Auditory Brainstem Response Signals Research Articles

Automatic Recognition of Auditory Brainstem Response Waveforms Using a Deep Learning-Based Framework.

Recognition of auditory brainstem response (ABR) waveforms may be challenging, particularly for older individuals or those with hearing loss. This study aimed to investigate deep learning frameworks to improve the automatic recognition of ABR waveforms in participants with varying ages and hearing levels. The research used a descriptive study design to collect and analyze pure tone audiometry and ABR data from 100 participants. The research was conducted at a tertiary academic medical center, specifically at the Clinical Audiology Center of Tsinghua Chang Gung Hospital (Beijing, China). Data from 100 participants were collected and categorized into four groups based on age and hearing level. Features from both time-domain and frequency-domain ABR signals were extracted and combined with demographic factors, such as age, sex, pure-tone thresholds, stimulus intensity, and original signal sequences to generate feature vectors. An enhanced Wide&Deep model was utilized, incorporating the Light-multi-layer perceptron (MLP) model to train the recognition of ABR waveforms. The recognition accuracy (ACC) of each model was calculated for the overall data set and each group. The ACC rates of the Light-MLP model were 97.8%, 97.2%, 93.8%, and 92.0% for Groups 1 to 4, respectively, with a weighted average ACC rate of 95.4%. For the Wide&Deep model, the ACC rates were 93.4%, 90.8%, 92.0%, and 88.3% for Groups 1 to 4, respectively, with a weighted average ACC rate of 91.0%. Both the Light-MLP model and the Wide&Deep model demonstrated excellent ACC in automatic recognition of ABR waveforms across participants with diverse ages and hearing levels. While the Wide&Deep model's performance was slightly poorer than that of the Light-MLP model, particularly due to the limited sample size, it is anticipated that with an expanded data set, the performance of Wide&Deep model may be further improved.

Hearing Screening through Frequency Analysis of Auditory Brainstem Response Using PhysioNet Data

Objectives. Responding to the reality of neonate patients with delayed childhood development due to late diagnosis of and intervention on hearing impairment, this study aims to determine the features based on time-frequency domain of auditory brainstem response (ABR) signals and to test the protocol on ABR signals from PhysioNet.Methods. This is done by pre-processing, performing time-frequency analysis, and characterizing hearing impairment using the dominant features of the ABR. In this study, normal (N) and hearing impaired (HI) ABR adult human signals were acquired from Physionet.org, a publicly available database. Considering its high signal-to-noise ratio, numerous filters and transformations were applied to extract the ABR. Consequently, the features acquired — dominant frequency and bigrams, were used as data classifiers.Results. Initial results using only N classifiers, that is features from the Normal dataset, and bandpass Chebyshev filter with a lower cut-off frequency of 60 Hz show that the tests yielded low to middle sensitivity. Further tests were done to improve the sensitivity that incorporated the HI classifiers, used data filtered with a low cut-off frequency of 300 Hz, and data divided per stimulus intensity level.Conclusion. Conclusions made are 1) data with both N and HI classifiers have higher sensitivity than those using only N classifiers, 2) data with a Chebyshev cut-off frequency of 300 Hz have a higher sensitivity than those with 60 Hz, and 3) data divided per intensity level have a higher sensitivity than data analyzed as a whole, and that features with stimulus intensity in middle ranges have a better distinction between HI and N patients.

A Robust Extraction Approach of Auditory Brainstem Response Using Adaptive Kalman Filtering Method.

The Auditory brainstem response (ABR) can provide valuable information on the function of the auditory pathway. However, the ABR signal has a very small amplitude, and it is easily submerged in different background noises with large amplitude. Conventional ABR extraction methods such as time-domain averaging (TDA) and Kalman filter (KF) were greatly affected by noise intensity, and the result relies on the empirical settings of parameters. ABR extraction method that can automatically adjust parameters to adapt different background noises was needed. An adaptive Kalman filtering (AKF) based ABR signal extraction method was proposed, in which two recursive rules were introduced to constantly update the parameters according to the real-time noise properties. It was used for ABR extraction from recordings in noises with different orders of larger magnitude. The AKF method demonstrated the best performance in obtaining reliable ABR waveform morphologies in the presence of large EMG noises compared with traditional methods of TDA or KF. It could extract satisfactory ABR signal with fewer trials of acoustic stimulus repetition, even from noise 10000 times larger than ABR signal. The AKF results also showed smaller absolute errors and higher correlation coefficients with the target ABR signal when different types (gum chewing, mouth opening and milk drinking) or levels of noises were introduced. The proposed AKF method is a great candidate to increase the robustness of current ABR measurements. It could provide reduced testing time and relaxed recording conditions for ABR and other evoked potentials extraction.

