A tracing evoked potential estimator

The paper presents an adaptive Gaussian radial basis function neural network (RBFNN) for rapid estimation of evoked potential (EP). Usually, a recorded EP is severely contaminated by background ongoing activities of the brain. Many approaches have been reported to enhance the signal-to-noise ratio (SNR) of the recorded signal. However, non-linear methods are seldom explored due to their complexity and the fact that the non-linear characteristics of the signal are generally hard to determine. An RBFNN possesses built-in non-linear activation functions that enable the neural network to learn any function mapping. An RBFNN was carefully designed to model the EP signal. It has the advantage of being linear-in-parameter, thus a conventional adaptive method can efficiently estimate its parameters. The proposed algorithm is simple so that its convergence behaviour and performance in signal-to-noise ratio (SNR) improvement can be mathematically derived. A series of experiments carried out on simulated and human test responses confirmed the superior performance of the method. In a simulation experiment, an RBFNN having 15 hidden nodes was trained to approximate human visual EP (VEP). For detecting human brain stem auditory EP (BAEP), the approach (40 hidden nodes and convergence rate = 0.005) speeded up the estimation remarkably by using only 80 ensembles to achieve a result comparable to that obtained by averaging 1000 ensembles.     

Tikhonov regularized solutions for improvement of signal-to-noise ratio in case of auditory-evoked potentials

In the present study, standard Tikhonov regularization (STR) Technique and the subspace regularization (SR) method have been applied to remove the additive EEG noise on average auditory-evoked potential (EP) signals. In methodological manner, the difference between these methods is the formation of regularization matrices which are used to solve the weighted problem of EP estimation. Those methods are compared to ensemble averaging (EA) with respect to signal-to-noise-ratio (SNR) improvement in experimental studies, simulations and pseudo-simulations. The results of tests no superiority of the SR in comparison to STR has been observed. In addition, the STR is found to be less computational complex. Moreover, results support the theoretical fact that the STR was introduced to be optimum for smooth solutions whereas the SR allows sharp variations in solutions. Thus, the STR is found to be more useful in removing the noise with the average signal remaining.     

A new vision on the averaging technique for the estimation of non-stationary Brainstem Auditory-Evoked Potentials: application of a metaheuristic method

The aim of this paper consists in highlighting the use of the averaging technique in some biomedical applications, such as evoked potentials (EP) extraction. We show that this technique, which is generally considered as classical, can be very efficient if the dynamic model of the signal to be estimated is a priori known. Therefore, using an appropriate model and under some specific conditions, one can show that the estimation can be performed efficiently even in case of a very low signal to noise ratio (SNR), which occurs when handling Brainstem Auditory-Evoked Potentials.     

A new combination: scale-space filtering of projected brain activities

In the present study, well known scale-space filtering (SSF) algorithm is used in combination with a linear mapping approach (LMA) to obtain clear auditory evoked potential (EP) waveform. The proposed combination involves two sequential steps: At first, the EEG noise level is reduced from −5 to 0 dB owing to the LMA based on the singular-value-decomposition. In the secondary process, the EEG noise remaining on the projected data is removed by using the SSF. A small number of sweeps are composed as a raw matrix to project the data without using the ensemble averaging at the beginning of the proposed method. Then, single sweeps are individually filtered in wavelet domain by using the SSF in the secondary step. The experimental results show that the SSF can extract the clear single-sweep auditory EP waveform where the LMA is used as a primary filtering. As well, the results indicate that the EP signal and background EEG noise create different wavelet coefficients due to their different characteristics. However, this characteristic difference can be considered to distinguish the EP signal and the EEG noise when the Signal-to-Noise-Ratio is higher than 0 dB.     

A tracing evoked potential estimator

The paper presents an adaptive Gaussian radial basis function neural network (RBFNN) for rapid estimation of evoked potential (EP). Usually, a recorded EP is severely contaminated by background ongoing activities of the brain. Many approaches have been reported to enhance the signal-to-noise ratio (SNR) of the recorded signal. However, non-linear methods are seldom explored due to their complexity and the fact that the non-linear characteristics of the signal are generally hard to determine. An RBFNN possesses built-in non-linear activation functions that enable the neural network to learn any function mapping. An RBFNN was carefully designed to model the EP signal. It has the advantage of being linear-in-parameter, thus a conventional adaptive method can efficiently estimate its parameters. The proposed algorithm is simple so that its convergence behaviour and performance in signal-to-noise ratio (SNR) improvement can be mathematically derived. A series of experiments carried out on simulated and human test responses confirmed the superior performance of the method. In a simulation experiment, an RBFNN having 15 hidden nodes was trained to approximate human visual EP (VEP). For detecting gene rate=0.005) speeded up the estimation remarkably by using only 80 ensembles to achieve a result comparable to that obtained by averaging 1000 ensembles.     

