Informatica

Transient evoked otoacoustic emissions (TEOAEs) have been analyzed for objective assessment of hearing function and monitoring of the influence of noise exposure and ototoxic drugs. This paper presents a novel application of the Hilbert–Huang transform (HHT) for detection and time-frequency mapping of TEOAEs. Since the HHT does not distinguish between signal and noise, it is combined with ensemble correlation in order to extract signal information in intervals with correlated activity. High resolution time-frequency mapping could predict 30 dBHL, or higher hearing loss, at different audiological frequencies in 63–90% of the cases and normal hearing in 75–90% of the cases. The proposed method offers TEOAE time-frequency mapping by constraining the analysis to regions with high signal-to-noise ratios. The results suggest that the HHT is suitable for hearing loss detection at individual frequencies and characterization of the fine structures of TEOAEs.

This paper presents an application of the Hilbert–Huang transform (HHT) and ensemble correlation for detection of the transient evoked otoacoustic emissions (TEOAEs), and high resolution time–frequency mapping. The HHT provides a powerful tool for nonlinear analysis of nonstationary signals such as TEOAEs. Since the HHT itself does not distinguish between signal and noise it was used with ensemble correlation to extract information about intervals with correlated activity. The combination of methods produced good results for both tasks TEOAE detection and time–frequency mapping. The resulting detection performance, using the mean hearing threshold as audiological separation criterion, was a specificity of 81% at a sensitivity of 90% to be compared to 65% with the traditional wave reproducibility detection criterion. High resolution time frequency mapping predicted in more than 70% of the cases hearing loss at a specific frequency in cases of ski-sloping audiograms. The present m ethod does not require a priori information on the signal and may, with minor changes, be successfully applied to analysis of other types of repetitive signals such as evoked potentials.

This paper presents a new approach for human cataract automatical detection based on ultrasound signal processing. Two signal decomposition techniques, empirical mode decomposition and discrete wavelet transform are used in the presented method. Performance comparison of these two decomposition methods when applied to this specific ultrasound signal is given. Described method includes ultrasonic signal decomposition to enhance signal specific features and increase signal to noise ratio with the following decision rules based on adaptive thresholding. The resulting detection performance of the proposed method using empirical mode decomposition was better to compare to discrete wavelet transform and resulted in 70% correctly identified “healthy subject” cases and 82%, 97% and 100% correctly identified “cataract cases” in the incipience, immature and mature cataract subject groups, respectively. Discussion is given on the reasons of different results and the differences between the two used signal decomposition techniques.

Application of knowledge discovery in databases (data mining) for medical decision support is discussed in this work. The aim of the study was to use decision support algorithm for the differential diagnosis of intraocular tumors using parameters from eye images obtained by the ultrasound examination. Application of predictive modeling algorithm for decision tree formation using See5.0/C5.0 data mining system is presented. The decision tree was build using tumor geometry and microstructure parameters. The use of decision tree allows to differentiate tumors from other tumor-like formations. Low percentage of diagnostic errors shows that decision tree is reliable enough to offer it as “second opinion” for physician's decision support.