Epileptiform Activity and Seizure Risk Follow Long-Term Non-Linear Attractor Dynamics

被引:0
作者
Rosch, Richard E. [1 ,2 ,3 ]
Scheid, Brittany [4 ,5 ]
Davis, Kathryn A. [6 ]
Litt, Brian [4 ,5 ,6 ]
Ashourvan, Arian [7 ]
机构
[1] Columbia Univ, Irving Med Ctr, Dept Pediat, New York, NY 10032 USA
[2] Columbia Univ, Irving Med Ctr, Dept Neurol, New York, NY 10032 USA
[3] Kings Coll London, Inst Psychiat Psychol & Neurosci, Dept Basic & Clin Neurosci, Cutcombe Rd, London SE5 9RT, England
[4] Univ Penn, Sch Engn & Appl Sci, Dept Bioengn, Philadelphia, PA 19104 USA
[5] Univ Penn, Penn Ctr Neuroengn & Therapeut, Philadelphia, PA 19104 USA
[6] Univ Penn, Perelman Sch Med, Dept Neurol, Philadelphia, PA 19104 USA
[7] Univ Kansas, Dept Psychol, Lawrence, KS 66045 USA
基金
英国惠康基金;
关键词
delay-embedding; Hankel alternative view of Koopman (HAVOK); singular value decomposition (SVD); EPILEPSY; RHYTHMS; CYCLES; SYSTEM;
D O I
10.1002/advs.202411829
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Many biological systems display circadian and slow multi-day rhythms, such as hormonal and cardiac cycles. In patients with epilepsy, these cycles also manifest as slow cyclical fluctuations in seizure propensity. However, such fluctuations in symptoms are consequences of the complex interactions between the underlying physiological, pathophysiological, and external causes. Therefore, identifying an accurate model of the underlying system that governs the multi-day rhythms allows for a more reliable seizure risk forecast and targeted interventions. The primary aim is to develop a personalized strategy for inferring long-term trajectories of epileptiform activity and, consequently, seizure risk for individual patients undergoing long-term ECoG sampling via implantable neurostimulation devices. To achieve this goal, the Hankel alternative view of Koopman (HAVOK) analysis is adopted to approximate a linear representation of nonlinear seizure propensity dynamics. The HAVOK framework leverages Koopman theory and delay-embedding to decompose chaotic dynamics into a linear system of leading delay-embedded coordinates driven by the low-energy coordinate (i.e., forcing). The findings reveal the topology of attractors underlying multi-day seizure cycles, showing that seizures tend to occur in regions of the manifold with strongly nonlinear dynamics. Moreover, it is demonstrated that the identified system driven by forcings with short periods up to a few days accurately predicts patients' slower multi-day rhythms, which improves seizure risk forecasting.
引用
收藏
页数:11
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