用奇异性检测技术提取诱发电位

本文介绍了用 小波变换模极大 值检测信号奇 异性的方法， 讨论了 信号与 白噪声 的小 波变换 和奇 异性指数之间区 别，模极大值沿 尺度 传递 的不同 特点 ，并 利用它 们的 区别 消除 诱发 电位 信号 中的噪 声，重 构出消 噪后 的信号， 取得 了较好 的效果 ，利用 较少的 刺激 次数就 可获取 诱发电 位信 号，有 效地提高 了信 噪比，大 大减 少了迭 加次数 ，特征 波的潜 伏期 及幅值 容易辨 认，易 于测量 ，且无 信号 失真。奇异性检 测技术有望成为 临床实用的诱 发电位提取 技术，并 可应用 于其他 生物 医学信 号的 消噪。     

Activation of the primary and association auditory cortex by the transition of sound intensity: a new method for functional examination of the auditory cortex in humans

During functional MRI image acquisition, the scanning equipment generates substantial auditory noise, the effects of which are usually ignored. To investigate the neural activity in response to the transition of noise, we measured cerebral responses to short silent periods (1 and 5 s) during which the slice readout gradients were switched off. In all 15 normal volunteers, the 1 s silence bilaterally activated the primary and association auditory cortex. Subtraction of the response to the 1 s silent period from that to the 5 s silent period revealed the activation related to the onset (transition of sound from OFF to ON) event, indicating that the 1 s response is offset (transition of sound from ON to OFF) related. The complex response of the auditory cortex to the transition of the noise should be considered in designing functional MRI with auditory tasks.     

Microprocessor-based auditory brainstem response (ABR) simulator

A low-cost, microprocessor-based instrument is designed to simulate the human auditory brainstem response (ABR) to acoustic clicks. the device normally delivers a continuous simulated EEG signal in the absence of click input. When an acoustic click is sensed, its onset and intensity are recognised promptly and an appropriate ABR waveform is superimposed on the ongoing EEG. Fast recognition of onset and intensity are achieved by using analogue detector circuits which issue microprocessor interrupts. The output waveform is synthesised by a 16-bit D/A convertor to preserve realistic signal (ABR) to noise (EEG) ratios. As multiplication operations cannot be performed at speeds appropriate for ABR waveform simulation, superposition is accomplished by position-weighted loading operations in a 16-bit register. The instrument is tested using a microcomputer-based signal averager. it satisfactorily simulates the randomness of single sweeps. Upon averaging enough sweeps, an ABR waveform is obtained. The device, believed to be the first available of its kind, is used to test and calibrate clinical EP systems and is expected to prove useful as a clinical engineering tool in hospitals and medical centres.     

Adaptive denoising for chemical exchange saturation transfer MR imaging

High image signal–to–noise ratio (SNR) is required to reliably detect the inherently small chemical exchange saturation transfer (CEST) effects in vivo. In this study, it was demonstrated that identifying spectral redundancies of CEST data by principal component analysis (PCA) in combination with an appropriate data–driven extraction of relevant information can be used for an effective and robust denoising of CEST spectra. The relationship between the number of relevant principal components and SNR was studied on fitted in vivo Z–spectra with artificially introduced noise. Three different data–driven criteria to automatically determine the optimal number of necessary components were investigated. In addition, these criteria facilitate straightforward assessment of data quality that could provide guidance for CEST MR protocols in terms of SNR. Insights were applied to achieve a robust denoising of highly sampled low power Z–spectra of the human brain at 3 and 7 T. The median criterion provided the best estimation for the optimal number of components consistently for all three investigated artificial noise levels. Application of the denoising technique to in vivo data revealed a considerable increase in image quality for the amide and rNOE contrast with a considerable SNR gain. At 7 T the denoising capability was quantified to be comparable or even superior to an averaging of six measurements. The proposed denoising algorithm enables an efficient and robust denoising of CEST data by combining PCA with appropriate data–driven truncation criteria. With this generally applicable technique at hand, small CEST effects can be reliably detected without the need for repeated measurements.     

Estimation of habituation and signal-to-noise ratio of cortical evoked potentials to oesophageal electrical and mechanical stimulation

Electrical and mechanical stimulation of the oesophagus has been recently proposed to examine the physiological effects of autonomic stimulation in humans. Cortical evoked potentials (EPs) to oesophageal stimulation provide an assessment of afferent fibres and central processing. However, habituation takes place during averaging of cortical EPs and reduces the signal-to-noise ratio (SNR) as the number of stimuli increases. The SNR of cortical EPs to oesophageal stimulation is computed for 15 normal subjects. Habituation is characterised by the Euclidean distance between the EEG response to single stimuli and the averaged EP, to serve as an objective measure of similarity between the averaged EP and the single-stimulus EEG. With electrical stimulation, the SNR is highest (0.41±0.21) for 1–12 stimuli and then significantly decreases to 0.2±0.08 for 13–24 stimuli (p<0.001). With balloon distension (BD), the SNR is highest (0.22±0.16) for 1–12 stimuli and lowest (0.12±0.14) for 13–24 stimuli, but these SNRs are not significantly different from each other. Both electrical and mechanical stimulation of the oesophagus produce rapidly adapting EPs. The SNR of the EPs is higher with electrical stimulation than with BD. The EPs response to BD has a higher variability and is more noisy. Consequently, these results suggest that the overall cortical EP response to electrical stimulation of the oesophagus is more reproducible than that due to balloon distension.     

