Departments of Biomedical Engineering, Parkville, Victoria 3010, Australia.
Florey Institute of Neuroscience and Mental Health, Parkville, Victoria 3052, Australia.
J Neurosci Methods. 2020 May 15;338:108683. doi: 10.1016/j.jneumeth.2020.108683. Epub 2020 Mar 19.
Peripheral autonomic nerves control visceral organs and convey information regarding their functional states and are, therefore, potential targets for new therapeutic and diagnostic approaches. Conventionally recorded multi-unit nerve activity in vivo undergoes slow differential drift of signal and noise amplitudes, making accurate monitoring of nerve activity for more than tens of minutes problematic.
We describe an on-line drift compensation algorithm that utilizes recursive least-squares to estimate the relative change in spike amplitude due to changes in the nerve-electrode interface over time.
We tested and refined our approach using simulated data and in vivo recordings from nerves supplying the small intestine under control conditions and in response to gut inflammation over several hours. The algorithm is robust to changes in recording conditions and signal-to-noise ratio and applicable to both single and multi-unit recordings. In uncompensated records, drift prevented "spike families" and single units from being discriminated accurately over hours. After rescaling, these were successfully tracked throughout recordings (up to 3 h).
Existing methods are subjective or compensate for drift using spatial information and spike shape data which is not practical in multi-unit peripheral nerve recordings. In contrast, this method is objective and applicable to data from a single differential multi-unit recording. In comparisons using simulated data the algorithm performed as well as or better than existing methods.
Results suggest our drift compensation algorithm is widely applicable and robust, though conservative, when differentiating prolonged responses from drift in signal. Extracellular nerve recordings; drift compensation; chronic nerve recordings; closed-loop; multi-unit activity; spike discrimination; recursive least squares; real-time.
外周自主神经控制内脏器官，并传递有关其功能状态的信息，因此是新的治疗和诊断方法的潜在靶点。传统上在体内记录的多单位神经活动经历信号和噪声幅度的缓慢差分漂移，使得超过几十分钟准确监测神经活动成为问题。
我们描述了一种在线漂移补偿算法，该算法利用递归最小二乘法估计由于神经-电极界面随时间变化而导致的尖峰幅度的相对变化。
我们使用模拟数据和在控制条件下以及在数小时内对供应小肠的神经进行的体内记录测试和改进了我们的方法。该算法对记录条件和信噪比的变化具有鲁棒性，适用于单单位和多单位记录。在未补偿的记录中，漂移阻止了“尖峰家族”和单个单位在数小时内准确区分。在重新缩放后，这些在整个记录过程中（长达 3 小时）都可以成功跟踪。
现有方法是主观的，或者使用空间信息和尖峰形状数据来补偿漂移，这在多单位外周神经记录中是不实际的。相比之下，该方法是客观的，适用于来自单个差分多单位记录的数据。在使用模拟数据进行的比较中，该算法的性能与现有方法一样好，或者更好。
结果表明，我们的漂移补偿算法在信号漂移中长时间区分反应时具有广泛的适用性和稳健性，尽管保守。
细胞外神经记录；漂移补偿；慢性神经记录；闭环；多单位活动；尖峰鉴别；递归最小二乘法；实时。
