Language, perception and production in profoundly deaf children

Prelingually profoundly deaf children usually experience problems with language learning (Webster, 1986; Campbell, Burden & Wright, 1992). The acquisition of written language would be no problem for them if normal development of reading and writing was not dependent on spoken language (Pattison, 1986). However, such children cannot be viewed as a homogeneous group since some, the minority, do develop good linguistic skills. Group studies have identified several factors relating to language skills: hearing loss and level of loss, I.Q., intelligibility, lip-reading, use of phonology and memory capacity (Furth, 1966; Conrad, 1979; Trybus & Karchmer, 1977; Jensema, 1975; Baddeley, Papagno & Vallar, 1988; Baddeley & Wilson, 1988; Hanson, 1989; Lake, 1980; Daneman & Carpenter,1980). These various factors appear to be interrelated, with phonological awareness being implicated in most. So to understand behaviour, measures of all these factors must be obtained. The present study aimed to achieve this whilst investigating the prediction that performance success may be due to better use of phonological information. Because linguistic success for the deaf child is exceptional, a case study approach was taken to avoid obscuring subtle differences in performance. Subjects were screened to meet 6 research criteria: profound prelingual deafness, no other known handicap, English the first language in the home, at least average non-verbal IQ , reading age 7-9 years and inter-subject dissimilarities between chronological reading age discrepancies. Case histories were obtained from school records and home interviews. Six subjects with diverse linguistic skills were selected, four of which undertook all tests. Phonological awareness and development was assessed across several variables: immediate memory span, intelligibility, spelling, rhyme judgement, speech discrimination and production. There was considerable inter-subject performance difference. One boy's speech production was singled out for a more detailed analysis. Useful aided hearing and consistent contrastive speech appear to be implicated in other English language skills. It was concluded that for phonological awareness to develop, the deaf child must receive useful inputs from as many media as possible (e.g., vision, audition, articulation, sign and orthography). When input is biassed toward the more reliable modalities of audition and articulation, there is a greater possibility of a robust and useful phonology being derived and thus better access to the English language

Diagnosis of hearing deficiency using EEG based AEP signals: CWT and improved-VGG16 pipeline

Hearing deficiency is the world’s most common sensation of impairment and impedes human communication and learning. Early and precise hearing diagnosis using electroencephalogram (EEG) is referred to as the optimum strategy to deal with this issue. Among a wide range of EEG control signals, the most relevant modality for hearing loss diagnosis is auditory evoked potential (AEP) which is produced in the brain’s cortex area through an auditory stimulus. This study aims to develop a robust intelligent auditory sensation system utilizing a pre-train deep learning framework by analyzing and evaluating the functional reliability of the hearing based on the AEP response. First, the raw AEP data is transformed into time-frequency images through the wavelet transformation. Then, lower-level functionality is eliminated using a pre-trained network. Here, an improved-VGG16 architecture has been designed based on removing some convolutional layers and adding new layers in the fully connected block. Subsequently, the higher levels of the neural network architecture are fine-tuned using the labelled time-frequency images. Finally, the proposed method’s performance has been validated by a reputed publicly available AEP dataset, recorded from sixteen subjects when they have heard specific auditory stimuli in the left or right ear. The proposed method outperforms the state-of-art studies by improving the classification accuracy to 96.87% (from 57.375%), which indicates that the proposed improved-VGG16 architecture can significantly deal with AEP response in early hearing loss diagnosis

A hybrid scheme for AEP based hearing deficiency diagnosis: CWT and convoluted k-nearest neighbour (CKNN) pipeline

The auditory evoked potential (AEP) has been considered a standard clinical instrument for hearing and neurological evaluation. Although several approaches for learning EEG signal characteristics have been established earlier, the hybridization concept has rarely been explored to produce novel representations of AEP features and achieve further performance enhancement for AEP signals. Moreover, the classification of auditory attention within a concise time interval is still facing some challenges. To address this concern, this study has proposed a hybridization scheme, represented as a hybrid convoluted k-nearest neighbour (CKNN) algorithm, consisting of concatenating the convolutional layer of CNN with k-nearest neighbour (k-NN) classifier. The proposed architecture helps in improving the accuracy of KNN from 83.23% to 92.26% with a 3-second decision window. The effect of several concise decision windows is also investigated in this analysis. The proposed architecture is validated by a publicly benchmark AEP dataset, and the outcomes indicate that the CKNN significantly outperforms other state-of-the-art techniques with a concise decision window. The proposed framework shows superior performance in a concise decision window that can be effectively used for early hearing deficiency diagnosis. This paper also presents several discoveries that could be helpful to the neurological community

