Efficacy of speech pathology management for chronic cough: a randomised placebo controlled trial of treatment efficacy

Background: Chronic cough that persists despite medical treatment may respond to speech pathology intervention, but the efficacy of such treatment has not been investigated in prospective randomised trials. The aim of this study was to determine the efficacy of a speech pathology intervention programme for chronic cough. Methods: A single blind, randomised, placebo controlled trial was conducted in 87 patients with chronic cough that persisted despite medical treatment. Patients were randomly allocated to receive either a specifically designed speech pathology intervention or a placebo intervention. Participants in both groups attended four intervention sessions with a qualified speech pathologist. Results: Participants in the treatment group had a significant reduction in cough (8.9 to 4.6, p, 0.001), breathing (7.9 to 4.7, p < 0.001), voice (7.3 to 4.6, p < 0.001) upper airway (8.9 to 5.9, p < 0.001) symptom scores and limitation (2.3 to 1.6, p < 0.001) ratings following intervention. There was also a significant reduction in breathing (6.8 to 5.6, p=0.047), cough (7.6 to 6.3, p=0.014), and limitation ( 2.3 to 2.0, p=0.038) scores in the placebo group, but the degree of improvement was significantly less than in the treatment group (p < 0.01). Clinical judgement of outcome indicated successful ratings in 88% of participants in the treatment group compared with 14% in the placebo group ( p, 0.001). Conclusion: Speech pathology is an effective management intervention for chronic cough which may be a viable alternative for patients who do not respond to medical treatment.

Cognitive neuroimaging: Cognitive science out of the armchair

Cognitive scientists were not quick to embrace the functional neuroimaging technologies that emerged during the late 20th century. In this new century, cognitive scientists continue to question, not unreasonably, the relevance of functional neuroimaging investigations that fail to address questions of interest to cognitive science. However, some ultra-cognitive scientists assert that these experiments can never be of relevance to the Study of cognition. Their reasoning reflects an adherence to a functionalist philosophy that arbitrarily and purposefully distinguishes mental information-processing systems from brain or brain-like operations. This article addresses whether data from properly conducted functional neuroimaging studies can inform and Subsequently constrain the assumptions of theoretical cognitive models. The article commences with a focus upon the functionalist philosophy espoused by the ultra-cognitive scientists, contrasting it with the materialist philosophy that motivates both cognitive neuromiaging investigations and connectionist modelling of cognitive systems. Connectionism and cognitive neuroimaging share many features, including an emphasis on unified cognitive and neural models of systems that combine localist and distributed representations. The utility of designing cognitive neuroimaging studies to test (primarily) connectionist models of cognitive phenomena is illustrated using data from functional magnetic resonance imaging (fMRI) investigations of language production and episodic memory. (C) 2005 Elsevier Inc. All rights reserved.

Dysprosodic speech following basal ganglia insult: Toward a conceptual framework for the study of cerebral representation of prosody

Progress in understanding brain/behavior relationships in adult-acquired dysprosody has led to models of cortical hemispheric representation of prosodic processing based on functional (linguistic vs affective) or physical (timing vs pitch) parameters. These explanatory perspectives have not been reconciled, and also a number of neurobehavior syndromes that include dysprosody among their neurological signs have not yet been integrated. In addition to expanding the functional perspective on prosody, some of these syndromes have implicated a significant role of subcortical nuclei in prosodic competence. In this article, two patients with acquired dysprosodic speech following damage to basal ganglia nuclei were evaluated using behavioral, acoustic, cognitive, and radiographic approaches. Selective quantitative measures were performed on each individual’s performance to provide detailed verification and clarification of clinical observations, and to test hypotheses regarding prosodic function. These studies, combined with a review of related clinical research findings, exemplify the value of a broader perspective on the neurobehavioral dysfunction underlying acquired adult dysprosodic speech, and lead to a new, proposed conceptual framework for the cerebral representation of prosody.

Successes and failures in assessing acquired language disorders using an Internet-based telerehabilitation system

We have used a telerehabilitation system (eREHAB) to remotely assess acquired language disorders via the Internet. The system was used to establish a 128 kbit/s videoconference between two sites and allowed a remote language assessment to be conducted using the standardized Boston Diagnostic Aphasia Examination (BDAE). The system had the capacity to display text and images, and could play pre-recorded instructions to the participant via various built-in tools. A touch screen allowed tasks involving picture identification to be completed easily. Eighteen participants with a diagnosis of an acquired language disorder were simultaneously assessed using the eREHAB system, and in the traditional face-to-face manner by two speech pathologists. There was very high agreement between the two assessors, with weighted kappa scores of 0.8–1.0 for 88% of the sub-tests of the BDAE. There was also high agreement (80–100%) and high kappa scores (0.67–0.90) between assessors on the six rating scales relating to language characteristics. The agreement between the two assessors for the diagnosis of the type of aphasia was 83%. Limitations of the system related mainly to problems inherent in IP videoconferencing. The inability to maintain the preferred speed of 128 kbit/s for the duration of the videoconference and the resultant increase in video and audio breakup and latency affected the clinician’s ability to administer the BDAE with the same ease and accuracy as in face-to-face administration. These difficulties were exacerbated when participants presented with a moderate to severe language disorder, auditory comprehension deficits or significant hearing loss. Despite these limitations, a valid assessment of language disorder was found to be feasible via this telerehabilitation application.

