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.

It is known that the minimum affine separating committee (MASC) combinatorial optimization problem, which is related to some machine learning techniques, is NP-hard and does not belong to Apx class unless P=NP. In this paper, it is shown that the MASC problem formulated in a fixed dimension space within n>1 is intractable even if sets defining an instance of the problem are in general position. A new polynomial-time approximation algorithm for this modification of the MASC problem is presented. An approximation ratio and complexity bounds of the algorithm are obtained.

In this paper on basis of the results (Dyomin et al., 2003a) the structure of Shannon information amount in the joint filtering and extrapolation problem of the stochastic processes by continuous‐discrete time memory observations is investigated. For particular class of processes with applying of the general results the problem of optimal transmission over the lag channels is considered and efficiency of filtering and extrapolation receptions under transmission over channels with memory or lag is investigated.

This paper describes a preliminary algorithm performing epilepsy prediction by means of visual perception tests and digital electroencephalograph data analysis. Special machine learning algorithm and signal processing method are used. The algorithm is tested on real data of epileptic and healthy persons that are treated in Kaunas Medical University Clinics, Lithuania. The detailed examination of results shows that computerized visual perception testing and automated data analysis could be used for brain damages diagnosing.