Daniel J. Strauss

Objective Quantification of the Tinnitus Decompensation by Synchronization Measures of Auditory Evoked Single Sweeps

Large-scale neural correlates of the tinnitus decompensation might be used for an objective evaluation of therapies and neurofeedback based therapeutic approaches. In this study, we try to identify large-scale neural correlates of the tinnitus decompensation using wavelet phase stability criteria of single sweep sequences of late auditory evoked potentials as synchronization stability measure. The extracted measure provided an objective quantification of the tinnitus decompensation and allowed for a reliable discrimination between a group of compensated and decompensated tinnitus patients. We provide an interpretation for our results by a neural model of top-down projections based on the Jastreboff tinnitus model combined with the adaptive resonance theory which has not been applied to model tinnitus so far. Using this model, our stability measure of evoked potentials can be linked to the focus of attention on the tinnitus signal. It is concluded that the wavelet phase stability of late auditory evoked potential single sweeps might be used as objective tinnitus decompensation measure and can be interpreted in the framework of the Jastreboff tinnitus model and adaptive resonance theory.

Hybrid wavelet-support vector classification of waveforms

The support vector machine (SVM) represents a new and very promising technique for machine learning tasks involving classification, regression or novelty detection. Improvements of its generalization ability can be achieved by incorporating prior knowledge of the task at hand.We propose a new hybrid algorithm consisting of signal-adapted wavelet decompositions and hard margin SVMs for waveform classification. The adaptation of the wavelet decompositions is tailored for hard margin SV classifiers with radial basis functions as kernels. It allows the optimization of the representation of the data before training the SVM and does not suffer from computationally expensive validation techniques.We assess the performance of our algorithm against the background of current concerns in medical diagnostics, namely the classification of endocardial electrograms and the detection of otoacoustic emissions. Here the performance of hard margin SVMs can significantly be improved by our adapted preprocessing step.

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 bramstem potentials, has been used as an objective measure of binaural interaction in humans. BIC measurements can therefore be considered its 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.

Discrimination of Sinus Rhythm, Atrial Flutter, and Atrial Fibrillation Using Bipolar Endocardial Signals

Discrimination of NSR, AFL, and AF. Introduction: Analysis of endocardial signals obtained from an electrode located in the right atrium as realized in newly designed dual chamber, implantable cardioverter defibrillators might be used to provide additional therapeutic options, such as overdrive pacing or low‐energy atrial cardioversion for the treatment of concomitant atrial flutter (AFL) or atrial fibrillation (AF). Therefore, we developed a computer algorithm for discrimination of normal sinus rhythm (NSR), AFL, and AF that may lead to adequate differential therapy of atrial tachyarrhythmias in an automated mode.

Extraction of Auditory Attention Correlates in Single Sweeps of Cortical Potentials by Maximum Entropy Paradigms and its Application

Recently, we have shown that the wavelet phase synchronization stability of single sweeps of auditory late responses (ALRs) allows for the quantification of the tinnitus decompensation. Our underlying model of adaptive resonance and spotlighting of attention links the synchronization stability directly to neural correlates of attention reflected in ALRs. Correlates of this attentional mechanism are further investigated in this study by using an auditory paradigm based on maximum entropy principle in healthy subjects. In particular, we show that the wavelet phase synchronization of ALR single sweeps allows for a direct online monitoring of phase locked auditory attention. Such an online monitoring cannot be implemented by known procedures as they are based on large-scale averages of ALRs. Apart from the objective quantification of the tinnitus decompensation, this measure can be used in every online and real time neurofeedback therapeutic approach where a direct stimulus locked attention monitoring is mandatory

Neural Synchronization Stability in the Tinnitus Decompensation

Neural correlates of psychophysiological tinnitus models in humans may be used for their neurophysiological validation as well as for their refinement and improvement to better understand the pathogenesis of the tinnitus decompensation and to develop new therapeutic approaches. In this study, we try to identify neural correlates of cortico-cortical top-down projections based on the Jastreboff tinnitus model combined with the adaptive resonance theory of Grossberg which has not been applied to the problem of tinnitus so far. In particular, the neural synchronization stability. In single sweep sequences of late auditory evoked potentials is evaluated by the wavelet phase coherence in the time-scale domain. The synchronization stability, which is according to the underlying model linked to the focus of attention on the tinnitus signal, discriminated between a group of compensated and decompensated tinnitus patients. It is concluded that top-down processes according to the adaptive resonance theory basically represent the influences of the limbic and autonomic nervous system in the Jastreboff tinnitus model. The neural correlates of these processes are globally reflected in the synchronization stability of late auditory evoked potentials and may be useful as objective tinnitus decompensation measure

Electrophysiological correlates of listening effort: neurodynamical modeling and measurement.

