A New Perspective in Epilepsy Classification: Applying the Taxonomy of Seizure Dynamotypes to Non-Invasive EEG and examining dynamical changes across sleep stages.

Epilepsy, a neurological disorder characterized by recurrent unprovoked seizures, significantly impacts patient quality of life. Current classification methods focus primarily on clinical observations and electroencephalography (EEG) analysis, often overlooking the underlying dynamics driving seizures. This study uses surface EEG data to identify seizure transitions using a dynamical systems-based framework-the taxonomy of seizure dynamotypes-previously examined only in invasive data. We applied principal component and independent component analysis to surface EEG recordings from 1,177 seizures in 158 patients with focal epilepsy, decomposing the signals into independent components (ICs). The ICs were visually labeled for clear seizure transitions and bifurcation morphologies, which were then examined using Bayesian multilevel modeling in the context of clinical factors.Our analysis reveals that certain onset bifurcations (SNIC and SupH) are more prevalent during wakefulness compared to their reduced rate during non-rapid eye movement (NREM) sleep, particularly NREM3. We discuss the possible implications of our results in the context of modeling approaches and suggest additional avenues to continue this exploration.Furthermore, we demonstrate the feasibility of automating this classification process using machine learning, achieving high performance in identifying seizure-related ICs and classifying inter-spike interval changes. Our findings suggest that the noise in surface EEG may obscure certain bifurcation morphologies, and we suggest technical improvements that could enhance detection accuracy. Expanding the dataset and incorporating long-term biological rhythms, such as circadian and multiday cycles, may provide a more comprehensive understanding of seizure dynamics and improve clinical decision-making. Traditional seizure classification focuses on clinical symptoms and electrophysiological signs but often overlooks the underlying seizure dynamics. The taxonomy of seizure dynamotypes introduces a novel computational approach that links electrophysiological transition signatures to these dynamics. While previously applied to invasive recordings, this study extends the taxonomy to non-invasive EEG. Our analysis reveals a relationship between sleep stages and seizure dynamics. We suggest that integrating these modeling approaches with sleep and circadian dynamics models may reveal insights into seizure timing and generalization, opening new pathways for better diagnostics. Broader adoption of this classification is limited by its labor-intensive visual inspection process. Here, we demonstrate the potential of automated classification, enabling analysis to scale to larger cohorts.