Lateralized Readiness Potentials Recorded with Near-Threshold Auditory Stimuli in Subjects Simulating Hearing Loss

Introduction: Preparatory motor cortical responses like the lateralized readiness potential (LRP) may be useful in revealing persistent attempts to feign hearing loss. Previous studies suggest only a marginal effect of stimulus intensity on the amplitude of the LRP. However, this has not been investigated using low-intensity auditory stimuli to cue NoGo trials. We address this in an experiment where subjects were instructed not to give a manual response to low-instensity stimuli, a situation that is akin to simulating hearing loss. Methods: The LRP was recorded from normal hearing listeners (N = 10) with 500 and 4,000-Hz pure tones and trains of 4,000 Hz (2-1-2) tonebursts. Electrophysiologic data underwent processing to (i) analyze the effect of the stimulus type on the LRP, (ii) classify results according to manual response with both logistic regression and linear support vector machine (SVM) models, and (iii) derive auditory brainstem responses (ABRs) from the tonebursts. Results: The amplitude of the LRP did not differ between the 3 stimuli used to elicit the response. Single-trial electrode data from Go and NoGo trials were submitted to supervised binary classification, and the logistic regression model gave a mean accuracy of close to 0.7. The Jewett wave V latencies of the resultant ABRs from some subjects were found to increase between the high (Go) and low (NoGo) intensity tonebursts. Conclusion: This study shows that auditory stimulus type does not affect the amplitude of the LRP and that the response can be recorded with stimuli that are near the auditory threshold. It can also be recorded with transient stimuli, and this allows for the possibility of simultaneously recording other confirmatory measurements, like ABR.

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Effects of Presbycusis and Other Types of Hearing Loss on Auditory Brainstem Responses

International Journal of Audiology ◽

10.3109/14992028609042141 ◽

1986 ◽

Vol 15 (4) ◽

pp. 179-185

Author(s):

Ulf Rosenhall ◽

Kai Pedersen ◽

Mats Dotevall

Keyword(s):

Hearing Loss ◽

Auditory Brainstem ◽

Auditory Brainstem Responses

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Hearing recovery from deafness caused by bromate intoxication

The Journal of Laryngology & Otology ◽

10.1017/s0022215118002001 ◽

2018 ◽

Vol 132 (11) ◽

pp. 1039-1041 ◽

Cited By ~ 1

Author(s):

J Suzuki ◽

Y Takanashi ◽

A Koyama ◽

Y Katori

Keyword(s):

Hearing Loss ◽

Sensorineural Hearing Loss ◽

Pure Tone ◽

Pure Tone Audiometry ◽

Auditory Brainstem ◽

Sensorineural Hearing ◽

Auditory Brainstem Responses ◽

Common Symptom ◽

Hearing Recovery ◽

Continuous Haemodiafiltration

AbstractObjectivesSodium bromate is a strong oxidant, and bromate intoxication can cause irreversible severe-to-profound sensorineural hearing loss. This paper reports the first case in the English literature of bromate-induced hearing loss with hearing recovery measured by formal audiological assessment.Case reportA 72-year-old woman was admitted to hospital with complaints of profound hearing loss, nausea, diarrhoea and anuria after bromate ingestion in a suicide attempt. On admission, pure tone audiometry and auditory brainstem responses showed profound bilateral deafness. Under the diagnosis of bromate-induced acute renal failure and sensorineural hearing loss, continuous haemodiafiltration was performed. When dialysis was discontinued, pure tone audiometry and auditory brainstem responses showed partial threshold recovery from profound deafness.ConclusionSevere-to-profound sensorineural hearing loss is a common symptom of bromate intoxication. Bromate-induced hearing loss may be partially treated, and early application of continuous haemodiafiltration might be useful as a treatment for this intractable condition.

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Auditory Brainstem Responses in a Case of High-Frequency Conductive Hearing Loss

Journal of Speech and Hearing Disorders ◽

10.1044/jshd.5004.346 ◽

1985 ◽

Vol 50 (4) ◽

pp. 346-350 ◽

Cited By ~ 8

Author(s):

Michael P. Gorga ◽

Jan K. Reiland ◽

Kathryn A. Beauchaine

Keyword(s):

Hearing Loss ◽

High Frequency ◽

Conductive Hearing Loss ◽

Auditory Brainstem ◽

Intensity Function ◽

Auditory Brainstem Responses ◽

Normal Limits ◽

Interpeak Latency ◽

Lower Limits ◽

Conductive Hearing

Click-evoked auditory brainstem responses were measured in a patient with high-frequency conductive hearing loss. As is typical in cases of conductive hearing loss, Wave I latency was prolonged beyond normal limits. Interpeak latency differences were just below the lower limits of the normal range. The Wave V latency-intensity function, however was abnormally steep. This pattern is explained by the hypothesis that the slope of the latency-intensity function is determined principally by the configuration of the hearing loss. In cases of high-frequency hearing loss (regardless of the etiology), the response may be dominated by more apical regions of the cochlea at lower intensities and thus have a longer latency.

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Bankruptcy Prediction of Engineering Companies in the EU Using Classification Methods

Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis ◽

10.11118/actaun201866051347 ◽

2018 ◽

Vol 66 (5) ◽

pp. 1347-1356 ◽

Cited By ~ 1

Author(s):

Michaela Staňková ◽

David Hampel

Keyword(s):

Logistic Regression ◽

Support Vector Machines ◽

Binary Classification ◽

Classification Tree ◽

Classification Trees ◽

Bankruptcy Prediction ◽

Support Vector ◽

Type I ◽

Vector Machines ◽

The Eu

This article focuses on the problem of binary classification of 902 small- and medium‑sized engineering companies active in the EU, together with additional 51 companies which went bankrupt in 2014. For classification purposes, the basic statistical method of logistic regression has been selected, together with a representative of machine learning (support vector machines and classification trees method) to construct models for bankruptcy prediction. Different settings have been tested for each method. Furthermore, the models were estimated based on complete data and also using identified artificial factors. To evaluate the quality of prediction we observe not only the total accuracy with the type I and II errors but also the area under ROC curve criterion. The results clearly show that increasing distance to bankruptcy decreases the predictive ability of all models. The classification tree method leads us to rather simple models. The best classification results were achieved through logistic regression based on artificial factors. Moreover, this procedure provides good and stable results regardless of other settings. Artificial factors also seem to be a suitable variable for support vector machines models, but classification trees achieved better results using original data.

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