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. | 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 next 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.published_or_final_versio

An adaptive RBF neural network model for evoked potential estimation

A method for evoked potential estimation based on an adaptive radial basis function neural network (RBFNN) model is presented in this paper. During training, the number of hidden nodes (number of RBFs) and model parameters are adjusted to fit the target signal which is obtained by averaging. In order to reduce computational complexity and the influence of noise in estimating single-trial evoked potential (EP), the number of hidden nodes is also minimized in training. After training, both peak latency and amplitude, being distinctive features of an EP, are characterized by center and height of the corresponding RBF respectively. In EP estimation, an adaptive algorithm is employed to track the peaks from trial to trial by adapting the center and height of RBFs directly. The adaptive RBFNN is tested on a computer simulated data set and clinical EP recording. Our proposed algorithm is suitable for tracking EP waveform variations.published_or_final_versio

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 (512×512:4), 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.published_or_final_versio

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.published_or_final_versio

Fast detection of venous air embolism in Doppler heart sound using the wavelet transform

The introduction of air bubbles into the systemic circulation can result in significant morbidity. Real-time monitoring of continuous heart sound in patients detected by precordial Doppler ultrasound is, thus, vital for early detection of venous air embolism (VAE) during surgery. In this study, the multiscale feature of wavelet transforms (WT's) is exploited to examine the embolic Doppler heart sound (DHS) during intravenous air injections in dogs. As both humans and dogs share similar physiological conditions, the authors' methods and results for dogs are expected to be applicable to humans. The WT of DHS at scale 2 j(j=1,2) selectively magnified the power of embolic, but not the normal, heart sound. Statistically, the enhanced embolic power was found to be sensitive (P<0.01 at 0.01 ml of injected air) and correlated significantly (P<0.0005, τ=0.83) with the volume of injected air from 0.01 to 0.10 ml. A fast detection algorithm of O(N) complexity with unit complexity constant for VAE was developed (processing speed=8 ms per heartbeat), which confirmed the feasibility of real-time processing for both humans and dogs.published_or_final_versio

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,published_or_final_versio

Volume Estimation by Wavelet Transform of Doppler Heart Sound During Venous Air-Embolism in Dogs

The Doppler heart sound signals detected by the precordial Doppler ultrasound method under simulated sub clinical and clinically significant venous air embolism were studied in anesthetized dogs. Signal processing using wavelet transform enhanced the contrast of embolic to normal signal, facilitating automatic detection and extraction of embolic signal simply by thresholding. Linear relationship of good correlation coefficient was obtained in log-log scale between the subclinical volume of injected air and the corresponding embolic signal power in all dogs. The calibration curve was found to be good estimate of the volume of embolic air during simulated clinically significant venous air embolism. Hence, we overcame the need of constant human attention for detecting venous air embolism and the lack of quantitative information on the volume of embolic air in the traditional precordial Doppler ultrasound method by the present approach.published_or_final_versio

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.published_or_final_versio

Deactivation of the inferior colliculus by cooling demonstrates intercollicular modulation of neuronal activity

The auditory pathways coursing through the brainstem are organized bilaterally in mirror image about the midline and at several levels the two sides are interconnected. One of the most prominent points of interconnection is the commissure of the inferior colliculus (CoIC). Anatomical studies have revealed that these fibers make reciprocal connections which follow the tonotopic organization of the inferior colliculus (IC), and that the commissure contains both excitatory and, albeit fewer, inhibitory fibers. The role of these connections in sound processing is largely unknown. Here we describe a method to address this question in the anaesthetized guinea pig. We used a cryoloop placed on one IC to produce reversible deactivation while recording electrophysiological responses to sounds in both ICs. We recorded single units, multi-unit clusters and local field potentials (LFPs) before, during and after cooling. The degree and spread of cooling was measured with a thermocouple placed in the IC and other auditory structures. Cooling sufficient to eliminate firing was restricted to the IC contacted by the cryoloop. The temperature of other auditory brainstem structures, including the contralateral IC and the cochlea were minimally affected. Cooling below 20◦C reduced or eliminated the firing of action potentials in frequency laminae at depths corresponding to characteristic frequencies up to ∼8 kHz. Modulation of neural activity also occurred in the un-cooled IC with changes in single unit firing and LFPs. Components of LFPs signaling lemniscal afferent input to the IC showed little change in amplitude or latency with cooling, whereas the later components, which likely reflect inter- and intra-collicular processing, showed marked changes in form and amplitude. We conclude that the cryoloop is an effective method of selectively deactivating one IC in guinea pig, and demonstrate that auditory processing in the IC is strongly influenced by the other

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