Detection of brainstem auditory evoked potential by adaptive filtering

Effect of subject state on auditory brainstem response threshold using Kalman-weighted averaging

OBJECTIVES

This study aims to explore the impact of a subject's testing state on auditory brainstem response (ABR) thresholds using a novel ABR system (Vivosonic Integrity™), which incorporates Kalman-weighted averaging and bluetooth electrical isolation to address the limitation of conventional ABR limitation to obtain a stable result under non-sedated conditions, especially for infants and children.

METHOD

Twenty-four adults (18-34 years old, 48 ears) with normal hearing were enrolled for ABR testing under three different states (lying quietly in the supine position or sleeping-lying; watching silent videos quietly in a seated position-sitting; and writing in a seated position-writing), which simulate the behaviors of young children most often encountered during non-sedated Kalman-weighted ABR testing in clinical practice. The click ABR (cABR) and tone-burst ABR (tbABR) thresholds (0.5, 1, 2, and 4 kHz) of each subject and the time taken to reach the monaural threshold for each kind of stimulus were recorded.

RESULTS

(1) The cABR and tbABR thresholds were observed to increase in the following order: lying < sitting < writing. Significant threshold differences were found between any two states, except for between the sitting and lying states for the cABR and between sitting and writing for the 0.5 kHz tbABR. (2) The time required for cABR testing in the writing state was significantly longer than that in the lying and sitting states. The time required for 1 and 4 kHz tbABR testing in the lying state was significantly shorter than that in the sitting or writing state. For 2 KHz tbABR, only testing time under writing was significantly longer than that under lying. There were no significant differences in the time used for 0.5 kHz tbABR testing among different states.

CONCLUSIONS

Different testing states have significant impacts on the thresholds of ABRs using Kalman-weighted averaging. A subject's state during ABR testing warrants consideration, and normal levels and correction values to estimate the hearing threshold from the ABR threshold should be determined for different testing states.

40-Hz Sinusoidal Auditory Steady-State Response and Tone Burst Auditory Brainstem Response Using a Kalman Filter to Determine Thresholds Pre- and Post-Myringotomy With Grommet Tube in Children With Mild, Low-Frequency Conductive Hearing Loss

PURPOSE

Accurate estimation of mild, low-frequency hearing loss is difficult in young children. This study aimed to determine the accuracy of 40-Hz sinusoidal auditory steady-state response (sASSR) compared with tone burst auditory brainstem response (TB-ABR) to detect mild, low-frequency hearing loss in children with otitis media with effusion and to measure postoperative thresholds.

METHODS

Thresholds at 500 and 4000 Hz were measured behaviorally and electrophysiologically using TB-ABR and 40-Hz sASSR with a Kalman filter in 26 children with otitis media with effusion. Recording was conducted preoperatively and postoperatively while children were actively awake. Repeated measures mixed analyses of variance were conducted to determine effects among measures and the two test frequencies.

RESULTS

Both 40-Hz sASSR and TB-ABR accurately detected preoperative and postoperative thresholds and were within 5-10 dB of the behavioral thresholds at 4000 Hz. At 500 Hz, the mean 40-Hz sASSR threshold was only 5 dB above the behavioral thresholds and 18 dB better than the 500-Hz ABR threshold. Positive correlations were found but not between 40-sASSR and TB-ABR at 500 Hz. Also, the interrater judgment of the response was better for sASSR (89%) than TB-ABR (83%).

CONCLUSION

The 40-Hz sASSR is more accurate than TB-ABR in determining a mild, low-frequency threshold.

