Statistics over features for internal carotid arterial disorders detection

Analysis of doppler ultrasound signals: ophthalmic arterial disorders detection case

The paper includes illustrative and detailed information about implementation of automated diagnostic systems and feature extraction/selection from signals recorded from ophthalmic arteries. The major objective of the paper is to be a guide for the readers, who want to develop an automated diagnostic systems for detection of arterial disorders. Toward achieving this objective, this paper present the techniques which should be considered in developing automated diagnostic systems. The paper will assist to the people in gaining a better understanding of the techniques in the detection of arterial disorders.

Discrete wavelet transform for analysis of spike-wave discharges in rats

In this paper, the discrete wavelet transform (DWT) as a feature extraction method was used in representing the spike-wave discharges (SWD) records of WAG/Rij rats. The SWD records of WAG/Rij rats were decomposed into time-frequency representations using the DWT and the statistical features were calculated to depict their distribution. The obtained wavelet coefficients were used to identify characteristics of the signal that were not apparent from the original time domain signal. The present study demonstrates that the wavelet coefficients are useful in determining the dynamics in the time-frequency domain of SWD records.