Extracting biological signals from non-linear, dynamic and stochastic experimental data can be challenging, especially when the signal is non-stationary. Many currently available methods make assumptions about the data structure (e.g., signal is periodic, sufficient recording time) and modify the raw data in pre-processing using filters and/or transformations. With an agnostic approach to biological data analysis as a goal, we implemented a signal detection algorithm in Python that quantifies the dimensional properties of waveform deviations from baseline via a running fit function. We call the resulting free program frequency-independent biological signal identification (FIBSI). We demonstrate the utility of FIBSI on two disparate types of experimental data: in vitro whole-cell current-clamp electrophysiological recordings of rodent sensory neurons (i.e., nociceptors) and in vivo fluorescence image time-lapse movies capturing gastrointestinal motility in larval zebrafish. In rodent nociceptors, depolarizing fluctuations in membrane potential are irregular in shape and difficult to distinguish from noise. Using FIBSI, we determined that nociceptors from naïve mice generate larger, more frequent fluctuations compared to naïve rats, suggesting species-specific specializations in rodent nociceptors. In zebrafish, measuring gut motility is a useful tool for addressing developmental and disease-related mechanisms associated with gut function. However, available methods are laborious, technically complex, and/or not cost-effective. We developed and tested a novel assay that can characterize intestinal peristalsis using imaging time series datasets. We used FIBSI to identify muscle contractions in the fluorescence signals and compared their frequencies in unfed and fed larvae. Additionally, FIBSI allowed us to discriminate between peristalsis and oscillatory sphincter-like movements in functionally distinct gut segments (foregut, midgut, and cloaca). We conclude that FIBSI, which is freely available via GitHub, is widely useful for the unbiased analysis of non-stationary signals and extraction of biologically meaningful information from experimental time series data and can be employed for both descriptive and hypothesis-driven investigations.

中文翻译：

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与频率无关的生物信号识别（FIBSI）：这是一个免费程序，可简化对非平稳时间序列数据的深入分析

从非线性，动态和随机实验数据中提取生物信号可能具有挑战性，尤其是在信号不稳定的情况下。许多当前可用的方法对数据结构进行假设（例如，信号是周期性的，足够的记录时间），并在预处理中使用过滤器和/或变换来修改原始数据。以一种不可知论的生物数据分析方法为目标，我们在Python中实现了一种信号检测算法，该算法通过运行拟合函数来量化与基线之间的波形偏差的维数属性。我们称结果为自由程序频率无关的生物信号识别（FIBSI）。我们在两种不同类型的实验数据上展示了FIBSI的效用：啮齿动物感觉神经元（即伤害感受器）的体外全细胞电流钳电生理记录，以及捕获幼虫斑马鱼胃肠蠕动的体内荧光图像延时电影。在啮齿动物伤害感受器中，膜电位的去极化波动形状不规则，难以与噪声区分开。使用FIBSI，我们确定与未加工大鼠相比，来自未加工小鼠的伤害感受器产生更大，更频繁的波动，这表明啮齿动物伤害感受器具有物种特异性。在斑马鱼中，测量肠蠕动是解决与肠功能相关的发育和疾病相关机制的有用工具。然而，可用的方法费力，技术上复杂和/或不具有成本效益。我们开发并测试了一种可以使用成像时间序列数据集表征肠道蠕动的新颖测定方法。我们使用FIBSI识别荧光信号中的肌肉收缩，并比较了未喂养和喂养幼虫的肌肉收缩频率。此外，FIBSI允许我们区分功能不同的肠段（前肠，中肠和泄殖腔）的蠕动和括约肌振荡运动。我们得出的结论是，可通过GitHub免费获得的FIBSI可广泛用于非平稳信号的无偏分析以及从实验时间序列数据中提取生物学上有意义的信息，并且可用于描述性研究和假设驱动的研究。我们使用FIBSI识别荧光信号中的肌肉收缩，并比较了未喂养和喂养幼虫的肌肉收缩频率。此外，FIBSI允许我们区分功能不同的肠段（前肠，中肠和泄殖腔）的蠕动和括约肌振荡运动。我们得出的结论是，可通过GitHub免费获得的FIBSI可广泛用于非平稳信号的无偏分析以及从实验时间序列数据中提取生物学上有意义的信息，并且可用于描述性研究和假设驱动的研究。我们使用FIBSI识别荧光信号中的肌肉收缩，并比较了未喂养和喂养幼虫的肌肉收缩频率。此外，FIBSI允许我们区分功能不同的肠段（前肠，中肠和泄殖腔）的蠕动和括约肌振荡运动。我们得出的结论是，可通过GitHub免费获得的FIBSI可广泛用于非平稳信号的无偏分析以及从实验时间序列数据中提取生物学上有意义的信息，并且可用于描述性研究和假设驱动的研究。