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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.

Disclosure control issues in complex medical data (2023)
Presentation / Conference
Green, E., Ritchie, F., Smith, J., Western, D., & White, P. (2023, September). Disclosure control issues in complex medical data. Paper presented at UNECE/Eurostat Expert Group on Statisticial Data Confidentiality, Wiesbaden

The covid19 pandemic assisted the acceleration of routine access to medical records for research. In the UK platforms including OpenSafely and NHSDigital, alongside emerging hospital trust based Trusted Research Environments (TREs), demonstrate the u... Read More about Disclosure control issues in complex medical data.

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.