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Localisation of clinically relevant events within Electroencephalogram (EEG) recordings can be useful for explaining the decisions made by automated EEG screening and decision support systems. The majority of existing deep learning based approaches that have been proposed in recent literature only classify EEG records as normal or pathological without providing any justification for their decisions and thus are not very transparent. In clinical practice it is often observed that a significant proportion of EEG recordings does not contain any abnormal (or pathological) events; even in cases classified as pathological. If deployed in practice such a setup would not be very useful since it would require neurologists to invest additional time, manually searching for events within an EEG recording before accepting or rejecting the decision proposed by the automated system. This work presents open-source software that can automatically localise and classify abnormalities both across time and EEG channels. Our work can thus be used to reveal the reasons behind an EEG recording being classified as normal or pathological/abnormal. Training an automated event localisation system requires a dataset containing fine-grained labels pointing out precise locations of events. To facilitate further development we are also releasing the dataset and annotations used in this work for use by the research community. This dataset contains 1,075 EEG recordings with precise temporal and channel locations of two broad categories of abnormal events: (i) Epileptiform discharges and (ii) Non-epileptiform abnormalities. Our localisation system is based on features derived from wavelet transforms. For event classification we investigated the performance of both classic machine learning algorithms (support vector machines, decision trees, random forest classifier) and deep convolutional neural networks (VGG16, GoogLeNet and EfficientNet). Our results indicate that deep convolutional neural networks outperform classic machine learning algorithms in terms of average values of precision, recall, F1-score and accuracy.

Introduction Preliminary clinical evaluation (PCE) can be a useful initial assessment of traumatic abnormalities by frontline radiographers; new graduates are expected to have the skills and knowledge required to provide this initial interpretation. This study evaluates the abnormality detection performance and accuracy of PCE commenting in newly qualified radiographers. Method Four newly qualified radiographers completed a fracture/dislocation detection task consisting of 58 cases, including providing a PCE for each suspicious area. Following this, an 8-week training program was completed to improve competence in recognizing abnormalities and providing an accurate PCE. Equally weighted jackknife alternative free-response receiver operating characteristic (wJAFROC) analysis was performed; a difference between pre- and post-training would be considered significant at a test alpha of less than 0.05. Results Fracture/dislocation detection was significantly better in the post-training evaluation for fixed observers and random cases ( F (1,57) = 4.48, p = 0.0387). The reader averaged wJAFROC FOM and 95% CIs for pre- and post-training were 0.619 (0.516, 0.737) and 0.703 (0.622, 0.852). A paired t-test demonstrated a significant difference in PCE scores in favour of the post-training evaluation p = 0.0006. This small cohort demonstrated difficulty in recognising undisplaced fractures and buckle fractures. Conclusion An 8-week training program had a positive impact on participants' ability to localise and accurately describe fractures. Implementation of abnormality detection training should be considered during preceptorship periods. Due to the small sample size, it is inappropriate to suggest these findings are representative of all graduate radiographers. [ABSTRACT FROM AUTHOR]