Assessment of low-frequency hearing with narrow-band chirp-evoked 40-Hz sinusoidal auditory steady-state response

Objective To determine the clinical utility of narrow-band chirp-evoked 40-Hz sinusoidal auditory steady state responses (s-ASSR) in the assessment of low-frequency hearing in noisy participants. Design Tone bursts and narrow-band chirps were used to respectively evoke auditory brainstem responses (tb-ABR) and 40-Hz s-ASSR thresholds with the Kalman-weighted filtering technique and were compared to behavioral thresholds at 500, 2000, and 4000 Hz. A repeated measure ANOVA and post-hoc t-tests, and simple regression analyses were performed for each of the three stimulus frequencies. Study sample Thirty young adults aged 18-25 with normal hearing participated in this study. Results When 4000 equivalent response averages were used, the range of mean s-ASSR thresholds from 500, 2000, and 4000 Hz were 17-22 dB lower (better) than when 2000 averages were used. The range of mean tb-ABR thresholds were lower by 11-15 dB for 2000 and 4000 Hz when twice as many equivalent response averages were used, while mean tb-ABR thresholds for 500 Hz were indistinguishable regardless of additional response averaging. Conclusion Narrow-band chirp-evoked 40-Hz s-ASSR requires a ∼15 dB smaller correction factor than tb-ABR for estimating low-frequency auditory threshold in noisy participants when adequate response averaging is used.

Measuring the Advantage of Kalman-Weighted Averaging for Auditory Brainstem Response Hearing Evaluation in Infants

PURPOSE

The purposes of this study were to (a) measure the effects of Kalman-weighted averaging methods on auditory brainstem response (ABR) threshold, latency, and amplitude; (b) translate lab findings to the clinical setting; and (c) estimate cost savings when ABRs can be obtained in nonsedated infants.

METHOD

ABRs were recorded in 40 adults with normal hearing during induced motor noise conditions using the Kalman-weighted averaging method implemented on a commercial system, the Vivosonic Integrity (Vivosonic Inc., Toronto, Ontario, Canada). The device was then used to test 34 infants in awake and asleep states. The advantages of the Kalman-weighted averaging method were modeled in terms of time saved for conducting an ABR evaluation.

RESULTS

Kalman-weighted ABR threshold estimates were 6-7 dB lower than with conventional methods during induced motor noise. When used to obtain ABRs in infants who were awake, the number of sweeps required to obtain a result was significantly greater than that required for a sleeping infant but well within the range for clinical application.

CONCLUSIONS

The use of Kalman-weighted averaging provides a measurable advantage over conventional methods and may reduce costs for the pediatric audiology practice.

Automatic quality assessment and peak identification of auditory brainstem responses with fitted parametric peaks

The recording of the auditory brainstem response (ABR) is used worldwide for hearing screening purposes. In this process, a precise estimation of the most relevant components is essential for an accurate interpretation of these signals. This evaluation is usually carried out subjectively by an audiologist. However, the use of automatic methods for this purpose is being encouraged nowadays in order to reduce human evaluation biases and ensure uniformity among test conditions, patients, and screening personnel. This article describes a new method that performs automatic quality assessment and identification of the peaks, the fitted parametric peaks (FPP). This method is based on the use of synthesized peaks that are adjusted to the ABR response. The FPP is validated, on one hand, by an analysis of amplitudes and latencies measured manually by an audiologist and automatically by the FPP method in ABR signals recorded at different stimulation rates; and on the other hand, contrasting the performance of the FPP method with the automatic evaluation techniques based on the correlation coefficient, FSP, and cross correlation with a predefined template waveform by comparing the automatic evaluations of the quality of these methods with subjective evaluations provided by five experienced evaluators on a set of ABR signals of different quality. The results of this study suggest (a) that the FPP method can be used to provide an accurate parameterization of the peaks in terms of amplitude, latency, and width, and (b) that the FPP remains as the method that best approaches the averaged subjective quality evaluation, as well as provides the best results in terms of sensitivity and specificity in ABR signals validation. The significance of these findings and the clinical value of the FPP method are highlighted on this paper.

