Adaptive filtering of evoked potentials with radial-basis-function neural network prefilter

Evoked potentials (EPs) are time-varying signals typically buried in relatively large background noise. To extract the EP more effectively from noise, we had previously developed an approach using an adaptive signal enhancer (ASE) (Chen et al., 1995). ASE requires a proper reference input signal for its optimal performance. Ensemble- and moving window-averages were formerly used with good results. In this paper, we present a new method to provide even more effective reference inputs for the ASE. Specifically, a Gaussian radial basis function neural network (RBFNN) was used to preprocess raw EP signals before serving as the reference input. Since the RBFNN has built-in nonlinear activation functions that enable it to closely fit any function mapping, the output of RBFNN can effectively track the signal variations of EP. Results confirmed the superior performance of ASE with RBFNN over the previous method.published_or_final_versio

Mechanisms of Hearing Loss after Blast Injury to the Ear

Given the frequent use of improvised explosive devices (IEDs) around the world, the study of traumatic blast injuries is of increasing interest. The ear is the most common organ affected by blast injury because it is the bodyﾒs most sensitive pressure transducer. We fabricated a blast chamber to re-create blast profiles similar to that of IEDs and used it to develop a reproducible mouse model to study blast-induced hearing loss. The tympanic membrane was perforated in all mice after blast exposure and found to heal spontaneously. Micro-computed tomography demonstrated no evidence for middle ear or otic capsule injuries; however, the healed tympanic membrane was thickened. Auditory brainstem response and distortion product otoacoustic emission threshold shifts were found to be correlated with blast intensity. As well, these threshold shifts were larger than those found in control mice that underwent surgical perforation of their tympanic membranes, indicating cochlear trauma. Histological studies one week and three months after the blast demonstrated no disruption or damage to the intra-cochlear membranes. However, there was loss of outer hair cells (OHCs) within the basal turn of the cochlea and decreased spiral ganglion neurons (SGNs) and afferent nerve synapses. Using our mouse model that recapitulates human IED exposure, our results identify that the mechanisms underlying blast-induced hearing loss does not include gross membranous rupture as is commonly believed. Instead, there is both OHC and SGN loss that produce auditory dysfunction

Real-time data-reusing adaptive learning of a radial basis function network for tracking evoked potentials

Tracking variations in both the latency and amplitude of evoked potential (EP) is important in quantifying properties of the nervous system. Adaptive filtering is a powerful tool for tracking such variations. In this paper, a data-reusing non-linear adaptive filtering method, based on a radial basis function network (RBFN), is implemented to estimate EP. The RBFN consists of an input layer of source nodes, a single hidden layer of non-linear processing units and an output layer of linear weights. It has built-in nonlinear activation functions that allow learning of function mappings. Moreover, it produces satisfactory estimates of signals against a background noise without a priori knowledge of the signal, provided that the signal and noise are independent. In clinical situations where EP responses change rapidly, the convergence rate of the algorithm becomes a critical factor. A carefully designed data-reusing RBFN can accelerate the convergence rate markedly and, thus, enhance its performance. Both theoretical analysis and simulation results support the unproved performance of our new algorithm. © 2006 IEEE.published_or_final_versio