基于相位特异性神经刺激的多种节律性生物信号实时相位检测优化。

IF 3.8
Mengzhan Liufu, Zachary M Leveroni, Sameera Shridhar, Nan Zhou, Jai Y Yu
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引用次数: 0

摘要

目的:在临床和研究中,闭环、相位特异性神经刺激是一种有效的调节持续大脑活动的方法。相位特定刺激依赖于实时估计正在进行的振荡的相位,并在目标相位发出控制命令。基于快速傅里叶变换(FFT)的相位检测算法因其计算效率高、鲁棒性好而得到广泛应用。然而,算法性能如何取决于输入信号的频谱特性以及如何优化算法参数尚不清楚。我们评估了三种相位检测算法(端点校正希尔伯特变换、希尔伯特变换和相位映射)在具有不同频谱特性(啮齿动物海马θ电位、人类脑电图α和人类原发性震颤)的现实世界生物信号上的计算机性能,以确定最佳模型和参数。然后,我们用在海马中植入电极的大鼠验证了算法在实时估计θ相位方面的性能。结果 ;首先,我们发现信号幅度和频率变化对算法性能有很大影响。频率特异性信噪比与性能呈正相关(跨指标平均R2 = 0.42),而幅度和频率可变性呈负相关(跨指标平均R2 = 0.50)。其次,我们发现用于相位估计的数据窗口的大小是基于fft算法的最佳性能的关键参数,其中最佳数据窗口大小对应于振荡周期(海马θ振荡~150 ms,人类脑电图α振荡~100 ms,特发性震颤~125 ms)。该数据窗口长度在体内得到验证,用于估计自由行为大鼠海马的θ波振荡相位,其中一个θ波周期的输入窗口大小与较短或较长的窗口大小相比,在所有指标中产生了最佳性能。 。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimizing real-time phase detection in diverse rhythmic biological signals for phase-specific neurostimulation.

Objective Closed-loop, phase-specific neurostimulation is a powerful method to modulate ongoing brain activity for clinical and research applications. Phase-specific stimulation relies on estimating the phase of an ongoing oscillation in real time and issuing a control command at a target phase. Phase detection algorithms based on the Fast Fourier transform (FFT) are widely used due to their computational efficiency and robustness. However, it is unclear how algorithm performance depends on the spectral properties of the input signal and how algorithm parameters can be optimized. Approach We evaluated the in silico performance of three phase detection algorithms (Endpoint-corrected Hilbert Transform, Hilbert Transform, and Phase Mapping) on three real-world biological signals with distinct spectral properties (rodent hippocampal theta potential, human EEG alpha, and human essential tremor) to identify the optima model and parameters. We then validated the algorithm performance for estimating theta phase in real-time using rats implanted with electrodes in the hippocampus. Results First, we found that signal amplitude and frequency variations strongly influence algorithm performance. Frequency-specific SNR was positively correlated with performance (mean R2 = 0.42 across metrics), while amplitude and frequency variability were negatively correlated (mean R2 = 0.50 across metrics). Second, we showed that the size of the data window used for phase estimation was the key parameter for optimal performance of FFT-based algorithms, where the optimal data window size corresponds to the period of the oscillation (~150 ms for hippocampal theta oscillations, ~100 ms for human EEG alpha, and ~125 ms for essential tremor). This data window length was validated in vivo for estimating the phase of theta oscillations from the hippocampus of freely behaving rats, where an input window size of one theta cycle yielded the best performance across all metrics compared with shorter or longer window sizes. .

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