Trusted research environments for health data (2024)
Digital Artefact
Western, D., Ritchie, F., & Gibson, A. (2024). Trusted research environments for health data. [Video/workshop]

This video captures the workshop on 'Trusted Research Environments for Health Data', delivered by David Western, Felix Ritchie, and Andy Gibson. This was part of the Connect, Collaborate, Create with Data and AI 2023 event organised by UWE Bristol an... Read More about Trusted research environments for health data.

Scope and arbitration in machine learning clinical EEG classification (2023)
Conference Proceeding
Zhu, Y., Canham, L., & Western, D. (2023). Scope and arbitration in machine learning clinical EEG classification. In 2023 IEEE Signal Processing in Medicine and Biology Symposium (SPMB). https://doi.org/10.1109/SPMB59478.2023.10372635

A key task in clinical EEG interpretation is to classify a recording or session as normal or abnormal. In machine learning approaches to this task, recordings are typically divided into shorter windows for practical reasons, and these windows inherit... Read More about Scope and arbitration in machine learning clinical EEG classification.

Exploring the potential of a pronunciation guide app to support non-native speaking students in higher education (2023)
Conference Proceeding
Western, D. (2023). Exploring the potential of a pronunciation guide app to support non-native speaking students in higher education. In ICETC '23: Proceedings of the 15th International Conference on Education Technology and Computers (78–85). https://doi.org/10.1145/3629296.3629309

Non-native speaking (NNS) students form an important part of the student population in the UK and many other countries’ higher education sectors. They face numerous additional challenges compared with their native speaking counterparts. Software to s... Read More about Exploring the potential of a pronunciation guide app to support non-native speaking students in higher education.

Automatic report-based labelling of clinical EEGs for classifier training (2022)
Conference Proceeding
Western, D., Weber, T., Kandasamy, R., May, F., Taylor, S., Zhu, Y., & Canham, L. (2022). Automatic report-based labelling of clinical EEGs for classifier training. In 2021 IEEE Signal Processing in Medicine and Biology Symposium (SPMB). https://doi.org/10.1109/SPMB52430.2021.9672295

Machine learning classifiers for detection of abnormal clinical electroencephalography (EEG) signals have advanced signficantly in recent years, largely supported by the carefully curated Temple University Hospital Abnormal EEG Corpus (TUAB). Further... Read More about Automatic report-based labelling of clinical EEGs for classifier training.