An Automatic Recognition for the Auditory Brainstem Response Waveform

Uragun, Balemir

doi:10.1109/icmla.2015.92

Cited by 3 publications

(7 citation statements)

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“…In this manuscript, the result from the Neural Network trained clustered data were used as a master-template, instead of averaging clustered data-points as were in [18] with reasons detailed in the [19]. The first step is to find the suitable curve fitting functions if it is appropriate for the master-template then second step is to assess the first step's result for filtering in some of the curve fitting functions, namely 1 st and 2 nd order Gaussian functions.…”

Section: Methodsmentioning

confidence: 99%

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The Mathematically Describable ILD Patterns

Uragun¹

2018

IJBBB

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This is a mathematical modelling study in computational science that recognized patterns used based on the biological data. The biological data were obtained through the number of clusters and the shape of each clustered means Interaural Level Differences (ILD) and were both analytically examined by the data classification study. Then the result of the classification study trained by the Artificial Neural Networks to build a master-template for a single array. Here, these typified single array patterns were exclusively tested for several curve fitted functions and the outcome was the probability density functions "pdf" with the linear regression parameters. This initial evaluation confirms two Gaussian functions were both suitable models for the data sets, and then coefficients of these functions verified for a correlation to be validated. In conclusion, a parameterized first-order Gaussian function can be used as the mathematical model for all ILD patterns; the energy efficiency is also discussed.

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Section: Methodsmentioning

confidence: 99%

“…This approach was also applied to the parameters of the pdf models. This curve fitting technique was previously proposed in the use of automatic pattern recognition for the Auditory Brainstem Response (ABR) waveform, [18]. The number of curve fitted functions examined to select the suitable function to be modeled for ABR.…”

Section: Using Curve Fitting Techniquesmentioning

confidence: 99%

The Mathematically Describable ILD Patterns

Uragun¹

2018

IJBBB

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“…It can observe functional status of auditory nerve and lower auditory center, and reflect conduction ability of brainstem auditory pathway [1,2]. Given that patient's hearing impairment can be diagnosed without his active cooperation, ABR has become one of the routine methods for newborn hearing screening and adult hearing disease diagnosis [3,4,5]. ABR waveform usually has a short eclipse period of 10ms, and electrode intensity in microvolts is recorded.…”

Section: Introductionmentioning

confidence: 99%

Automatic Recognition of Auditory Brainstem Response Characteristic Waveform based on BiLSTM

Chen

Zhan

Pan

et al. 2020

Preprint

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BackgroundAuditory brainstem response (ABR) test is widely used in newborn hearing screening and hearing disease diagnosis. Identifying and marking are challenging and repetitive tasks because of complex rules of ABR characteristic waveform and interference of background noise.MethodsThis study proposes an automatic method to recognize ABR characteristic waveform. First, binarization is created to mark 1024 sampling points accordingly. The selected characteristic area of ABR data is 0-8ms. The marking area is enlarged to expand feature information and reduce marking error. Second, a bi-directional long short-term memory (BiLSTM) network structure is established to improve relevance of sampling points, and an ABR sampling point classifier is obtained by training. Finally, mark points are obtained through thresholding.ResultsSpecific structure, related parameters, recognition effect, and noise resistance of network were explored in 614 sets of ABR clinical data, and recognition accuracy of waves I, III, and V can reach 92.91%.DiscussionThus, the proposed method can reduce the repetitive work of doctors and meet accuracy effectively. Therefore, this method has clinical potential.

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“…This group of methods tries to estimate the potential being analyzed successively by referring to the past scopes information. By actualizing this procedure, the undesirable signal may be filtered out from each sweep of recorded ABR [8,12]. Although some types of filters may improve the signal-to-noise ratios, they decrease in response amplitude in parallel with distortion of the ABR waveform at high-pass settings over 65 Hz single-trial covariance analysis [12,13].…”

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confidence: 99%

“…By actualizing this procedure, the undesirable signal may be filtered out from each sweep of recorded ABR [8,12]. Although some types of filters may improve the signal-to-noise ratios, they decrease in response amplitude in parallel with distortion of the ABR waveform at high-pass settings over 65 Hz single-trial covariance analysis [12,13]. Automatic peak-picking is another method that has been presented so far [12,14,15].…”

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confidence: 99%

A Novel Method for Automated Estimation of Effective Parameters of Complex Auditory Brainstem Response: Adaptive Processing Based on the Correntropy Concept

Shojaedini

Jafarpisheh²,

Rouhbakhsh³

et al. 2022

IRJ

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Objectives: Automated Auditory Brainstem Responses (ABR) peak detection is a novel technique to facilitate the measurement of neural synchrony along the auditory pathway through the brainstem. Analyzing the location of the peaks in these signals and the time interval between them may be utilized either for analyzing the hearing process or detecting peripheral and central lesions in the human hearing system. Methods: In this paper, model-based signal processing is proposed to estimate the effective parameters of ABR signals. In this process, the biological parameters of the signal are assessed by utilizing a Finite Impulse Response (FIR) adaptive filter in which its adaptation procedure is performed based on the correntropy concept. The proposed method is applied on a set of ABR signals recorded in response to three stimuli of /da/, /ba/, and /ga/, and then its performances are compared with an existing state-of-the-art technique. Results: The results show that the proposed method can significantly increase the accuracy of estimating the parameters in stable stimulations (/da/, /ba/) for major positive and negative peaks. This improvement is more significant (up to 2-3 times) for /ba/ stimulus and especially in major positive peaks. However, in other peaks, the improvements also occurred in smaller amounts. However, for unstable stimuli (/ga/), no significant improvement was achieved. Discussion: Increasing the accuracy performance of the proposed method for detecting the stable stimuli (while its performance remains unchanged) for detecting unstable stimuli indicates its effectiveness in automated clinical analysis of ABR signals.

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An Automatic Recognition for the Auditory Brainstem Response Waveform

Cited by 3 publications

References 72 publications

The Mathematically Describable ILD Patterns

The Mathematically Describable ILD Patterns

Automatic Recognition of Auditory Brainstem Response Characteristic Waveform based on BiLSTM

A Novel Method for Automated Estimation of Effective Parameters of Complex Auditory Brainstem Response: Adaptive Processing Based on the Correntropy Concept

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