Ayyoubi Amir Hossein, Besheli Behrang Fazli, Swamy Chandra Prakash, Okkabaz Jhan L, Quach Michael M, Curry Daniel J, Ince Nuri F
Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, USA.
Department of Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN, USA.
Conf Proc (Midwest Symp Circuits Syst). 2024 Aug;2024:591-595. doi: 10.1109/mwscas60917.2024.10658804. Epub 2024 Sep 16.
The wireless transmission of neural data may pose the risk of packet loss (PL), potentially compromising signal quality or, in extreme cases, causing complete data loss. Addressing lost packets is essential to ensure data integrity and preserve vital neural patterns. This study investigates the effect of PL interference on epilepsy neuro biomarkers, focusing specifically on interictal epileptiform spikes and high frequency oscillations (HFOs), and the performance of the low computational cost interpolation methods. We observed that 95% of spikes and 81% of HFOs could be recovered with linear interpolation at 5% PL, while 97% and 86%, respectively, with spline interpolation. Linear interpolation has the potential to recover neural events and reduce the noise floor with modest packet loss levels of up to 5% at a lower computational cost. However, for a higher level of PL, utilizing more intricate methodologies such as spline interpolation becomes imperative.
神经数据的无线传输可能会带来数据包丢失(PL)的风险,这有可能损害信号质量,在极端情况下甚至会导致数据完全丢失。解决丢失的数据包对于确保数据完整性和保留重要的神经模式至关重要。本研究调查了PL干扰对癫痫神经生物标志物的影响,特别关注发作间期癫痫样放电和高频振荡(HFOs),以及低计算成本插值方法的性能。我们观察到,在5%的PL情况下,线性插值可以恢复95%的尖峰和81%的HFOs,而样条插值分别可以恢复97%和86%。线性插值有可能在计算成本较低的情况下,在高达5%的适度数据包丢失水平下恢复神经事件并降低本底噪声。然而,对于更高水平的PL,使用更复杂的方法(如样条插值)变得势在必行。