Latency change estimation for evoked potentials via frequency selective adaptive phase spectrum analyzer

Based on wavelet transform to analyze rabbit somatosensory evoked potential

Rabbits were narcotized and stimulated by 0.5Hz electric pulse. Potential of scalp was sampled at 3764Hz. Wave components of rabbit somatosensory evoked potential (SEP) can be located precisely through the method of continuous wavelet transform (CWT). Frequency feature of SEP can also be analyzed by CWT. The technique of continuous wavelet transform, which can project a one-dimension signal into a two-dimension time-frequency space, will become a useful method to process medical electronic signal. Key words: wavelet transform, somatosensory evoked potential, single-trial.

Diabetic Retinopathy Analysis

Diabetic retinopathy is one of the common complications of diabetes. Unfortunately, in many cases the patient is not aware of any symptoms until it is too late for effective treatment. Through analysis of evoked potential response of the retina, the optical nerve, and the optical brain center, a way will be paved for early diagnosis of diabetic retinopathy and prognosis during the treatment process. In this paper, we present an artificial-neural-network-based method to classify diabetic retinopathy subjects according to changes in visual evoked potential spectral components and an anatomically realistic computer model of the human eye under normal and retinopathy conditions in a virtual environment using 3D Max Studio and Windows Movie Maker.

Phase estimation of visual evoked responses

The phase of visual evoked responses (VERs) is one of the basic parameters in functional diagnostics of the visual system. A new method for phase estimation of VERs based on the observer model in system identification is introduced. Simulated data show significantly less variance of estimation than actual estimators do. By means of the new estimator, the dynamics of the visual system according to selected optical stimuli has been analyzed.

Latency change estimation for evoked potentials: a comparison of algorithms

Evoked potentials (EPs) have been widely used to quantify neurological system properties. Changes in EP latency may indicate impending neurological dysfunctions. This paper provides a review and a performance comparison of three classes of latency change estimation algorithms: correlation based, adaptive least mean square (LMS) based, and p-norm based algorithms. Data analysis based on computer simulated data and data from impact acceleration and hypoxia experiments were conducted. This concluded that correlation-based algorithms should only be used when the expected latency change is fixed and the noises are not correlated. While all adaptive LMS type algorithms were capable of tracking and estimating latency changes (fixed or variable) under Gaussian noise conditions, the direct LMS-based algorithm reduced the estimation error power given by traditional filter type LMS algorithms as much as 93%. When periodic interference was present, the frequency selective LMS algorithms outperformed other LMS-based algorithms and reduced of the estimation error power by 89%. Alpha-stable noise processes are better approximations of noises found in various EP analysis applications and adaptive p-norm based algorithms are found to be very robust under such noise conditions, eliminate erroneous abrupt latency changes other algorithms would have produced.