The Effects of Different Reference Methods on Decision-Making Implications of Auditory Brainstem Response.

Hearing loss is a common disease affecting public health all around the world. In clinic, auditory brainstem response (ABR) has been widely used for the detection of hearing loss based on its convenience and accuracy. The different reference methods directly influence the quality of the ABR waveform which in turn affects the ABR-based diagnosis. Therefore, in this study, a reference electrode standardization technique (REST) was adopted to systematically investigate and evaluate the effect of different reference methods on the quality of ABR waveform in comparison with the conventional average reference (AR) and mean mastoid (MM) methods. In this study, ABR signals induced by click stimulus were acquired via an EEG electrode cap arrays, and those located on the six channels along the midline were compared systemically. The results showed that, when considering the different channels, the ABR in the Cz channel showed the best morphology. Then, the ABR waveforms acquired via the REST method possessed better morphologies with large amplitude (0.06 ± 0.02 μV for wave I, 0.07 ± 0.02 μV for wave III, and 0.21 ± 0.04 μV for wave V) when compared with the traditional method. Summarily, we found that the REST and MM methods improved the quality of ABR on both amplitude and morphology under different stimulation rates and levels without changing the latencies of ABR when compared with the conventional AR method, suggesting that the REST and MM methods have the potential to help physicians with high accurate ABR-based clinical diagnosis. Moreover, this study might also provide a theoretic basis of reference methods on the acquisition of electroencephalogram over public health issues.

The Effects of Random Stimulation Rate on Measurements of Auditory Brainstem Response.

Electroencephalography (EEG) signal is an electrophysiological recording from electrodes placed on the scalp to reflect the electrical activities of the brain. Auditory brainstem response (ABR) is one type of EEG signals in response to an auditory stimulus, and it has been widely used to evaluate the potential disorders of the auditory function within the brain. Currently, the ABR measurements in the clinic usually adopt a fixed stimulation rate (FSR) technique in which the late evoked response could contaminate the ABR signals and deteriorate the waveform differentiation after averaging, thus compromising the overall auditory function assessment task. To resolve this issue, this study proposed a random stimulation rate (RSR) method by integrating a random interval between two adjacent stimuli. The results showed that the proposed RSR method was consistently repeatable and reliable in multiple trials of repeated measurements, and there was a large amplitude of successive late evoked response that would contaminate the ABR signals for conventional FSR methods. The ABR waveforms of the RSR method showed better wave I–V morphology across different stimulation rates and stimulus levels, and the improved ABR morphology played an important role in early diagnoses of auditory pathway abnormities. The correlation coefficients as functions of averaging time showed that the ABR waveform of the RSR method stabilizes significantly faster, and therefore, it could be used to speed up current ABR measurements with more reliable testing results. The study suggests that the proposed method would potentially aid the adequate reconstruction of ABR signals towards a more effective means of hearing loss screening, brain function diagnoses, and potential brain–computer interface.

Nicotinic acetylcholine receptor subunit α7-knockout mice exhibit degraded auditory temporal processing.

The CHRNA7 gene that encodes the α7-subunit of the nicotinic acetylcholine receptor (α7-nAChR) has been associated with some autism spectrum disorders and other neurodevelopmental conditions characterized, in part, by auditory and language impairment. These conditions may include auditory processing disorders that represent impaired timing of neural activity, often accompanied by problems understanding speech. Here, we measure timing properties of sound-evoked activity via the auditory brainstem response (ABR) of α7-nAChR knockout mice of both sexes and wild-type colony controls. We find a significant timing delay in evoked ABR signals that represents midbrain activity in knockouts. We also examine spike-timing properties of neurons in the inferior colliculus, a midbrain nucleus that exhibits high levels of α7-nAChR during development. We find delays of evoked responses along with degraded spiking precision in knockout animals. We find similar timing deficits in responses of neurons in the superior paraolivary nucleus and ventral nucleus of the lateral lemniscus, which are brainstem nuclei thought to shape temporal precision in the midbrain. In addition, we find that other measures of temporal acuity including forward masking and gap detection are impaired for knockout animals. We conclude that altered temporal processing at the level of the brainstem in α7-nAChR-deficient mice may contribute to degraded spike timing in the midbrain, which may underlie the observed timing delay in the ABR signals. Our findings are consistent with a role for the α7-nAChR in types of neurodevelopmental and auditory processing disorders and we identify potential neural targets for intervention.NEW & NOTEWORTHY Disrupted signaling via the α7-nicotinic acetylcholine receptor (α7-nAChR) is associated with neurodevelopmental disorders that include impaired auditory processing. The underlying causes of dysfunction are not known but a common feature is abnormal timing of neural activity. We examined temporal processing of α7-nAChR knockout mice and wild-type controls. We found degraded spike timing of neurons in knockout animals, which manifests at the level of the auditory brainstem and midbrain.