An increased listing effort represents a major problem in humans with hearing impairment. Neurodiagnostic methods for an objective listening effort estimation might support hearing instrument fitting procedures. However the cognitive neurodynamics of listening effort is far from being understood and its neural correlates have not been identified yet. In this paper we analyze the cognitive neurodynamics of listening effort by using methods of forward neurophysical modeling and time-scale electroencephalographic neurodiagnostics. In particular, we present a forward neurophysical model for auditory late responses (ALRs) as large-scale listening effort correlates. Here endogenously driven top–down projections related to listening effort are mapped to corticothalamic feedback pathways which were analyzed for the selective attention neurodynamics before. We show that this model represents well the time-scale phase stability analysis of experimental electroencephalographic data from auditory discrimination paradigms. It is concluded that the proposed neurophysical and neuropsychological framework is appropriate for the analysis of listening effort and might help to develop objective electroencephalographic methods for its estimation in future.

Alterations in Event Related Potentials (ERP) associated with tinnitus distress and attention.

Tinnitus related distress corresponds to different degrees of attention paid to the tinnitus. Shifting attention to a signal other than the tinnitus is therefore particularly difficult for patients with high tinnitus related distress. As attention effects on Event Related Potentials (ERP) have been shown this should be reflected in ERP measurements (N100, phase locking). In order to prove this hypothesis single sweep ERP recordings were obtained in 41 tinnitus patients as well as 10 control subjects during a period of time when attention was shifted to a tone (attended) and during a second phase (unattended) when they did not focus attention to the tone. Whereas tinnitus patients with low distress showed a significant reduction in both N100 amplitude and phase locking when comparing the attended and unattended measurement condition a group of patients with high tinnitus related distress did not show such ERP alterations. Using single sweep ERP measurements the results of our study show, that attention in high tinnitus related distress patients is captured by their tinnitus significantly more than in low distress patients. Furthermore our results provide the basis for future neurofeedback based tinnitus therapies aiming at maximizing the ability to shift attention away from the tinnitus.

Feature extraction by shape-adapted local discriminant bases

In recent years, wavelet packets have proven their capabilities for dimensionality reduction in waveform recognition. A well-accepted scheme is the local discriminant bases (LDB) algorithm which relies on the best-basis paradigm. In this paper, we combine the LDB algorithm with signal-adapted filter banks based on the lattice structure to construct more powerful LDBs. Here, additionally to the conventional tree adjustment, we adapt the shape of the analyzing atoms to extract discriminatory information among signal classes. We apply our shape-adapted LDBs, which we also call morphological LDBs, for current tasks of biosignal processing, namely feature extraction in waveforms from audiology and electrocardiology. Against the background of these applications, we show that our morphological LDBs outperform LDBs based on a fixed dictionary. We also present results which seem to open new research perspectives in audiology.

Fast detection of wave V in ABRs using a smart single sweep analysis system

The analysis of auditory brainstem responses (ABRs) is accepted to be the most reliable method for the objective diagnosis and quantification of hearing loss in newborns. However, in currently available setups, a large number of sweeps has to be averaged to obtain a meaningful signal at low stimulation levels due to a poor signal-to-noise ratio. In this study, we present a new approach to the detection of wave V in ABRs using a smart single sweep analysis system. A small number of sweeps is decomposed by optimized tight frames and evaluated by a kernel based novelty detection machine. This hybrid supervised learning scheme is combined with an intersweep dissimilarity tracing for the final decision making. At the challenging stimulation level of 30 dB, our system reached a reasonable specificity and sensitivity for the detection of wave V in a fraction of the measurement time of conventional schemes.