materials-Functional Diagnosis by Laser Spectroscopy and New Spectral Analysis Methods.

Alzheimers disease (AD) is a brain disorder that is mainly characterized by a progressive degeneration of neurons in the brain, causing a decline in cognitive abilities and difficulties in engaging in day-to-day activities. This study compares an FFT-based spectral analysis against a functional connectivity analysis based on phase synchronization, for finding known differences between AD patients and Healthy Control (HC) subjects. Both of these quantitative analysis methods were applied on a dataset comprising bipolar EEG montages values from 20 diagnosed AD patients and 20 age-matched HC subjects. Additionally, an attempt was made to localize the identified AD-induced brain activity effects in AD patients. The obtained results showed the advantage of the functional connectivity analysis method compared to a simple spectral analysis. Specifically, while spectral analysis could not find any significant differences between the AD and HC groups, the functional connectivity analysis showed statistically higher synchronization levels in the AD group in the lower frequency bands (delta and theta), suggesting that the AD patients brains are in a phase-locked state. Further comparison of functional connectivity between the homotopic regions confirmed that the traits of AD were localized in the centro-parietal and centro-temporal areas in the theta frequency band (4-8 Hz). The contribution of this study is that it applies a neural metric for Alzheimers detection from a data science perspective rather than from a neuroscience one. The study shows that the combination of bipolar derivations with phase synchronization yields similar results to comparable studies employing alternative analysis methods.

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A Strategic Application of Fast Fourier Transform as a Novel Tool to Evaluate the Extent of Neuronal Insult in Rat Model of Focal Cerebral Ischemia

Bangladesh Journal of Medical Physics ◽

10.3329/bjmp.v5i1.14666 ◽

2013 ◽

Vol 5 (1) ◽

pp. 29-36 ◽

Cited By ~ 3

Author(s):

S Paul ◽

P Bhattacharya ◽

AK Pandey ◽

N Sharma ◽

JP Tiwari ◽

...

Keyword(s):

Mathematical Modeling ◽

Animal Model ◽

Spectral Analysis ◽

Cerebral Ischemia ◽

Rat Brain ◽

Brain Function ◽

Focal Cerebral Ischemia ◽

Physiological State ◽

Analysis Methods ◽

Eeg Data

The present work envisages mathematical modeling of induced focal cerebral ischemia in animal model using EEG data with the help of Fast Fourier Transformation method. Amongst several analysis methods, spectral analysis methods are important because it detects the frequencies and characteristics changes of brain waveforms depending on the brain function affected from disorders and physiological state. There are many applications of FFT, and the most important being that it is one of the basic conventional spectral analysis methods. However, it has some limitations, for instance, it adds contributions in the low frequency region which are not present in the original signal, and necessitates the use of windowing for decreasing the error rate. The present analysis was undertaken to ensure actual correlation of the different mathematical paradigms. EEG data were obtained from different regions of rat brain and were processed by FFT modeling in MATLAB platform. The assessment of long lasting functional outcome and to prevalent classical approach to study stroke was necessitated and therefore highly recommended to evaluate the efficacy of therapeutic strategies in relation to EEG in animal model of brain stroke. This mathematical modeling specifically Power Spectrum Density analysis was done to correlate the different prevalent condition of rat brain function. DOI: http://dx.doi.org/10.3329/bjmp.v5i1.14666 Bangladesh Journal of Medical Physics Vol.5 No.1 2012 29-36

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