Signal-to-noise ratio of intraoperative tibial nerve somatosensory-evoked potentials

To reveal the intrinsic signal-to-noise ratio (SNR) of single-trial somatosensory-evoked potentials (SEP). SEP was recorded from 13 scoliosis patients during surgery. The power of SEP was estimated with least-square fitting to obtain the most accurate value and then to estimate the SNR of every trial of SEP. The SNR of cortical SEP from 13 cases presented individual difference among each other. According to the mean and standard deviation, the coefficients of variation of cortical and subcortical SEP were 4.2% and 23%, respectively. The SNR of SEP was estimated to be -24 +/- 1 dB in cortical SEP and -22 +/- 5 dB in subcortical SEP. The lowest SNR of individual case was found to be -30 dB in cortical SEP and -53 dB in subcortical SEP. The results showed that SNR of intraoperative SEP recordings varies from person to person and presents a higher variability in subcortical than that in cortical, with a broad range from -53 to -5 dB. The results from this study can be used to understand the nature of SEP signals, which could guide researchers and designers on SEP denoising method selection, extraction, and measurement, as well as equipment development.

Estimation of postaverage SNR from evoked responses under nonstationary noise

In any measure of event-related potentials, it is important to be able to estimate the postaverage signal-to-noise ratio (SNR) in order to assess the quality of the measured signals. The estimated postaverage SNR can be an important detection criteria (as in infant hearing-screening of evoked auditory potentials) and a control factor when comparing signals obtained during different conditions (accounting for residual noise variability). Standard SNR estimation methods, such as the fixed-single-point (Fsp) statistic (C. Elberling and M. Don, "Quality estimation of averaged auditory brainstem responses," Scandinavian Audiol., vol. 13, pp. 187-197, 1984), assume a single-stationary noise source, with the postaverage SNR increasing proportionally to the number of trials averaged. This study proposes a modified version of the Fsp statistic, the nonstationary fixed-multiple-point (NS Fmp), that can account for a discrete number of noise sources of different power, and can also be modified for weighted averaging (WNS Fmp). A new noise segmentation procedure is also proposed that dynamically partitions contiguous trials based on their noise power estimates and a series of F-tests. Results from computer simulation and real data from auditory brain stem recordings show that the NS Fmp method yields lower mean square error than do the Fsp, and that the WNS Fmp has higher receiver-operating-curve area than do the standard Fsp procedure.

Real-time somatosensory evoked potential monitoring using FPGA-based adaptive filter

This study was to analyze the performance of fixed-point adaptive signal enhancer (ASE) for somsatosensory evoked potential (SEP) measurement. An animal study was conducted to evaluate the usefulness of this technique in detection of spinal cord injury. The ASE technique has been reported powerful to improve signal to noise ratio of SEP. Therefore, we proposed an integrated circuit. module to perform real time ASE for fast SEP measurement, and a fast fixed-point algorithm was employed in the Field Programmable Gate Array design. We compared the SEP results from FPGA-beased ASE methods and the conventional ensemble averaging (EA) method during an animal experiment. Experimental results on a spinal cord compressive injury model showed that the FPGA-based ASE methods have superior performance over the EA method. It would be helpful to design fixed-point ASE based on FPGA for real-time monitoring of intraoperative SEP.

Fast extraction of somatosensory evoked potential based on second order blind identification

Second order blind identification (SOBI) technique is a promising independent component analysis (ICA) method to extract somatosensory evoked potential (SEP). This simulation study focused on SEP extraction from EEG and power-line noise contaminated SEP signals at signal to noise ratio (SNR) of -10dB and -20dB. The correlation coefficients between template SEP and SOBI extracted SEP showed significant high similarity (r>0.76) at -10dB and mild acceptable similarity (r>0.6) at -20dB EEG contaminated SEP. However, SOBI extracted SEP showed good performance in power-line noise situation to achieve high correlation coefficients with template SEP (r=0.96). The fast extracted SEP showed stable amplitude and latency, which are almost identical with the SEP template. The results suggested that SOBI is an appropriate method to extract SEP from noisy background.