A pilot study of online assessment of childhood speech disorders

We investigated the feasibility of assessing childhood speech disorders via an Internet-based telehealth system (eREHAB). The equipment provided videoconferencing through a 128 kbit/s Internet link, and enabled the transfer of pre-recorded video and audio data from the participant to the online clinician. Six children (mean age = 5.3 years) with a speech disorder were studied. Assessments of single-word articulation, intelligibility in conversation, and oro-motor structure and function were conducted for each participant, with simultaneous scoring by a face to face and an online clinician. There were high levels of agreement between the two scoring environments for single-word articulation (92%), speech intelligibility (100%) and oro-motor tasks (91%). High levels of inter- and intra-rater agreement were achieved for the online ratings for most measures. The results suggest that an Internet-based assessment protocol has potential for assessing paediatric speech disorders.

A global vision system for a robot soccer team

This paper describes the real time global vision system for the robot soccer team the RoboRoos. It has a highly optimised pipeline that includes thresholding, segmenting, colour normalising, object recognition and perspective and lens correction. It has a fast ‘paint’ colour calibration system that can calibrate in any face of the YUV or HSI cube. It also autonomously selects both an appropriate camera gain and colour gains robot regions across the field to achieve colour uniformity. Camera geometry calibration is performed automatically from selection of keypoints on the field. The system acheives a position accuracy of better than 15mm over a 4m × 5.5m field, and orientation accuracy to within 1°. It processes 614 × 480 pixels at 60Hz on a 2.0GHz Pentium 4 microprocessor.

Precision-recall operating characteristic (P-ROC) curves in imprecise environments

Traditionally, machine learning algorithms have been evaluated in applications where assumptions can be reliably made about class priors and/or misclassification costs. In this paper, we consider the case of imprecise environments, where little may be known about these factors and they may well vary significantly when the system is applied. Specifically, the use of precision-recall analysis is investigated and compared to the more well known performance measures such as error-rate and the receiver operating characteristic (ROC). We argue that while ROC analysis is invariant to variations in class priors, this invariance in fact hides an important factor of the evaluation in imprecise environments. Therefore, we develop a generalised precision-recall analysis methodology in which variation due to prior class probabilities is incorporated into a multi-way analysis of variance (ANOVA). The increased sensitivity and reliability of this approach is demonstrated in a remote sensing application.

Low-cost real-time gesture recognition

A major impediment to developing real-time computer vision systems has been the computational power and level of skill required to process video streams in real-time. This has meant that many researchers have either analysed video streams off-line or used expensive dedicated hardware acceleration techniques. Recent software and hardware developments have greatly eased the development burden of realtime image analysis leading to the development of portable systems using cheap PC hardware and software exploiting the Multimedia Extension (MMX) instruction set of the Intel Pentium chip. This paper describes the implementation of a computationally efficient computer vision system for recognizing hand gestures using efficient coding and MMX-acceleration to achieve real-time performance on low cost hardware.

Off-line signature verification and forgery detection system based on fuzzy modeling

This paper presents an innovative approach for signature verification and forgery detection based on fuzzy modeling. The signature image is binarized and resized to a fixed size window and is then thinned. The thinned image is then partitioned into a fixed number of eight sub-images called boxes. This partition is done using the horizontal density approximation approach. Each sub-image is then further resized and again partitioned into twelve further sub-images using the uniform partitioning approach. The features of consideration are normalized vector angle (α) from each box. Each feature extracted from sample signatures gives rise to a fuzzy set. Since the choice of a proper fuzzification function is crucial for verification, we have devised a new fuzzification function with structural parameters, which is able to adapt to the variations in fuzzy sets. This function is employed to develop a complete forgery detection and verification system.

Probabilistic visual recognition of artificial landmarks for simultaneous localization and mapping

Probabilistic robotics most often applied to the problem of simultaneous localisation and mapping (SLAM), requires measures of uncertainty to accompany observations of the environment. This paper describes how uncertainty can be characterised for a vision system that locates coloured landmarks in a typical laboratory environment. The paper describes a model of the uncertainty in segmentation, the internal cameral model and the mounting of the camera on the robot. It explains the implementation of the system on a laboratory robot, and provides experimental results that show the coherence of the uncertainty model.

Representation and learning of visual information for pose recognition

Recovering position from sensor information is an important problem in mobile robotics, known as localisation. Localisation requires a map or some other description of the environment to provide the robot with a context to interpret sensor data. The mobile robot system under discussion is using an artificial neural representation of position. Building a geometrical map of the environment with a single camera and artificial neural networks is difficult. Instead it would be simpler to learn position as a function of the visual input. Usually when learning images, an intermediate representation is employed. An appropriate starting point for biologically plausible image representation is the complex cells of the visual cortex, which have invariance properties that appear useful for localisation. The effectiveness for localisation of two different complex cell models are evaluated. Finally the ability of a simple neural network with single shot learning to recognise these representations and localise a robot is examined.