Clinicians' Guide to Obtaining a Valid Auditory Brainstem Response to Determine Hearing Status: Signal, Noise, and Cross-Checks.

The auditory brainstem response (ABR) is a powerful tool for making clinical decisions about the presence, degree, and type of hearing loss in individuals in whom behavioral hearing thresholds cannot be obtained or are not reliable. Although the test is objective, interpretation of the results is subjective. This review provides information about evidence-based criteria, suggested by the 2013 Newborn Hearing Screening Program guidelines, and the use of cross-check methods for making valid interpretations about hearing status from ABR recordings. The use of an appropriate display scale setting, templates of expected response properties, and objective criteria to estimate the residual noise, signal level, and signal-to-noise ratio will provide quality data for determining ABR thresholds. Cross-checks (e.g., immittance measures, otoacoustic emissions testing, functional indications of a child's hearing) are also needed to accurately interpret the ABR. Using evidence-based ABR signal detection criteria and considering the results within the context of other physiologic tests and assessments of hearing function will improve the clinician's accuracy for detecting hearing loss and, when present, the degree of hearing loss. Diagnostic accuracy will ensure that appropriate remediation is initiated and that children or infants with normal hearing are not subjected to unnecessary intervention.

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.

AUTOMATED AVERAGING OF AUDITORY EVOKED RESPONSE WAVEFORMS USING WAVELET ANALYSIS

The auditory brainstem response (ABR) is widely used as an index to assist hearing and brain function diagnoses. In particular, in clinical applications, the rapid detection of ABR peak characteristics is required. One approach to improving the speed of detection is to decrease the number of signal averaging procedures while denoising during the detection of ABR waveforms; another approach is to extract the characteristics of ABR waveform components. In our previous study, to represent ABR waveform components, we obtained not only the frequency characteristics of an ABR but also the frequency characteristics of each component of the ABR based on the time (latency). Using a one-dimensional discrete wavelet transform (DWT) in this latency-frequency analysis, we described an approximate method of reproducing ABR signals with a low SNR from observed values obtained with a smaller number of averaging procedures. At the same time, using this multiple-level frequency decomposition of ABR signals according to the known frequency content of the ABR, we extracted the peak latency of the fast component of the ABR using fewer averagings of the ABR data. From these decomposition and reconstruction results for ABR signals, we proposed the optimal decomposition level of the ABR and explained how we used the waveform of the ABR reconstructed by the inverse DWT (IDWT). In this paper, we propose a method of automated averaging of the ABR using the waveform reconstructed by discrete wavelet multiresolution analysis (MRA). Our proposed method will be useful for the fast detection of ABR latency characteristics in hearing screening test.

Method And Apparatus For Signal Encoding Evoked Responses

A method and apparatus for utilizing the benefits of encoded signal transmission and reception to enhance the performance of medical testing devices (100) adapted to evoke and measure biological response signals such as auditory evoked potentials (AEP), and the auditory brainstem response (ABR) signals in particular. Auditory stimuli, such as clicks, are presented to the ear of a human patient, in a predetermined encoded sequence, resulting in the generation of auditory responses and bio-electric response signals in the human patient. These response signals from the patient are acquired and observed, and are processed according to the predetermined encoded sequence in which the auditory stimuli were presented to the patient's ear in order to extract the desired auditory evoked potential signals or ABR signals.

Analysis of the variability of auditory brainstem response components through linear regression

The analysis of the Auditory Brainstem Response (ABR) is of fundamental importance to the investigation of the auditory system behavior, though its interpretation has a subjective nature because of the manual process employed in its study and the clinical experience required for its analysis. When analyzing the ABR, clinicians are often interested in the identification of ABR signal components referred to as Jewett waves. In particular, the detection and study of the time when these waves occur (i.e., the wave latency) is a practical tool for the diagnosis of disorders affecting the auditory system. In this context, the aim of this research is to compare ABR manual/visual analysis provided by different examiners. Methods: The ABR data were collected from 10 normal-hearing subjects (5 men and 5 women, from 20 to 52 years). A total of 160 data samples were analyzed and a pair- wise comparison between four distinct examiners was executed. We carried out a statistical study aiming to identify significant differences between assessments provided by the examiners. For this, we used Linear Regression in conjunction with Bootstrap, as a method for evaluating the relation between the responses given by the examiners. Results: The analysis suggests agreement among examiners however reveals differences between assessments of the variability of the waves. We quantified the magnitude of the obtained wave latency differences and 18% of the investigated waves presented substantial differences (large and moderate) and of these 3.79% were considered not acceptable for the clinical practice. Conclusions: Our results characterize the variability of the manual analysis of ABR data and the necessity of establishing unified standards and protocols for the analysis of these data. These results may also contribute to the validation and development of automatic systems that are employed in the early diagnosis of hearing loss.

