The NMT Scalp EEG Dataset: An Open-Source Annotated Dataset of Healthy and Pathological EEG Recordings for Predictive Modeling

Electroencephalogram (EEG) is widely used for the diagnosis of neurological conditions like epilepsy, neurodegenerative illnesses and sleep related disorders. Proper interpretation of EEG recordings requires the expertise of trained neurologists, a resource which is scarce in the developing world. Neurologists spend a significant portion of their time sifting through EEG recordings looking for abnormalities. Most recordings turn out to be completely normal, owing to the low yield of EEG tests. To minimize such wastage of time and effort, automatic algorithms could be used to provide pre-diagnostic screening to separate normal from abnormal EEG. Data driven machine learning offers a way forward however, design and verification of modern machine learning algorithms require properly curated labeled datasets. To avoid bias, deep learning based methods must be trained on large datasets from diverse sources. This work presents a new open-source dataset, named the NMT Scalp EEG Dataset, consisting of 2,417 recordings from unique participants spanning almost 625 h. Each recording is labeled as normal or abnormal by a team of qualified neurologists. Demographic information such as gender and age of the patient are also included. Our dataset focuses on the South Asian population. Several existing state-of-the-art deep learning architectures developed for pre-diagnostic screening of EEG are implemented and evaluated on the NMT, and referenced against baseline performance on the well-known Temple University Hospital EEG Abnormal Corpus. Generalization of deep learning based architectures across the NMT and the reference datasets is also investigated. The NMT dataset is being released to increase the diversity of EEG datasets and to overcome the scarcity of accurately annotated publicly available datasets for EEG research

NeuroAssist: Open-Source Automatic Event Detection in Scalp EEG

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