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Detection of brainstem auditory evoked potential by adaptive filtering.

A method of detecting brainstem auditory evoked potential (BAEP) using adaptive signal enhancement (ASE) is proposed and tested in humans and cats. The ASE in this system estimates the signal component of the primary input, which is correlated with the reference input to the adaptive filter. The reference input is carefully designed to make an optimal and rapid estimation of the signal corrupted with noise, such as ongoing EEG. With a good choice of reference input, it is possible to track the variability of BAEP efficiently and rapidly. Moreover, the number of repetitions required could be markedly reduced and the result of the system is superior to that of ensemble averaging (EA). To detect BAEP in cats, only 30 ensemble averages are needed to obtain a reasonable reference input to the adaptive filter, and, for humans, 350–750 ensemble averages are sufficient for a satisfactory result. Using the LMS adaptive algorithm, individual BAEP can be obtained in real-time.

Measurement of human BAERs by the maximum length sequence technique

The traditional brainstem auditory evoked response (BAER) measurement technique (ensemble averaging) is time-consuming and is not acceptable for some time-critical clinical applications. In the paper the application of a pseudorandom binary sequence, the maximum length sequence, to human BAER measurements is examined. This technique permits a faster click rate to stimulate the test subject, and obtaines a higher signal-to-noise ratio (SNR) response through deconvolution. When compared with conventional averaging, the method can result in an improved SNR or in faster measurement of BAER. The theory of the technique and the experimental setup are presented, and theoretical analysis on the SNR improvement by this technique against averaging is also given. Actual measurements of BAER on both humans and cats indicate that this technique is effective, especially when the measurement time is not too long, or the number of trials is not too large.

Multiscale characterization of chronobiological signals based on the discrete wavelet transform

To compensate for the deficiency of conventional frequency-domain or time-domain analysis, this paper presents a multiscale approach to characterize the chronobiological time series (CTS) based on a discrete wavelet transform (DWT). We have shown that the local modulus maxima and zero-crossings of the wavelet coefficients at different scales give a complete characterization of rhythmic activities. We further constructed a tree scheme to represent those interacting activities across scales, Using the bandpass filter property of the DWT in the frequency domain, we also characterized the band-related activities by calculating energy in respective rhythmic bands. Moreover, since there is a fast and easily implemented algorithm for the DWT. This new approach may simplify the signal processing and provide a more efficient and complete study of the temporal-frequency dynamics of the CTS. Preliminary results are presented using the proposed method on the locomotion of mice under altered lighting conditions, verifying its competency for CTS analysis.

Adaptive neural network filter for visual evoked potential estimation

The authors describe a new approach to enhance the signal-to-noise-ratio (SNR) of visual evoked potential (VEP) based on an adaptive neural network filter. Neural networks are usually used in an nonadaptive way. The weights in the neural network are adjusted during training but remain constant in actual use. Here, the authors use an adaptive neural network filter with adaptation capabilities similar to those of the traditional linear adaptive filter and suitable training scheme is also examined. In contrast with linear adaptive filters, adaptive neural network filters possess nonlinear characteristics which can better match the nonlinear behaviour of evoked potential signals. Simulations employing VEP signals obtained experimentally confirm the superior performance of the adaptive neural network filter against traditional linear adaptive filter.

A PC-based system for long-term monitoring of animal activity

This paper describes a PC-based animal locomotor and sound activities synchronous analysis and recording system. In the former, using video recording and image analysis techniques, the geometric locations of an animal in a cage and its bodily displacement areas between consecutive time in two-dimensions were detected. Tremendous data reduction rate has also been obtained (512/spl times/512:4), which facilitates our PC computer (Pentium 100) to perform a long-term (up to several weeks according to the space of hard disk) and on-line (1 sec) analysis and storage of the animal locomotor signals. In the latter, the sounds generated by the animal were recorded at the cage over a consecutive 1-sec time and its root mean square (RMS) value was used to index the sound level. Our preliminary study showed that such a combination of monitoring and recording system gives a faithful and comprehensive representation of animal activity.

Detection and quantification of venous air embolism by wavelet analysis of Doppler heart sound

The wavelet analysis of the Doppler heart sound detected under controlled venous air embolism at sub-clinically and clinically significant volumes was studied in anaesthetized dogs. Signal processing with wavelet enhances the Dower of embolic signal and facilitates the simple detection and extraction of embolic heart beats by thresholding. The cumulative power of the extracted embolic heart beats is found to be linearly related to the volume of embolic air on the log-log scale, suggesting that it is feasible to estimate clinically significant volume of embolic air in human subjects by linearly extrapolating from sub-clinical embolic volumes.

Estimation of single brainstem auditory evoked potential using time-sequenced adaptive filtering

The time-sequenced adaptive filtering (TSAF) technique has been successfully used to track variation of brainstem auditory evoked potential (BAEP) in humans. 400 ensembles used by TSAF will produce a satisfactory result rivaling ensemble averaging using 2000 ensembles. Furthermore, a single BAEP signal can be obtained which makes it possible for clinicians to analyze the variation of signals across trials.<<ETX>>

Multi-rhythm study on the synchronous ability of mice to the light transitions

This journal issue entitled: Physiology Symposium - Annual Symposium, Hong Kong, April 1998 / Tainan, October 1997: Abstractspp. 286–304 of this journal issue contains abstracts of the Annual Physiology Symposium 1997

Fine ultradian structures of mouse locomotion in response to light-dark reversal

This journal issue entitled: Physiology Symposium - Annual Symposium, Hong Kong, April 1998 / Tainan, October 1997: Abstractspp. 253–285 of this journal issue contains abstracts of the Annual Physiology Symposium 1998pp. 253–285 of this journal issue contains abstracts of the Annual Physiology Symposium 1998pp. 253–285 of this journal issue contains abstracts of the Annual Physiology Symposium 1998

Estimation of evoked potentials using wavelet transform based time-frequency adaptive filtering

A time-frequency domain adaptive filtering method is presented to estimate evoked potentials. The wavelet transform is used to represent the original responses in the time-frequency domain with a discrete set of wavelet coefficients. Each coefficient, which is related to the time extent and the frequency extent in the time-frequency plane, is processed by an adaptive signal enhancer (ASE) to enhance the signal components. The processed coefficients are then used to reconstruct the evoked potential signals with the inverse wavelet transform. Visual evoked potentials (VEPs) from human subjects are estimated, and good results are obtained by this method.