Assessment of inter-examiner agreement and variability in the manual classification of auditory brainstem response

BackgroundThe analysis of the Auditory Brainstem Response (ABR) is of fundamental importance to the investigation of the auditory system behaviour, though its interpretation has a subjective nature because of the manual process employed in its study and the clinical experience required for its analysis. When analysing the ABR, clinicians are often interested in the identification of ABR signal components referred to as Jewett waves. In particular, the detection and study of the time when these waves occur (i.e., the wave latency) is a practical tool for the diagnosis of disorders affecting the auditory system. Significant differences in inter-examiner results may lead to completely distinct clinical interpretations of the state of the auditory system. In this context, the aim of this research was to evaluate the inter-examiner agreement and variability in the manual classification of ABR.MethodsA total of 160 ABR data samples were collected, for four different stimulus intensity (80dBHL, 60dBHL, 40dBHL and 20dBHL), from 10 normal-hearing subjects (5 men and 5 women, from 20 to 52 years). Four examiners with expertise in the manual classification of ABR components participated in the study. The Bland-Altman statistical method was employed for the assessment of inter-examiner agreement and variability. The mean, standard deviation and error for the bias, which is the difference between examiners’ annotations, were estimated for each pair of examiners. Scatter plots and histograms were employed for data visualization and analysis.ResultsIn most comparisons the differences between examiner’s annotations were below 0.1 ms, which is clinically acceptable. In four cases, it was found a large error and standard deviation (>0.1 ms) that indicate the presence of outliers and thus, discrepancies between examiners.ConclusionsOur results quantify the inter-examiner agreement and variability of the manual analysis of ABR data, and they also allows for the determination of different patterns of manual ABR analysis.

Use of instantaneous energy of ABR signals for fast detection of wave V

Brainstem evoked response audiometry is a powerful diagnostic technique in audiology, otology and neurology. ABR(Auditory Brainstem Response) machine has been very useful and popular in past two decades for detection of hearing defects and pathologies in newborns and children. In ABR, wave V is the most prominent and robust wave that has been used as indicator of hearing loss. However, a fast detection of the wave V is necessary in order to implement newborn hearing screening. Researchers have introduced different kind of signal processing technique in order to achieve this target and one of this is Fast Fourier Transform (FFT) and Wavelet Transform. In this study, the instantaneous energy of ABR signal had been introduced as a marker to identify the ABR waves. Study showed that the instantaneous energy of auditory brainstem response can be used as a marker to identify the ABR waves. This study had proposed a platform for fast hearing screening system.

Wave V Detection Using Instantaneous Energy of Auditory Brainstem Response Signal

Problem statement: ABR machine is a well known machine that has been used on detection of hearing problem especially for babies or children. In ABR, wave V is the most prominent and robust wave that has been used as indicator of hearing loss. However, a fast detection of the wave V is necessary in order to implement newborn hearing screening. There are various types of signal processing methods introduced by researchers in order to achieve the target. Fast Fourier Transform (FFT) and wavelet transform are well known techniques that have been used in digital signal processing. Approach: In this study, the instantaneous energy of ABR signal had been introduced as a marker to identify the ABR waves. Results: Study showed that the instantaneous energy of auditory brainstem response can be used a marker to identify the ABR waves. Conclusion: This study had proposed a platform for fast hearing screening system.

Development of a Chirp Stimulus PC-Based Auditory Brainstem Response Audiometer

Hearing losses during infancy and childhood have many negative future effects and impacts on the child life and productivity. The earlier detection of hearing losses, the earlier medical intervention and then the greater benefit of remediation will be. During this research a PC-based audiometer is designed and, currently, the audiometer prototype is in its final development steps. It is based on the auditory brainstem response (ABR) method. Chirp stimuli instead of traditional click stimuli will be used to invoke the ABR signal. The stimulus is designed to synchronize the hair cells movement when it spreads out over the cochlea. In addition to the available hardware utilization (PC and PCI board), the efforts confined to design and implement a hardware prototype and to develop a software package that enables the system to behave as ABR audiometer. By using such a method and chirp stimulus, it is expected to be able to detect hearing impairment (sensorineural) in the first few days of the life and conduct hearing test at low frequency of stimulus. Currently, the intended chirp stimulus has been successfully generated and the implemented module is able to amplify a signal (on the order of ABR signal) to a recordable level. Moreover, a NI-DAQ data acquisition board has been chosen to implement the PC-prototype interface.

