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Tonio Ball

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22 papers

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TMLR Journal 2025 Journal Article

Multi-BK-Net: Multi-Branch Multi-Kernel Convolutional Neural Networks for Clinical EEG Analysis

Ann-Kathrin Kiessner
Tonio Ball
Joschka Boedecker

Classifying an electroencephalography (EEG) recording as pathological or non-pathological is an important first step in diagnosing and managing neurological diseases and disorders. As manual EEG classification is costly, time-consuming and requires highly trained experts, deep learning methods for automated classification of general EEG pathology offer a promising option to assist clinicians in screening EEGs. Convolutional neural networks (CNNs) are well-suited for classifying pathological EEG signals due to their ability to perform end-to-end learning. In practice, however, current CNN solutions suffer from limited classification performance due to I) a single-scale network design that cannot fully capture the high intra- and inter-subject variability of the EEG signal, the diversity of the data, and the heterogeneity of pathological EEG patterns and II) the small size and limited diversity of the dataset commonly used to train and evaluate the networks. These challenges result in a low sensitivity score and a performance drop on more diverse patient populations, further hindering their reliability for real-world applications. Here, we propose a novel multi-branch, multi-scale CNN called Multi-BK-Net (Multi-Branch Multi-Kernel Network), comprising five parallel branches that incorporate temporal convolution, spatial convolution, and pooling layers, with temporal kernel sizes defined by five clinically relevant frequency bands in its first block. Evaluation is based on two public datasets with predefined test sets: the Temple University Hospital (TUH) Abnormal EEG Corpus and the TUH Abnormal Expansion Balanced EEG Corpus. Our Multi-BK-Net outperforms five baseline architectures and state-of-the-art end-to-end approaches in terms of accuracy and sensitivity on these datasets, setting a new benchmark. Furthermore, ablation experiments highlight the importance of the multi-branch, multi-scale input block of the Multi-BK-Net. Overall, our findings indicate the efficacy of multi-branch, multi-scale CNNs in accurately and reliably classifying EEG pathology, demonstrating advantages in handling data heterogeneity compared to other deep learning approaches. Thus, this study contributes to the ongoing development of deep end-to-end methods for general EEG pathology classification.

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YNICL Journal 2023 Journal Article

An extended clinical EEG dataset with 15,300 automatically labelled recordings for pathology decoding

Ann-Kathrin Kiessner
Robin T. Schirrmeister
Lukas A.W. Gemein
Joschka Boedecker
Tonio Ball

Automated clinical EEG analysis using machine learning (ML) methods is a growing EEG research area. Previous studies on binary EEG pathology decoding have mainly used the Temple University Hospital (TUH) Abnormal EEG Corpus (TUAB) which contains approximately 3,000 manually labelled EEG recordings. To evaluate and eventually even improve the generalisation performance of machine learning methods for EEG pathology, decoding larger, publicly available datasets is required. A number of studies addressed the automatic labelling of large open-source datasets as an approach to create new datasets for EEG pathology decoding, but little is known about the extent to which training on larger, automatically labelled dataset affects decoding performances of established deep neural networks. In this study, we automatically created additional pathology labels for the Temple University Hospital (TUH) EEG Corpus (TUEG) based on the medical reports using a rule-based text classifier. We generated a dataset of 15,300 newly labelled recordings, which we call the TUH Abnormal Expansion EEG Corpus (TUABEX), and which is five times larger than the TUAB. Since the TUABEX contains more pathological (75%) than non-pathological (25%) recordings, we then selected a balanced subset of 8,879 recordings, the TUH Abnormal Expansion Balanced EEG Corpus (TUABEXB). To investigate how training on a larger, automatically labelled dataset affects the decoding performance of deep neural networks, we applied four established deep convolutional neural networks (ConvNets) to the task of pathological versus non-pathological classification and compared the performance of each architecture after training on different datasets. The results show that training on the automatically labelled TUABEXB dataset rather than training on the manually labelled TUAB dataset increases accuracies on TUABEXB and even for TUAB itself for some architectures. We argue that automatically labelling of large open-source datasets can be used to efficiently utilise the massive amount of EEG data stored in clinical archives. We make the proposed TUABEXB available open source and thus offer a new dataset for EEG machine learning research.