Time-frequency characterization of mismatch negativity in nociceptive responses.

An efficient way to investigate the neural basis of nociceptive responses is the event-related brain potentials (ERPs). One component belonging to this family of ERPs is the mismatch negativity (MMN). It reflects pre-attentive detection of changes in the incoming stimulus by comparing the new stimulus with sensory memory traces. In this work, single trials of ERP taken from EEG signal recorded under thermal and electric stimulation were analyzed with time-frequency representation (TFR). The main objective of this work was to characterize responses to frequent and infrequent stimuli with TFR functions. Variables defined on instantaneous frequency and instantaneous power presented a statistical significance (p-value<0.0001) differentiating these two kind of responses. Furthermore, differences between the averaged instantaneous power and instantaneous frequency were also analyzed. It was found that instantaneous power and instantaneous frequency were able to better isolate the MMN components from EEG noise in certain frequency bands.

Auto-mutual information function of the EEG as a measure of depth of anesthesia.

Monitoring the depth of anesthesia (DOA) is necessary in order to decrease the incident of awareness in anesthesia and to prevent delays in the recovery phase. In the last decades a number of noninvasive methods have been proposed for the analysis of the electroencephalogram (EEG) for monitoring DOA. The objective of this work was to apply auto mutual information function (AMIF) to EEGs of patients under anesthesia in order to find variables able to characterize the following 4 states: awake, sedated, anesthetized and burst suppression episodes. The results show that the single and combined AMIF parameters were able to correctly classify the states in the range 72.2%-94.1% and 61.1%-100%, respectively.

Exons and introns characterization in nucleic acid sequences by time-frequency analysis.

A current problem in deoxyribonucleic acid (DNA) sequence analysis is to determine the exact locations of the genes and also in eukaryotes, the protein-coding regions in the mRNA primary transcript (pre-mRNA).The conversion into discrete numerical values of the symbols associated to the nucleotides of these sequences allows for a signal to address the problems related to localization and annotation of genes. In this work, thermodynamic data of free energy changes (ΔG°) on the formation of a duplex structure of DNA or RNA are used to convert the symbols into numerical values associated with the nucleotide sequence pre-mRNA. This study presents an analysis, based on techniques of time-frequency representation of a large number of gene sequences, in order to find variables related to pre-mRNA that could best characterize and discriminate coding regions from non-coding regions. It has been found that instantaneous frequency variables and instantaneous spectral energy variables in different frequency bands, allowed exons and introns to be correctly classified with more than 85%.