Innovative Evidence-Based Assessment and Treatment of Oropharyngeal Dysphagia and Communication Disorders in Infants and Young Children at High Risk of Cerebral Palsy

Infants with cerebral palsy (CP) have concomitant feeding and communication disorders with lifelong detrimental consequences, including premature death. Early detection and intervention for these deficits is under-researched. This thesis investigates evidence, current practice, innovative assessment, and novel intervention for these domains. To critically appraise evidence for all interventions for children with CP, a large-scale systematic review was conducted. There is high level evidence to support the use of electrical stimulation alongside oral sensory motor interventions, and Functional Chewing Training. Low positive evidence exists supporting direct intervention for literacy, parent training and augmentative alternative communication for language and communication disorders. An international survey of dysphagia practice revealed lack of alignment with evidence, with few patients receiving gold standard assessments, adaptation over direct treatment, and children receiving less-intensive treatment than adults. Ultrasound and Fibreoptic Endoscopic Evaluation of Swallowing were piloted as novel instrumental assessments of OPD in infants with CP. These tools show promise for safe early detection of OPD and warrant more research to establish psychometrics. A second systematic review was undertaken to determine evidence for OPD interventions specifically for infants with CP. Results found that neuroplasticity and motor learning-based interventions are most promising. The Baby Intensive Early Active Treatment (BabiEAT) program was then designed to harness plasticity and was tested against standard care in a pilot randomised control trial. Results showed that BabiEAT was feasible and acceptable, and conferred superior gains in feeding and parental quality of life while maintaining health and safety. To limit preventable death and optimise outcomes, speech pathologists must keep abreast of evidence, upskill, and implement new successful approaches

Efficiency evaluation of external environments control using bio-signals

There are many types of bio-signals with various control application prospects. This dissertation regards possible application domain of electroencephalographic signal. The implementation of EEG signals, as a source of information used for control of external devices, became recently a growing concern in the scientific world. Application of electroencephalographic signals in Brain-Computer Interfaces (BCI) (variant of Human-Computer Interfaces (HCI)) as an implement, which enables direct and fast communication between the human brain and an external device, has become recently very popular. Currently available on the market, BCI solutions require complex signal processing methodology, which results in the need of an expensive equipment with high computing power. In this work, a study on using various types of EEG equipment in order to apply the most appropriate one was conducted. The analysis of EEG signals is very complex due to the presence of various internal and external artifacts. The signals are also sensitive to disturbances and non-stochastic, what makes the analysis a complicated task. The research was performed on customised (built by the author of this dissertation) equipment, on professional medical device and on Emotiv EPOC headset. This work concentrated on application of an inexpensive, easy to use, Emotiv EPOC headset as a tool for gaining EEG signals. The project also involved application of embedded system platform - TS-7260. That solution caused limits in choosing an appropriate signal processing method, as embedded platforms characterise with a little efficiency and low computing power. That aspect was the most challenging part of the whole work. Implementation of the embedded platform enables to extend the possible future application of the proposed BCI. It also gives more flexibility, as the platform is able to simulate various environments. The study did not involve the use of traditional statistical or complex signal processing methods. The novelty of the solution relied on implementation of the basic mathematical operations. The efficiency of this method was also presented in this dissertation. Another important aspect of the conducted study is that the research was carried out not only in a laboratory, but also in an environment reflecting real-life conditions. The results proved efficiency and suitability of the implementation of the proposed solution in real-life environments. The further study will focus on improvement of the signal-processing method and application of other bio-signals - in order to extend the possible applicability and ameliorate its effectiveness

UWOMJ Volume 64, Number 1, Winter 1994

Schulich School of Medicine & Dentistryhttps://ir.lib.uwo.ca/uwomj/1241/thumbnail.jp