Visualization of lumbar muscle contraction synergy using surface electromyography (sEMG) streaming topography

Because of the difficulty in analysis and interpretation of surface electromyography (sEMG), the specific muscle contraction synergy associated with low back pain continues to be debated. Streaming topography is a novel method of continuously visualizing the distribution of sEMG signals during dynamic motion to provide a more comprehensive examination and subsequent insight into the synergy of muscle recruitment pattern. The purpose of this study was to assess the feasibility of streaming topography as a diagnostic tool. Ten healthy subjects were recruited to establish the normal pattern of lumbar muscle activity. An array of surface EMG electrodes was applied to the low back region and recorded during forward bending. The root mean square (RMS) of the sEMG signals were calculated as a function of both position and time to produce streaming topographical videos of the muscle activity in the lumbar region. In addition, a preliminary clinical study was carried out with 3 LBP patients. In normal subjects, RMS streaming topography was consistent, reproducible, and reliable. In clinical observation, the RMS streaming topography of LBP patients was obviously different from that of normal subjects. Some of LBP patients showed an asymmetric distribution during symmetric action. Streaming topography provides a dynamic analysis of lumbar muscle activities and illustrates the synergy of muscle contractions, which may be useful to improve physiotherapy management of LBP.

Selection of floating-point or fixed-point for adaptive noise canceller in somatosensory evoked potential measurement

Adaptive noise canceller (ANC) has been used to improve signal to noise ratio (SNR) of somsatosensory evoked potential (SEP). In order to efficiently apply the ANC in hardware system, fixed-point algorithm based ANC can achieve fast, cost-efficient construction, and low-power consumption in FPGA design. However, it is still questionable whether the SNR improvement performance by fixed-point algorithm is as good as that by floating-point algorithm. This study is to compare the outputs of ANC by floating-point and fixed-point algorithm ANC when it was applied to SEP signals. The selection of step-size parameter (micro) was found different in fixed-point algorithm from floating-point algorithm. In this simulation study, the outputs of fixed-point ANC showed higher distortion from real SEP signals than that of floating-point ANC. However, the difference would be decreased with increasing micro value. In the optimal selection of micro, fixed-point ANC can get as good results as floating-point 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.

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.

Real-time ocular artifact suppression using recurrent neural network for electro-encephalogram based brain-computer interface

The paper presents an adaptive noise canceller (ANC) filter using an artificial neural network for real-time removal of electro-oculogram (EOG) interference from electro-encephalogram (EEG) signals. Conventional ANC filters are based on linear models of interference. Such linear models provide poorer prediction for biomedical signals. In this work, a recurrent neural network was employed for modelling the interference signals. The eye movement and eye blink artifacts were recorded by the placing of an electrode on the forehead above the left eye and an electrode on the left temple. The reference signal was then generated by the data collected from the forehead electrode being added to data recorded from the temple electrode. The reference signal was also contaminated by the EEG. To reduce the EEG interference, the reference signal was first low-pass filtered by a moving averaged filter and then applied to the ANC. Matlab Simulink was used for real-time data acquisition, filtering and ocular artifact suppression. Simulation results show the validity and effectiveness of the technique with different signal-to-noise ratios (SNRs) of the primary signal. On average, a significant improvement in SNR up to 27 dB was achieved with the recurrent neural network. The results from real data demonstrate that the proposed scheme removes ocular artifacts from contaminated EEG signals and is suitable for real-time and short-time EEG recordings.

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.

Validation of an Adaptive Signal Enhancer in Intraoperative Somatosensory Evoked Potentials Monitoring

The conventional approach of ensemble averaging in intraoperative somatosensory evoked potentials (SEP) monitoring requires more than 500 trials to extract a reliable waveform for neurologic diagnosis. Previous studies showed that an adaptive signal enhancer (ASE) could increase the signal-to-noise ratio of input signals. This study assessed the accuracy and efficiency of the ASE in the extraction of neurologic normal human and abnormal rat SEP. Cortical and subcortical SEP were taken from 16 subjects undergoing scoliosis surgery. SEP extracted by ASE were compared with those obtained with 500-trial averaging in terms of peak latency, amplitude, and waveforms using correlation coefficients. An animal study composed of 18 rats was used to test the ASE in detecting abnormal SEP changes due to spinal cord compression. The results demonstrate the accuracy of ASE by showing very high correlations between ASE-processed SEP and ensemble averaging-processed SEP in waveforms, peak latencies, and amplitudes. The results also show the efficiency of the ASE in extracting SEP waveforms from 50 input trials, which provided waveforms of sufficiently high quality and latency/amplitude measurements equivalent to those obtained in 500 trials of conventional ensemble averaging. Because of its fast extraction ability, adaptive signal enhancement could be an appropriate alternative to conventional ensemble averaging in intraoperative spinal cord monitoring.

