Professional development of health informatics in Northern Ireland.

This paper addresses the assessment and verification of health informatics professional competencies. Postgraduate provision in Health Informatics was targeted at informatics professionals working full-time in the National Health Service, in Northern Ireland, United Kingdom. Many informatics health service positions do not require a formal informatics background, and as we strive for professionalism, a recognized qualification provides important underpinning. The course, delivered from a computing perspective, builds upon work-based achievement and provides insight into emerging technologies associated with the 'connected health' paradigm. The curriculum was designed with collaboration from the Northern Ireland Health and Social Care ICT Training Group. Material was delivered by blended learning using a virtual learning environment and face-to-face sessions. Professional accreditation was of high importance. The aim was to provide concurrent qualifications: a postgraduate certificate, awarded by the University of Ulster and a professional certificate validated and accredited by a professional body comprising experienced health informatics professionals. Providing both qualifications puts significant demands upon part-time students, and a balance must be achieved for successful completion.

Can brain computer interfaces become practical assistive devices in the community?

A Brain Computer Interface (BCI) provides direct communication from the brain to a computer or electronic device. In order for BCIs to become practical assistive devices it is necessary to develop robust systems, which can be used outside of the laboratory. This paper appraises the technical challenges, and outlines the design of an intuitive user interface, which can be used for smart device control and entertainment applications, of specific interest to users. We adopted a user-centred approach, surveying two groups of participants: fifteen volunteers who could use BCI as an additional technology and six users with complex communication and assistive technology needs. Interaction is based on a four way choice, parsing a hierarchical menu structure which allows selection of room location and then device (e.g. light, television) within a smart home. The interface promotes ease of use which aim to improve the BCI communication rate.

Auditory brainstem response classification: a hybrid model using time and frequency features.

OBJECTIVE: The auditory brainstem response (ABR) is an evoked response obtained from brain electrical activity when an auditory stimulus is applied to the ear. An audiologist can determine the threshold level of hearing by applying stimuli at reducing levels of intensity, and can also diagnose various otological, audiological, and neurological abnormalities by examining the morphology of the waveform and the latencies of the individual waves. This is a subjective process requiring considerable expertise. The aim of this research was to develop software classification models to assist the audiologist with an automated detection of the ABR waveform and also to provide objectivity and consistency in this detection. MATERIALS AND METHODS: The dataset used in this study consisted of 550 waveforms derived from tests using a range of stimulus levels applied to 85 subjects ranging in hearing ability. Each waveform had been classified by a human expert as 'response=Yes' or 'response=No'. Individual software classification models were generated using time, frequency and cross-correlation measures. Classification employed both artificial neural networks (NNs) and the C5.0 decision tree algorithm. Accuracies were validated using six-fold cross-validation, and by randomising training, validation and test datasets. RESULTS: The result was a two stage classification process whereby strong responses were classified to an accuracy of 95.6% in the first stage. This used a ratio of post-stimulus to pre-stimulus power in the time domain, with power measures at 200, 500 and 900Hz in the frequency domain. In the second stage, outputs from time, frequency and cross-correlation classifiers were combined using the Dempster-Shafer method to produce a hybrid model with an accuracy of 85% (126 repeat waveforms). CONCLUSION: By combining the different approaches a hybrid system has been created that emulates the approach used by an audiologist in analysing an ABR waveform. Interpretation did not rely on one particular feature but brought together power and frequency analysis as well as consistency of subaverages. This provided a system that enhanced robustness to artefacts while maintaining classification accuracy.

A comparison of supervised classification methods for auditory brainstem response determination.

The ABR is commonly used in the Audiology clinic to determine and quantify hearing loss. Its interpretation is subjective, dependent upon the expertise and experience of the clinical scientist. In this study we investigated the role of machine learning for pattern classification in this domain. We extracted features from the ABRs of 85 test subjects (550 waveforms) and compared four complimentary supervised classification methods: Naïve Bayes, Support Vector Machine Multi-Layer Perceptron and KStar. The Abr dataset comprised both high level and near threshold recordings, labeled as 'response' or 'no response' by the human expert. Features were extracted from single averaged recordings to make the classification process straightforward. A best classification accuracy of 83.4% was obtained using Naïve Bayes and five relevant features extracted from time and wavelet domains. Naïve Bayes also achieved the highest specificity (86.3%). The highest sensitivity (93.1%) was obtained with Support Vector Machine-based classification models. In terms of the overall classification accuracy, four classifiers have shown the consistent, relatively high performance, indicating the relevance of selected features and the feasibility of using machine learning and statistical classification models in the analysis of ABR.

Combining wavelet analysis and Bayesian networks for the classification of auditory brainstem response.

The auditory brainstem response (ABR) has become a routine clinical tool for hearing and neurological assessment. In order to pick out the ABR from the background EEG activity that obscures it, stimulus-synchronized averaging of many repeated trials is necessary, typically requiring up to 2000 repetitions. This number of repetitions can be very difficult, time consuming and uncomfortable for some subjects. In this study, a method combining wavelet analysis and Bayesian networks is introduced to reduce the required number of repetitions, which could offer a great advantage in the clinical situation. 314 ABRs with 64 repetitions and 155 ABRs with 128 repetitions recorded from eight subjects are used here. A wavelet transform is applied to each of the ABRs, and the important features of the ABRs are extracted by thresholding and matching the wavelet coefficients. The significant wavelet coefficients that represent the extracted features of the ABRs are then used as the variables to build the Bayesian network for classification of the ABRs. In order to estimate the performance of this approach, stratified ten-fold cross-validation is used.