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 improved performance of our new algorithm.

Adaptive signal enhancement of somatosensory evoked potential for spinal cord compression detection: an experimental study.

The objective of this study was to assess the efficacy of adaptive signal enhancement (ASE) as a means of indicating intraoperative spinal cord impingement. ASE technique was used to determine the changes in the somatosensory evoked potential (SEP) elicited from eighteen rats with varying levels of spinal cord compression. ASE technique was found to be able to effectively extract SEP signals for the detection of spinal cord injury. Furthermore, while the traditional ensemble averaging (EA) technique requires more than 500 trials for meaningful signal processing in severe noisy SEP recordings, the ASE method required only 50 trials to provide similar information. Because of its fast and reliable SEP detection, the ASE method is ideal for spinal cord monitoring in the clinical setting.

Novel sparse component analysis approach to free radical EPR spectra decomposition.

Free radicals play important roles in many physiological and pathological pathways in biological systems. These free radicals can be detected and quantified by their EPR spectra. The measured EPR spectra are often mixtures of pure spectra of several different free radicals and other chemicals. Blind source separation can be applied to estimate the pure spectra of interested free radicals. However, since the pure EPR spectra are often not independent of each other, the approach based on independent component analysis (ICA) cannot accurately extract the required spectra. In this paper, a novel sparse component analysis method for blind source separation, which exploits the sparsity of the EPR spectra, is presented to reliably extract the pure source spectra from their mixtures with high accuracy. This method has been applied to the analysis of EPR spectra of superoxide, hydroxyl, and nitric oxide free radicals, for both simulated data and real world ex vivo experiment. Compared to the traditional self-modeling method and our previous ICA-based blind source separation method, the proposed sparse component analysis approach gives much better results and can give perfect separation for mixtures of superoxide spectrum and hydroxyl spectrum in the ideal noise-free case. This method can also be used in other similar applications of quantitative spectroscopy analysis.

Multi-adaptive filtering technique for surface somatosensory evoked potentials processing.

Somatosensory evoked potential (SEP) testing has been widely applied to diagnosis of various neurological disorders. However, SEP recorded using surface electrodes is buried in noises, which makes the signal-to-noise ratio (SNR) very poor. Conventional averaging method usually requires up to thousands of raw SEP input trials to increase the SNR so that an identifiable waveform can be produced for latency and amplitude measurement. In this study, a multi-adaptive filtering (MAF) technique, emerging from the combination of well-developed adaptive noise canceller and adaptive signal enhancer, is introduced for fast and accurate surface SEP extraction. The MAF technique first processes the raw surface recorded SEP by the Canceller with a reference noise channel of background noise for adaptive subtraction before entering the Enhancer. The MAF was verified by filtering simulated SEP signals in which electroencephalography and Gaussian noise of different SNRs were added. It was found that the MAF could effectively suppress the noise and enhance the SEP components such that the SNR of the SEP is improved. Results showed that MAF with 50 input trials could provide similar performance in SEP detection to those extracted by the conventional averaging method with 1000 trials even at an SNR of -20 dB.

Analysis of time-varying coherence of EEG during face recognition based on harmonic transform.

Distinct cortical activity during face recognition was reported by a number of studies. Classical coherence analysis reflects the synchronization between two random processes in certain frequency under the assumption that the signals are stationary. In EEG study, the coherence analysis is mainly used to analyze the coupling and drive-response relation about the brain activities in different regions. Classical coherence analysis can extract the rhythmic consistency of the EEG activities in different regains. However, classical coherence can not extract the transient synchrony characteristics of brain activities. In order to track the spatial-temporal characteristics of EEG activities during cognitive conception, the time-varying coherence analysis is proposed. The EEG of 10 participants was recorded during recognition of familiar and unfamiliar faces, experiment results show that there is obvious difference about the model of information communication between temporal region and other brain regions in alpha rhythm during cognitive processing.

Image analysis system for assessment of immunohistochemically stained proliferative marker (MIB-1) in oesophageal squamous cell carcinoma.

The prognosis of oesophageal cancer patients is related to the portion of MIB-1 positively stained tumour nuclei. In this study, an image analysis system was developed based on LEICA Image Processing and Analysis System to reduce the subjective, tedious and inaccurate manual counting of nuclei staining. Representative oesophageal cancer tissues were collected and immunohistochemical preparations of MIB-1 were made. The MIB-1 positive nuclei in these tumours were assessed by quantitative counting, semi-quantitative counting, and three computer assessment methods using LEICA QWIN PRO. Our results showed that computer assessment methods were reliable and consistent. The procedure using the system could be accomplished within 15 min. Overlapped or missed counting of nuclei by the observer were eliminated. The image analysis system can really assist experts in obtaining reliable data for the prognosis of oesophageal cancer patients quickly.

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.

New Bayesian discriminator for detection of atrial tachyarrhythmias.

BACKGROUND: Accurate, rapid detection of atrial tachyarrhythmias has important implications in the use of implantable devices for treatment of cardiac arrhythmia. Currently available detection algorithms for atrial tachyarrhythmias, which use the single-index method, have limited sensitivity and specificity. METHODS AND RESULTS: In this study, we evaluated the performance of a new Bayesian discriminator algorithm in the detection of atrial fibrillation (AF), atrial flutter (AFL), and sinus rhythm (SR). Bipolar recording of 364 rhythms (AF=156, AFL=88, SR=120) at the high right atrium were collected from 20 patients who underwent electrophysiological procedures. After initial signal processing, a column vector of 5 features for each rhythm were established, based on the regularity, rate, energy distribution, percent time of quiet interval, and baseline reaching of the rectified autocorrelation coefficient functions. Rhythm identification was obtained by use of Bayes decision rule and assumption of Gaussian distribution. For the new Bayesian discriminator, the overall sensitivity for detection of SR, AF, and AFL was 97%, 97%, and 94%, respectively; and the overall specificity for detection of SR, AF, and AFL was 98%, 98%, and 99%, respectively. The overall accuracy of detection of SR, AF, and AFL was 98%, 97% and 98%, respectively. Furthermore, sensitivity, specificity, and accuracy of this algorithm were not affected by a range of white Gaussian noises with different intensities. CONCLUSIONS: This new Bayesian discriminator algorithm, based on Bayes decision of multiple features of atrial electrograms, allows rapid on-line and accurate (98%) detection of AF with robust anti-noise performance.