Objective detection of brainstem auditory evoked potentials with a priori information from higher presentation levels

This paper describes a brainstem auditory evoked potentials (BAEPs) detection method based on supervised pattern recognition. A previously used pattern recognition technique relying on cross-correlation with a template was modified in order to include a priori information allowing detection accuracy. Reference is made to the patient's audiogram and to the latency-intensity (LI) curve with respect to physiological mechanisms. Flexible and adaptive constraints are introduced in the optimization procedure by means of eight rules. Several data samples were used in this study. The determination of parameters was performed through 270 BAEPs from 20 subjects with normal and high audiometric thresholds and through additional BAEPs from 123 normal ears and 14 ears showing prominent wave VI BAEPs. The evaluation of the detection performance was performed in two steps: first, the sensitivity, specificity and accuracy were estimated using 283 BAEPs from 20 subjects showing normal and high audiometric thresholds and secondly, the sensitivity, specificity and accuracy of the detection and the accuracy of the response threshold were estimated using 213 BAEPs from 18 patients in clinic. Taking into account some a priori information, the accuracy in BAEPs detection was enhanced from 76 to 90%. The patient response thresholds were determined with a mean error of 5 dB and a standard deviation error of 8.3 dB. Results were obtained using experimental data; therefore, they are promising for routine use in clinic.

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.

A method for removing imaging artifact from continuous EEG recorded during functional MRI

Combined EEG/fMRI recording has been used to localize the generators of EEG events and to identify subject state in cognitive studies and is of increasing interest. However, the large EEG artifacts induced during fMRI have precluded simultaneous EEG and fMRI recording, restricting study design. Removing this artifact is difficult, as it normally exceeds EEG significantly and contains components in the EEG frequency range. We have developed a recording system and an artifact reduction method that reduce this artifact effectively. The recording system has large dynamic range to capture both low-amplitude EEG and large imaging artifact without distortion (resolution 2 microV, range 33.3 mV), 5-kHz sampling, and low-pass filtering prior to the main gain stage. Imaging artifact is reduced by subtracting an averaged artifact waveform, followed by adaptive noise cancellation to reduce any residual artifact. This method was validated in recordings from five subjects using periodic and continuous fMRI sequences. Spectral analysis revealed differences of only 10 to 18% between EEG recorded in the scanner without fMRI and the corrected EEG. Ninety-nine percent of spike waves (median 74 microV) added to the recordings were identified in the corrected EEG compared to 12% in the uncorrected EEG. The median noise after artifact reduction was 8 microV. All these measures indicate that most of the artifact was removed, with minimal EEG distortion. Using this recording system and artifact reduction method, we have demonstrated that simultaneous EEG/fMRI studies are for the first time possible, extending the scope of EEG/fMRI studies considerably.

Subspace regularization method for the single-trial estimation of evoked potentials

A method for the single-trial estimation of the evoked potentials is proposed. The method is based on the so-called subspace regularization approach in which the second-order statistics of the set of the measurements is used to form a prior information model for the evoked potentials. The method is closely related to the Bayesian estimation. The performance of the proposed method is evaluated using realistic simulations. As a specific application the method is applied to the estimation of the target responses in the P300 test.

Evoked potential estimation using modified time-sequenced adaptive filter

A method called modified time-sequenced adaptive filtering (MTSAF) is applied to estimate evoked potential (EP) signals and track the temporal variations of EPs. The MTSAF consists of a set of adaptive filters (AFs), with each processing a time segment of EP data. After convergence, each AF reaches the best estimation of EP signals over its own time segment in terms of minimum mean squared error (MMSE). Numerical results of simulated and human EP data show that the MTSAF reaches better estimation of EPs than a conventional adaptive signal enhancer (ASE). With the MTSAF, the temporal variations of EPS across trials can be estimated to reveal more subtle variations of EPs, which may be of clinical value.