Evoked potential measurement of the masked hearing threshold of a Pacific white-sided dolphin (Lagenorhynchus obliquidens)

The masked hearing threshold of a Pacific white-sided dolphin (Lagenorhynchus obliquidens) was determined by measuring the animal’s auditory brainstem response (ABR). The dolphin was trained to wear surface-contact electrodes embedded in suction cups and to swim into a hoop centered at 1 m below the water surface facing a sound projector 5 m away. Broadband transient signals with center frequencies of 8, 16, 32, 64, 80, and 100 kHz were used as the stimuli. ABR signals were measured by digitizing the electrode signals in 32 point blocks at a sampling rate of 20 kHz. Five hundred blocks were averaged in order to obtain an ABR. The response latency for suprathreshold threshold signals was approximately 1.9 ms with the highest peak-to-peak ABR amplitude of approximately 2.8 uV occurring for a signal frequency of 64 kHz. The spectrum of the ABR signal was similar to that of Tursiops truncatus, with a major peak at 1120 Hz and a secondary peak at 664 Hz. Threshold was determined by progressively reducing the amplitude of the stimulus until an evoked potential could not be detected. The energy signal-to-noise ratio within an integration window at threshold varied between 1 and 8 dB.

Lossy compression of eye movement and auditory brainstem response signals

Eye movement and auditory brainstem response signals recorded for balance and hearing investigations were used as a medical test battery for several types of lossy compression techniques. These signals are associated with the function of the ears. The former signals are used to assess the balance problems (especially vertigo) of a subject and the latter his or her hearing problems. New technique is also presented based on successive approximation quantization. The effect of information loss on medical parameters computed from the signals in the course of compression was evaluated for brainstem response signals. It is important to ensure that lossy compression techniques of these biomedical signals do not impair medical parameter values computed from the signals.

Segmentation of auditory brainstem response signals

Auditory brainstem responses are used to detect hearing defects in audiology and otoncurology. The use of computer programs for the analysis of such recordings is increasing. To identify their detailed properties a pattern recognition algorithm implemented in an analysis program must be highly reliable. For the recognition process, some preprocessing phases after recording are necessary, such as filtering and often also segmentation. In the following, we will explore segmentation, which can be used in preprocessing of biomedical signals after filtering. We studied linear segmentation, where slopes of short signal segments are computed and divided into different classes according to their values. A segment length of 8 samples for a sampling frequency of 50 kHz employed was best according to our tests and error criteria. Using clustering, we found that less than 10 segment classes is suitable for pattern recognition.

Recovery of the auditory brainstem response by sign-bit and conventional averaging.

In evoked potential studies, such as measurement of the auditory brainstem response (ABR), the signal of interest is recovered from the noise background by averaging at least 1000 EEG segments, recorded in phase, with a repeated auditory signal. The possibility is examined that information about whether each sample point was positive or negative relative to a baseline (i.e., averaging the sign bit) would allow recovery of the ABR for clinical purposes. A demonstration is presented in which auditory brainstem responses based on the full 12 bits of a typical analog-to-digital converter (TA or true amplitude procedure) are compared to those based on a single bit (SB procedure). For the signal-to-noise (S/N) ratios associated with an ABR signal in EEG noise (i.e., in the range 0.01>or=S/N>or=0.5), the recovery of the ABR signal using the SB procedure is quite satisfactory. Since the SB procedure is much less demanding computationally than the TA procedure, and thus faster and less expensive, it could be useful for practical ABR recovery. >

Auditory brainstem response signal transduction through a Telephonics TDH-39 earphone

Waveforms and power spectra of acoustic signals used to elicit the auditory brainstem response were analyzed with respect to electrical and acoustical differences. Signals were of alternating polarity and included a dc pulse, tone bursts of 0.5 and 4.0 kHz, a 4.0-kHz tone pip, and a 2.0-kHz logon. The earphone faithfully transduced the temporal dimension of the 5.0-ms bursts and pips while the logon (450 μs) and the click (100 μs) durations were on the order of ten times greater than the electrical input. Earphone distortion included the loss of well-defined energy peaks and a spread of energy to higher frequencies. Signal polarity was not maintained through the earphone.