Modulation of auditory signal-to-noise ratios by efferent stimulation

One of the primary challenges that sensory systems face is extracting relevant information from background noise. In the auditory system, the ear receives efferent feedback, which may help it extract signals from noise. Here we directly test the hypothesis that efferent activity increases the signal-to-noise ratio (SNR) of the ear, using the relatively simple teleost ear. Tone-evoked saccular potentials were recorded before and after efferent stimulation, and the SNR of the responses was calculated. In quiet conditions, efferent stimulation suppressed saccular responses to a tone, reducing the SNR. However, when masking noise was added, efferent stimulation increased the SNR of the saccular responses within a range of stimulus combinations. These data demonstrate that auditory efferent feedback can increase SNR in conditions where a signal is masked by noise, thereby enhancing the encoding of signals in noise. Efferent feedback thus performs a fundamental signal processing function, helping the animal to hear sounds in difficult listening conditions.     

Adaptive Wiener Filtering for Improved Acquisition of Distortion Product Otoacoustic Emissions

An innovative acoustic noise canceling method using adaptive Wiener filtering (AWF) was developed for improved acquisition of distortion product otoacoustic emissions (DPOAEs). The system used one microphone placed in the test ear for the primary signal. Noise reference signals were obtained from three different sources: (a) pre-stimulus response from the test ear microphone, (b) post-stimulus response from a microphone placed near the head of the subject and (c) post-stimulus response obtained from a microphone placed in the subjects nontest ear. In order to improve spectral estimation, block averaging of a different number of single sweep responses was used. DPOAE data were obtained from 11 ears of healthy newborns in a well-baby nursery of a hospital under typical noise conditions. Simultaneously obtained recordings from all three microphones were digitized, stored and processed off-line to evaluate the effects of AWF with respect to DPOAE detection and signal-to-noise ratio (SNR) improvement. Results show that compared to standard DPOAE processing, AWF improved signal detection and improved SNR. © 1998 Biomedical Engineering Society. PAC98: 4364Jb, 4360-c, 8790+y     

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.     

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.     

Wavelet-based enhancement of signalaveraged electrocardiograms for late potential detection

An optimal wavelet filter to improve the signal-to-noise ratio (SNR) of the signal-averaged electrocardiogram is described. As the averaging technique leads to the best unbiased estimator, the challenge is to attenuate the noise while preserving the low amplitude signals that are usually embedded in it. An optimal, in the meansquare sense, wavelet-based filter has been derived from the model of the signal. However, such a filter needs exact knowledge of the noise statistic and the noise-free signal. Hence, to implement such a filter, a method based on successive subaveraging and wavelet filtering is proposed. Its performance was evaluated using simulated and real ECGs. An improvement in SNR of between 6 and 10 dB can be achieved compared to a classical averaging technique which uses an ensemble of 64 simulated ECG beats. Tests on real ECGs demonstrate the utility of the method as it has been shown that by using fewer beats in the filtered ensemble average, one can achieve the same noise reduction. Clinical use of this technique would reduce the ensemble needed for averaging while obtaining the same diagnostic result.     

Wavelet-based enhancement of signal-averaged electrocardiograms for late potential detection

An optimal wavelet filter to improve the signal-to-noise ratio (SNR) of the signal-averaged electrocardiogram is described. As the averaging technique leads to the best unbiased estimator, the challenge is to attenuate the noise while preserving the low amplitude signals that are usually embedded in it. An optimal, in the mean-square sense, wavelet-based filter has been derived from the model of the signal. However, such a filter needs exact knowledge of the noise statistic and the noise-free signal. Hence, to implement such a filter, a method based on successive sub-averaging and wavelet filtering is proposed. Its performance was evaluated using simulated and real ECGs. An improvement in SNR of between 6 and 10 dB can be achieved compared to a classical averaging technique which uses an ensemble of 64 simulated ECG beats. Tests on real ECGs demonstrate the utility of the method as it has been shown that by using fewer beats in the filtered ensemble average, one can achieve the same noise reduction. Clinical use of this technique would reduce the ensemble needed for averaging while obtaining the same diagnostic result.     

