{"ID":2867462,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.19387","arxiv_id":"2509.19387","title":"Hybrid Pipeline SWD Detection in Long-Term EEG Signals","abstract":"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.","short_abstract":"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 ac...","url_abs":"https://arxiv.org/abs/2509.19387","url_pdf":"https://arxiv.org/pdf/2509.19387v1","authors":"[\"Antonio Quintero Rincon\",\"Nicolas Masino\",\"Veronica Marsico\",\"Hadj Batatia\"]","published":"2025-09-22T02:45:43Z","proceeding":"eess.SP","tasks":"[\"eess.SP\",\"cs.LG\",\"stat.AP\",\"stat.ML\"]","methods":"[\"Large Language Model\"]","has_code":false}