# Hybrid Pipeline SWD Detection in Long-Term EEG Signals

arXiv: https://arxiv.org/abs/2509.19387 · HTML: https://mlsanity.com/paper/2509.19387/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 46 references that are in the map.
- First posted on arXiv 22 Sep 2025.

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

- [Spike-and-wave epileptiform discharge pattern detection based on Kendall's Tau-b coefficient](https://mlsanity.com/paper/1911.13018/) (2019)
- [Effect sizes as a statistical feature-selector-based learning to detect breast cancer](https://mlsanity.com/paper/2411.06868/) (2024)

## Abstract (arXiv)

Spike-and-wave discharges (SWDs) are the electroencephalographic hallmark of absence epilepsy, yet their manual identification in multi-day recordings remains labour-intensive and error-prone. We present a lightweight hybrid pipeline that couples analytical features with a shallow artificial neural network (ANN) for accurate, patient-specific SWD detection in long-term, monopolar EEG. A two-sided moving-average (MA) filter first suppresses the high-frequency components of normal background activity. The residual signal is then summarised by the mean and the standard deviation of its normally distributed samples, yielding a compact, two-dimensional feature vector for every 20s window. These features are fed to a single-hidden-layer ANN trained via back-propagation to classify each window as SWD or non-SWD. The method was evaluated on 780 channels sampled at 256 Hz from 12 patients, comprising 392 annotated SWD events. It correctly detected 384 events (sensitivity: 98%) while achieving a specificity of 96.2 % and an overall accuracy of 97.2%. Because feature extraction is analytic, and the classifier is small, the pipeline runs in real-time and requires no manual threshold tuning. These results indicate that normal-distribution descriptors combined with a modest ANN provide an effective and computationally inexpensive solution for automated SWD screening in extended EEG recordings.