Biomedical subjects

Daniel J Strauss

Publications and source records attributed to Daniel J Strauss.

4 recordsLinked to original sources

PubMed2004

Analysis and detection of binaural interaction in auditory evoked brainstem responses by time-scale representations.

The beta-wave of the binaural interaction component (BIC) in auditory evoked brainstem responses has been shown to be an objective measure of binaural interaction. However, a reliable and automated detection of this component capable of clinical use still remains a challenge. In this study, wavelet based time-scale representations of auditory evoked brainstem responses were investigated for the analysis of binaural interaction and for an automated detection of the beta-wave. Twenty normal hearing subjects with verified normal directional hearing and speech intelligibility in noise were included in our study. In all of these subjects, the BICs exhibited a characteristic concentration of energy in the time-scale domain which allowed for an automated detection of the beta-wave. Moreover, our study provides an explanation why the beta-wave is hard to detect for larger interaural time delays using time-scale entropy based arguments. It is concluded that time-scale representations of auditory brainstem responses are well suited for the analysis of binaural interaction and allow for an automated detection of the beta-wave.

Acoustic Stimulation↗

PubMed2004

A time-frequency feature extraction scheme for the automated detection of binaural interaction in auditory brainstem responses.

The binaural interaction component (BIC), the difference between the summed monaurally evoked potentials of each ear and the binaurally evoked brainstem potentials, has been shown to be related to directional hearing. However, the detection of the beta-peak as the most consistent part of the BIC is often difficult. Furthermore, there is no clearly defined signal feature characterizing the difference between the monaurally and the binaurally evoked brainstem responses. A closer look at the signals shows that amplitude differences as well as latency differences and variations in wave V slopes could be the reason for the formation of a beta-peak. Using a time-scale feature extraction scheme, we were able to define a signal feature (morphological local discriminant bases (MLDB) coefficient 1) that accounts for the difference between the sum of the monaurally and binaurally evoked brainstem potentials. With use of this signal feature, reliable automated detection of differences between monaurally and binaurally evoked potentials is possible. As coefficient 1 replicates the behaviour of subjective measurements as well as of the BIC measurements, it can also be seen as a correlate of binaural interaction. With use of this signal feature, it is possible to judge from a given binaurally evoked potential whether it contains information on binaural interaction or not, without comparing it to the sum of the monaurally evoked brainstem responses Consequently, binaural interaction can be assessed by one, instead of three, measurements by using the method described in this paper.

Acoustic Stimulation↗

PubMed2004

Objective detection of the central auditory processing disorder: a new machine learning approach.

The objective detection of binaural interaction is of diagnostic interest for the evaluation of the central auditory processing disorder (CAPD). The beta-wave of the binaural interaction component in auditory brainstem responses has been suggested as an objective measure of binaural interaction and has been shown to be of diagnostic value in the CAPD diagnosis. However, a reliable and automated detection of the beta-wave capable of clinical use still remains a challenge. We propose a new machine learning approach to the detection of the CAPD that is based on adapted tight frame decompositions which are tailored for support vector machines with radial kernels. Using shift-invariant scale and morphological features of the binaurally evoked brainstem potentials, our approach provides at least comparable results to the beta-wave detection in view of the discrimination of subjects being at risk for CAPD and subjects being not at risk for CAPD. Furthermore, as no information from the monaurally evoked potentials is necessary, the measurement cost is reduced by two-thirds compared to the computation of the binaural interaction component. We conclude that a machine learning approach in the form of a hybrid tight frame-support vector classification is effective in the objective detection of the CAPD.

Artificial Intelligence↗

PubMed2003

The binaural interaction component (BIC) in children with central auditory processing disorders (CAPD).

The detection of binaural interaction is of diagnostic interest in patients with central auditory processing disorders (CAPDs), as binaural hearing tasks are frequently affected in these patients. Owing to the comorbidity associated with disorders such as an attention-deficit hyperactivity disorder, pathological results in subjective tests often show extra-auditory factors such as reduced attention rather than impaired central auditory function. Therefore, objective measures for auditory processing disorders are essential. The binaural interaction component (BIC), which is the arithmetical difference between the sum of the monaurally evoked auditory potentials of each ear and the binaurally evoked brainstem potentials, has been used as an objective measure of binaural interaction in humans. BIC measurements can therefore be considered as a possible diagnostic tool in CAPD patients. One aim of the present study was to examine whether and to what extent BIC measurements are capable of differentiating between normal children and children 'at risk for CAPD'. BIC measurements were performed on 17 children at risk for CAPD and in a group of 25 children with normal results in the central audiometric tests used. Using the presence or absence of clearly demonstrable BIC waveforms as an indication of whether a CAPD is present or not, a sensitivity and specificity of 76% could be achieved. We conclude that BIC measurements might be of some diagnostic value in CAPD patients.

Child↗