Fast extraction method of auditory brainstem response based on wavelet transformation

Sun, Yonghe; Chen, Zhaoxue

doi:10.1109/icwapr.2007.4421758

Cited by 3 publications

(1 citation statement)

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“…In the traditional mode, wavelet transform is a commonly used method in ABR extraction and recognition research (15). In ABR extraction, wavelet transform can achieve the effect of eliminating noise by selecting the detailed components of specific frequencies for reconstruction and to make the ABR waveform smoother.…”

Section: Wavelet Transformmentioning

confidence: 99%

Automatic Recognition of Auditory Brainstem Response Characteristic Waveform Based on Bidirectional Long Short-Term Memory

et al. 2021

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Background: Auditory brainstem response (ABR) testing is an invasive electrophysiological auditory function test. Its waveforms and threshold can reflect auditory functional changes in the auditory centers in the brainstem and are widely used in the clinic to diagnose dysfunction in hearing. However, identifying its waveforms and threshold is mainly dependent on manual recognition by experimental persons, which could be primarily influenced by individual experiences. This is also a heavy job in clinical practice.Methods: In this work, human ABR was recorded. First, binarization is created to mark 1,024 sampling points accordingly. The selected characteristic area of ABR data is 0–8 ms. The marking area is enlarged to expand feature information and reduce marking error. Second, a bidirectional 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.Results: The specific structure, related parameters, recognition effect, and noise resistance of the network were explored in 614 sets of ABR clinical data. The results show that the average detection time for each data was 0.05 s, and recognition accuracy reached 92.91%.Discussion: The study proposed an automatic recognition of ABR waveforms by using the BiLSTM-based machine learning technique. The results demonstrated that the proposed methods could reduce recording time and help doctors in making diagnosis, suggesting that the proposed method has the potential to be used in the clinic in the future.

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Section: Wavelet Transformmentioning

confidence: 99%

Automatic Recognition of Auditory Brainstem Response Characteristic Waveform Based on Bidirectional Long Short-Term Memory

et al. 2021

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Automatically labeling of Brainstem Auditory Evoked Potential based on vector algorithm

Sun

Chen

2011

2011 4th International Congress on Image and Signal Processing

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Further Research about Fast Extraction Method of Auditory Brainstem Response

Gui

Sun

Gao

et al. 2011

AMR

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There are the similarities and the differences between wavelet transformation and traditional superposing and averaging method are discussed in de-noising ABR signal. Actually, it is easy to see that the advantage of wavelet transform, but it’s unbelievable to other people. In this paper, by spectral analysis, a quantity measurement estimate is given. So we can conclude that wavelet transform has better result than traditional superposing and averaging for noise removal.

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Fast extraction method of auditory brainstem response based on wavelet transformation

Cited by 3 publications

References 4 publications

Automatic Recognition of Auditory Brainstem Response Characteristic Waveform Based on Bidirectional Long Short-Term Memory

Automatic Recognition of Auditory Brainstem Response Characteristic Waveform Based on Bidirectional Long Short-Term Memory

Automatically labeling of Brainstem Auditory Evoked Potential based on vector algorithm

Further Research about Fast Extraction Method of Auditory Brainstem Response

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