# Seizure detection from Electroencephalogram signals via Wavelets and Graph Theory metrics

arXiv: https://arxiv.org/abs/2312.00811 · HTML: https://mlsanity.com/paper/2312.00811/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 48 references that are in the map.
- First posted on arXiv 28 Nov 2023.

## Builds on (most cited first, 4 of 4)

- [Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation](https://mlsanity.com/paper/2010.16061/) (2020)
- [Graph analysis of functional brain networks: practical issues in translational neuroscience](https://mlsanity.com/paper/1406.7391/) (2014)
- [An Infra-Structure for Performance Estimation and Experimental Comparison of Predictive Models in R](https://mlsanity.com/paper/1412.0436/) (2014)
- [EEG Signal Processing using Wavelets for Accurate Seizure Detection through Cost Sensitive Data Mining](https://mlsanity.com/paper/2109.13818/) (2021)

## Abstract (arXiv)

Epilepsy is one of the most prevalent neurological conditions, where an epileptic seizure is a transient occurrence due to abnormal, excessive and synchronous activity in the brain. Electroencephalogram signals emanating from the brain may be captured, analysed and then play a significant role in detection and prediction of epileptic seizures. In this work we enhance upon a previous approach that relied on the differing properties of the wavelet transform. Here we apply the Maximum Overlap Discrete Wavelet Transform to both reduce signal textitnoise and use signal variance exhibited at differing inherent frequency levels to develop various metrics of connection between the electrodes placed upon the scalp. %The properties of both the noise reduced signal and the interconnected electrodes differ significantly during the different brain states. Using short duration epochs, to approximate close to real time monitoring, together with simple statistical parameters derived from the reconstructed noise reduced signals we initiate seizure detection. To further improve performance we utilise graph theoretic indicators from derived electrode connectivity. From there we build the attribute space. We utilise open-source software and publicly available data to highlight the superior Recall/Sensitivity performance of our approach, when compared to existing published methods.