Filtering electrocardiographic signals using an unbiased and normalized adaptive noise reduction system

We present a novel unbiased and normalized adaptive noise reduction (UNANR) system to suppress random noise in electrocardiographic (ECG) signals. The system contains procedures for the removal of baseline wander with a two-stage moving-average filter, comb filtering of power-line interference with an infinite impulse response (IIR) comb filter, an additive white noise generator to test the system's performance in terms of signal-to-noise ratio (SNR), and the UNANR model that is used to estimate the noise which is subtracted from the contaminated ECG signals. The UNANR model does not contain a bias unit, and the coefficients are adaptively updated by using the steepest-descent algorithm. The corresponding adaptation process is designed to minimize the instantaneous error between the estimated signal power and the desired noise-free signal power. The benchmark MIT-BIH arrhythmia database was used to evaluate the performance of the UNANR system with different levels of input noise. The results of adaptive filtering and a study on convergence of the UNANR learning rate demonstrate that the adaptive noise-reduction system that includes the UNANR model can effectively eliminate random noise in ambulatory ECG recordings, leading to a higher SNR improvement than that with the same system using the popular least-mean-square (LMS) filter. The SNR improvement provided by the proposed UNANR system was higher than that provided by the system with the LMS filter, with the input SNR in the range of 5-20 dB over the 48 ambulatory ECG recordings tested.     

A novel approach for the averaging of magnetocardiographically recorded heart beats

Performing signal averaging in an efficient and correct way is indispensable since it is a prerequisite for a broad variety of magnetocardiographic (MCG) analysis methods. One of the most common procedures for performing the signal averaging to increase the signal-to-noise ratio (SNR) in magnetocardiography, as well as in electrocardiography (ECG), is done by means of spatial or temporal techniques. In this paper, an improvement of the temporal averaging method is presented. In order to obtain an accurate signal detection, temporal alignment methods and objective classification criteria are developed. The processing technique based on hierarchical clustering is introduced to take into account the non-stationarity of the noise and, to some extent, the biological variability of the signals reaching the optimum SNR. The method implemented is especially designed to run fast and does not require any interaction from the operator. The averaging procedure described in this work is applied to the averaging of MCG data as an example, but with its intrinsic properties it can also be applied to the averaging of ECG recording, averaging of body-surface-potential mapping (BSPM) and averaging of magnetoencephalographic (MEG) or electroencephalographic (EEG) signals.     

Short-term sound temporal envelope characteristics determine multisecond time patterns of activity in human auditory cortex as shown by fMRI

Functional magnetic resonance imaging (fMRI) of human auditory cortex has demonstrated a striking range of temporal waveshapes in responses to sound. Prolonged (30 s) low-rate (2/s) noise burst trains elicit "sustained" responses, whereas high-rate (35/s) trains elicit "phasic" responses with peaks just after train onset and offset. As a step toward understanding the significance of these responses for auditory processing, the present fMRI study sought to resolve exactly which features of sound determine cortical response waveshape. The results indicate that sound temporal envelope characteristics, but not sound level or bandwidth, strongly influence response waveshapes, and thus the underlying time patterns of neural activity. The results show that sensitivity to sound temporal envelope holds in both primary and nonprimary cortical areas, but nonprimary areas show more pronounced phasic responses for some types of stimuli (higher-rate trains, continuous noise), indicating more prominent neural activity at sound onset and offset. It has been hypothesized that the neural activity underlying the onset and offset peaks reflects the beginning and end of auditory perceptual events. The present data support this idea because sound temporal envelope, the sound characteristic that most strongly influences whether fMRI responses are phasic, also strongly influences whether successive stimuli (e.g., the bursts of a train) are perceptually grouped into a single auditory event. Thus fMRI waveshape may provide a window onto neural activity patterns that reflect the segmentation of our auditory environment into distinct, meaningful events.     

A lifespan comparison of the reliability,test‐retest stability,and signal‐to‐noise ratio of event‐related potentials assessed during performance monitoring

The reliability, stability, and signal‐to‐noise ratio (SNR) of event‐related potentials (ERPs) were investigated in children, adolescents, younger adults, and older adults in performance monitoring tasks. P2, N2, P3, and P2‐N2 peak‐to‐peak amplitude showed high odd‐even split reliabilities in all age groups, ranging from.70 to.90. Multigroup analyses showed that test‐retest stabilities (across 2 weeks) of ERP amplitudes did not differ among the four age groups. In contrast, relative to adolescents and younger adults, SNRs were lower in children and older adults, with higher noise levels in children and lower signal power in older adults. We conclude that age differences in the SNR of stimulus‐locked ERPs can be successfully compensated by the averaging procedure with about 40 trials in the average. However, age differences in baseline noise and split‐half reliability should be considered when comparing age groups in single trial measures or time‐varying processes with ERPs.