Determining hearing threshold from brain stem evoked potentials. Optimizing a neural network to improve classification performance

Alpsan, D.; Towsey, Michael; Özdamar, Özcan; Tsoi, Ah Chung; Ghista, Dhanjoo N.

doi:10.1109/51.310986

Cited by 17 publications

(7 citation statements)

References 7 publications

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“…In addition, this condition can be further relaxed if the second derivative is utilised allowing peaks to be found as points of infl ection. The proposed algorithm is also conceptually simple and can be directly and easily related to current practice, unlike some of the previous algorithms that, for example, utilise ANN approaches that provide only a 'black-box' solution [Alpsan et al, 1994;Popescu et al, 1999;Tian et al, 1997]. More structured, feature-based approaches, such as the one used in this study, have also been shown to offer improved performance when compared to ANN approaches that often directly accept the ABR waveform as the input vector [Sanchez et al, 1995].…”

Section: Algorithm Performancementioning

confidence: 95%

“…Methods previously used for analysis of ABR peaks in suprathreshold waveforms have included: detecting the local maxima in low-pass fi ltered waveforms [Pratt et al, 1989], designing fi lters matched to the peaks of interest [Vannier et al, 2001;Vannier et al, 2002;Woodworth et al, 1983] and applying artifi cial neural networks (ANNs) [Alpsan et al, 1994;Popescu et al, 1999;Tian et al, 1997]. However, the most commonly used method has been to band-limit the ABR waveform and then to estimate its gradient (fi rst derivative).…”

Section: Introductionmentioning

confidence: 99%

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Automated Analysis of the Auditory Brainstem Response Using Derivative Estimation Wavelets

Bradley¹,

Wilson

2004

Audiol Neurotol

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In this paper, we describe an algorithm that automatically detects and labels peaks I–VII of the normal, suprathreshold auditory brainstem response (ABR). The algorithm proceeds in three stages, with the option of a fourth: (1) all candidate peaks and troughs in the ABR waveform are identified using zero crossings of the first derivative, (2) peaks I–VII are identified from these candidate peaks based on their latency and morphology, (3) if required, peaks II and IV are identified as points of inflection using zero crossings of the second derivative and (4) interpeak troughs are identified before peak latencies and amplitudes are measured. The performance of the algorithm was estimated on a set of 240 normal ABR waveforms recorded using a stimulus intensity of 90 dBnHL. When compared to an expert audiologist, the algorithm correctly identified the major ABR peaks (I, III and V) in 96–98% of the waveforms and the minor ABR peaks (II, IV, VI and VII) in 45–83% of waveforms. Whilst peak II was correctly identified in only 83% and peak IV in 77% of waveforms, it was shown that 5% of the peak II identifications and 31% of the peak IV identifications came as a direct result of allowing these peaks to be found as points of inflection.

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Section: Algorithm Performancementioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Automated Analysis of the Auditory Brainstem Response Using Derivative Estimation Wavelets

Bradley¹,

Wilson

2004

Audiol Neurotol

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“…Several studies have been reported pertaining to the analysis of evoked potential (EP) in the scalp EEG using NN [108][109][110]. Some of them are concerned about visual EPs [109][110][111][112][113][114][115][116][117][118][119][120][121][122], some about auditory EPs [100,110,118,[123][124][125][126][127][128][129][130][131][132][133][134] and some about somatosensory EPs [110,[135][136][137][138][139]. For a fundamental treatment of use of NN in the analysis of event related potential (ERP) see Ref.…”

Section: Other Patternsmentioning

confidence: 99%

Human scalp EEG processing: Various soft computing approaches

Majumdar

2011

Applied Soft Computing

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“…Accordingly, since early on in ABR application, there have been attempts to automate and develop objective methods to determine hearing thresholds from ABR measurements. Over the years, ABR has been discussed in literature as Auditory Evoked Potentials (AEP) [ 12 ], Cortical Auditory Evoked Potentials (CAEP) [ 13 ], Brainstem auditory evoked potential (BAEP) [ 14 , 15 ], Brainstem Evoked Response Audiometry (BERA) [ 16 ], and Auditory Evoked Potential (EAP) [ 17 ] . Many approaches applied and combined methods from different fields of statistics [ 9 , 13 , 17 – 30 ], often involving feature extraction from the time and/or the frequency domain.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Objective hearing threshold identification from auditory brainstem response measurements using supervised and self-supervised approaches

et al. 2022

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Hearing loss is a major health problem and psychological burden in humans. Mouse models offer a possibility to elucidate genes involved in the underlying developmental and pathophysiological mechanisms of hearing impairment. To this end, large-scale mouse phenotyping programs include auditory phenotyping of single-gene knockout mouse lines. Using the auditory brainstem response (ABR) procedure, the German Mouse Clinic and similar facilities worldwide have produced large, uniform data sets of averaged ABR raw data of mutant and wildtype mice. In the course of standard ABR analysis, hearing thresholds are assessed visually by trained staff from series of signal curves of increasing sound pressure level. This is time-consuming and prone to be biased by the reader as well as the graphical display quality and scale.In an attempt to reduce workload and improve quality and reproducibility, we developed and compared two methods for automated hearing threshold identification from averaged ABR raw data: a supervised approach involving two combined neural networks trained on human-generated labels and a self-supervised approach, which exploits the signal power spectrum and combines random forest sound level estimation with a piece-wise curve fitting algorithm for threshold finding.We show that both models work well and are suitable for fast, reliable, and unbiased hearing threshold detection and quality control. In a high-throughput mouse phenotyping environment, both methods perform well as part of an automated end-to-end screening pipeline to detect candidate genes for hearing involvement. Code for both models as well as data used for this work are freely available.

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Determining hearing threshold from brain stem evoked potentials. Optimizing a neural network to improve classification performance

Cited by 17 publications

References 7 publications

Automated Analysis of the Auditory Brainstem Response Using Derivative Estimation Wavelets

Automated Analysis of the Auditory Brainstem Response Using Derivative Estimation Wavelets

Human scalp EEG processing: Various soft computing approaches

Objective hearing threshold identification from auditory brainstem response measurements using supervised and self-supervised approaches

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