# Imaging the time series of one single referenced EEG electrode for Epileptic Seizures Risk Analysis

arXiv: https://arxiv.org/abs/2206.14520 · HTML: https://mlsanity.com/paper/2206.14520/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 18 references that are in the map.
- First posted on arXiv 29 Jun 2022.

## Builds on (most cited first, 2 of 2)

- [Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review](https://mlsanity.com/paper/2002.01925/) (2020)
- [Epileptic Seizure Risk Assessment by Multi-Channel Imaging of the EEG](https://mlsanity.com/paper/2204.07034/) (2022)

## Abstract (arXiv)

The time series captured by a single scalp electrode (plus the reference electrode) of refractory epileptic patients is used to forecast seizures susceptibility. The time series is preprocessed, segmented, and each segment transformed into an image, using three different known methods: Recurrence Plot, Gramian Angular Field, Markov Transition Field. The likelihood of the occurrence of a seizure in a future predefined time window is computed by averaging the output of the softmax layer of a CNN, differently from the usual consideration of the output of the classification layer. By thresholding this likelihood, seizure forecasting has better performance. Interestingly, for almost every patient, the best threshold was different from 50%. The results show that this technique can predict with good results for some seizures and patients. However, more tests, namely more patients and more seizures, are needed to better understand the real potential of this technique.