Epileptic multi-seizure type classification using electroencephalogram signals from the Temple University Hospital Seizure Corpus: A review

McCallan, Niamh; Davidson, Scot; Ng, Kok Yew; Biglarbeigi, Pardis; Finlay, Dewar; Lan, Boon Leong; McLaughlin, James

doi:10.1016/j.eswa.2023.121040

Cited by 9 publications

(3 citation statements)

References 63 publications

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“…(Fisher et al, 2017;McCallan et al, 2023). Detecting these different types of seizures is crucial in epilepsy as it can assist in enhanced diagnosis, more targeted therapies, and better control of seizures (McCallan et al, 2023). However, supervised approaches can only be as good as our de nitions of these seizure types.…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

Semi-automated seizure detection using interpretable machine learning models

Antonoudiou,

Basu,

Maguire

2024

Preprint

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Despite the vast number of seizure detection publications there are no validated open-source tools for automating seizure detection based on electrographic recordings. Researchers instead rely on manual curation of seizure detection that is highly laborious, inefficient, error prone, and heavily biased. Here we developed an open-source software called SeizyML that uses sensitive machine learning models coupled with manual validation of detected events reducing bias and promoting efficient and accurate detection of electrographic seizures. We compared the validity of four interpretable machine learning models (decision tree, gaussian naïve bayes, passive aggressive classifier, and stochastic gradient descent classifier) on an extensive electrographic seizure dataset that we collected from chronically epileptic mice. We find that the gaussian naïve bayes and stochastic gradient descent models achieved the highest precision and f1 scores, while also detecting all seizures in our mouse dataset and only require a small amount of data to train the model and achieve good performance. Further, we demonstrate the utility of this approach to detect electrographic seizures in a human EEG dataset. This approach has the potential to be a transformative research tool overcoming the analysis bottleneck that slows research progress.

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Section: Limitations and Future Directionsmentioning

confidence: 99%

Semi-automated seizure detection using interpretable machine learning models

Antonoudiou,

Basu,

Maguire

2024

Preprint

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“…Yet, there is a broad range of seizures as categorized by clinical outcomes (such as focal, generalized tonic-clonic, etc.) (Fisher et al, 2017; McCallan et al, 2023). Detecting these different types of seizures is crucial in epilepsy as it can assist in enhanced diagnosis, more targeted therapies, and better control of seizures (McCallan et al, 2023).…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

“…(Fisher et al, 2017; McCallan et al, 2023). Detecting these different types of seizures is crucial in epilepsy as it can assist in enhanced diagnosis, more targeted therapies, and better control of seizures (McCallan et al, 2023). However, supervised approaches can only be as good as our definitions of these seizure types.…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

Semi-automated seizure detection using interpretable machine learning models

Antonoudiou,

Basu,

Maguire

2023

Preprint

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Numerous methods have been proposed for seizure detection automation, yet the tools to harness these methods and apply them in practice are limited. Here we compare four interpretable and widely-used machine learning models (decision tree, gaussian naive bayes, passive aggressive classifier, stochastic gradient descent classifier) on an extensive electrographic seizure dataset collected from chronically epileptic mice. We find that the gaussian naive bayes model achieved the highest precision and f1 score, while also detecting all seizures in our dataset and only requires a small amount of data to train the model and achieve good performance. We use this model to create an open-source python application SeizyML that couples model performance with manual curation allowing for efficient and accurate detection of electrographic seizures.

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Enhancing Epilepsy Diagnosis with Deep Learning and Multi-channel Processing of EEG Signals

Yang,

Zhou,

et al. 2024

Studies in Computational Intelligence

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Epileptic multi-seizure type classification using electroencephalogram signals from the Temple University Hospital Seizure Corpus: A review

Cited by 9 publications

References 63 publications

Semi-automated seizure detection using interpretable machine learning models

Semi-automated seizure detection using interpretable machine learning models

Semi-automated seizure detection using interpretable machine learning models

Enhancing Epilepsy Diagnosis with Deep Learning and Multi-channel Processing of EEG Signals

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