File(s) under embargo

Reason: 12 month embargo requested by publisher

11

month(s)

4

day(s)

until file(s) become available

Automated Tonic-Clonic Seizure Detection Using Random Forests and Spectral Analysis on Electroencephalography Data

conference contribution
posted on 13.09.2022, 13:24 authored by Craig Stewart, Wai Keung FungWai Keung Fung, Nazila Fough, Radhakrishna Prabhu

Artificial intelligence (AI) has a potential for impact in the diagnosis of neurologi-

cal conditions, the academic consensus generally has a positive outlook regarding how AI can

improve the care of stroke victims and those who suffer from neuro-degenerative conditions such

as dementia. When combined with Internet of Things technology, this could facilitate a new par-

adigm for epilepsy treatment. These technologies have applications in improving the welfare of

epileptics, epilepsy being a common neurological condition that can result in premature death

without a quick response. As such it is important for the system to avoid false negatives. This

investigation focused on how machine learning algorithms can be utilised to identify these events

through Electroencephalography (EEG) data. The UCI/Bonn dataset, a classic benchmark for

automated epilepsy detection systems was identified and utilised. This investigation focused on

the random forest algorithm. Given that EEG neurological data represents time series data and

machine learning excels at this task, automation could be achievable via a wearable device. From

there, Fast Fourier Transforms (FFT) were applied to identify if spectral features of EEG signals

would aid identification of seizures. This method achieved an accuracy of 99%, precision of 98%

and a recall of 100% in 12.2 milliseconds time to classify and one second of EEG data. These

results show that random forests combined with FFT are a viable technique for attaining high

recall when detecting grand mal epileptic seizures in short periods of time. CHB-MIT dataset

was utilized for parity also showing good performance.

History

Presented at

6th International Conference on System-Integrated Intelligence (SysInt 2022), September 7-9, 2022, Genova, Italy

Published in

Advances in System-Integrated Intelligence. SYSINT 2022. Lecture Notes in Networks and Systems

Publisher

Springer

Version

AM (Accepted Manuscript)

Citation

Stewart, C., Fung, W.K., Fough, N., Prabhu, R. (2023). Automated Tonic-Clonic Seizure Detection Using Random Forests and Spectral Analysis on Electroencephalography Data. In: , et al. Advances in System-Integrated Intelligence. SYSINT 2022. Lecture Notes in Networks and Systems, vol 546. Springer, Cham. https://doi.org/10.1007/978-3-031-16281-7_64

Print ISSN

2367-3370

Electronic ISSN

2367-3389

ISBN

978-3-031-16280-0

Cardiff Met Affiliation

  • Cardiff School of Technologies

Cardiff Met Authors

Wai Keung Fung

Copyright Holder

© The Publisher

Language

en