Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milan, Italy.
Dalle Molle Institute for Artificial Intelligence, University of Southern Switzerland, University of Applied Science and Arts of Southern Switzerland, Lugano, Switzerland.
J Sleep Res. 2022 Oct;31(5):e13567. doi: 10.1111/jsr.13567. Epub 2022 Feb 20.
The aim of this study was to assess, with numerical simulations, if the complex mechanism of two (or more) interacting spinal/supraspinal structures generating periodic leg movements can be modelled with a single-generator approach. For this, we have developed the first phenomenological model to generate periodic leg movements in-silico. We defined the onset of a movement in one leg as the firing of a neuron integrating excitatory and inhibitory inputs from the central nervous system, while the duration of the movement was defined in accordance to statistical evidence. For this study, polysomnographic leg movement data from 32 subjects without periodic leg movements and 65 subjects with periodic leg movements were used. The proportion of single-leg and double-leg inputs, as well as their strength and frequency, were calibrated on the without periodic leg movements dataset. For periodic leg movements subjects, we added a periodic excitatory input common to both legs, and the distributions of the generator period and intensity were fitted to their dataset. Besides the many simplifying assumptions - the strongest being the stationarity of the generator processes during sleep - the model-simulated data did not differ significantly, to a large extent, from the real polysomnographic data. This represents convincing preliminary support for the validity of our single-generator model for periodic leg movements. Future model extensions will pursue the ambitious project of a supportive diagnostic and therapeutic tool, helping the specialist with realistic forecasting, and with cross-correlations and clustering with other patient meta-data.
本研究旨在通过数值模拟评估,两个(或更多)相互作用的脊髓/脊髓上结构产生周期性腿部运动的复杂机制是否可以通过单一发生器方法进行建模。为此,我们开发了第一个用于在计算机中产生周期性腿部运动的现象学模型。我们将一条腿的运动起始定义为从中枢神经系统整合兴奋性和抑制性输入的神经元的放电,而运动的持续时间则根据统计证据来定义。在这项研究中,使用了 32 名无周期性腿部运动的受试者和 65 名有周期性腿部运动的受试者的多导睡眠图腿部运动数据。单腿和双腿输入的比例及其强度和频率在无周期性腿部运动数据集中进行了校准。对于周期性腿部运动受试者,我们添加了一个共同作用于两条腿的周期性兴奋性输入,并且发生器周期和强度的分布与他们的数据集相拟合。除了许多简化假设 - 最强的是发生器过程在睡眠期间的稳定性 - 模型模拟数据在很大程度上与真实的多导睡眠图数据没有显著差异。这为我们的周期性腿部运动单一发生器模型的有效性提供了令人信服的初步支持。未来的模型扩展将追求一个支持性诊断和治疗工具的雄心勃勃的项目,帮助专家进行现实的预测,并与其他患者元数据进行交叉相关和聚类。