No abstract

BackgroundThe clinically used methods of pain diagnosis do not allow for objective and robust measurement, and physicians must rely on the patient’s report on the pain sensation. Verbal scales, visual analog scales (VAS) or numeric rating scales (NRS) count among the most common tools, which are restricted to patients with normal mental abilities. There also exist instruments for pain assessment in people with verbal and / or cognitive impairments and instruments for pain assessment in people who are sedated and automated ventilated. However, all these diagnostic methods either have limited reliability and validity or are very time-consuming. In contrast, biopotentials can be automatically analyzed with machine learning algorithms to provide a surrogate measure of pain intensity.MethodsIn this context, we created a database of biopotentials to advance an automated pain recognition system, determine its theoretical testing quality, and optimize its performance. Eighty-five participants were subjected to painful heat stimuli (baseline, pain threshold, two intermediate thresholds, and pain tolerance threshold) under controlled conditions and the signals of electromyography, skin conductance level, and electrocardiography were collected. A total of 159 features were extracted from the mathematical groupings of amplitude, frequency, stationarity, entropy, linearity, variability, and similarity.ResultsWe achieved classification rates of 90.94% for baseline vs. pain tolerance threshold and 79.29% for baseline vs. pain threshold. The most selected pain features stemmed from the amplitude and similarity group and were derived from facial electromyography.ConclusionThe machine learning measurement of pain in patients could provide valuable information for a clinical team and thus support the treatment assessment.

No abstract

No abstract

Altered auditory feedback effectively inhibits stuttering immediately after speech has been initiated. However, unlike a true choral signal, which is exogenously initiated and offers the most complete fluency enhancement, AAF requires speech to be initiated by the user and 'fed back' before it can directly inhibit stuttering. It is suggested that AAF can be a viable clinical option for those who stutter and should often be used in combination with therapeutic techniques, particularly those that aid speech initiation. The substantially higher rate of stuttering occurring on initiation supports a hypothesis that overt stuttering events help 'release' and 'inhibit' central stuttering blocks. This perspective is examined in the context of internal models and mirror neurons.