FIBSI

FIBSI detects and quantifies biologically meaningful signals in non-stationary, noisy time series data to enable unbiased analysis of biological recordings such as electrophysiology and fluorescence imaging.

Key Features:

• Non-Stationary Signal Analysis: Tailored for non-linear, dynamic, and stochastic experimental data where signals are irregular and embedded in noise.
• Dimensional Property Quantification: Uses a signal detection algorithm implemented in Python to quantify dimensional properties of waveform deviations from baseline via a running fit function.
• Frequency-Independent / Unbiased Analysis: Operates without assuming periodicity and can identify signals without requiring filters or transformations, preserving the raw form of biological activity.

Scientific Applications:

• Electrophysiological Recordings: Applied to in vitro whole-cell current-clamp recordings of rodent sensory neurons (nociceptors) to distinguish depolarizing membrane-potential fluctuations from noise and reveal species-specific differences between naïve mice and rats.
• Fluorescence Imaging: Applied to in vivo fluorescence time-lapse movies of gastrointestinal motility in larval zebrafish to identify muscle contractions, compare peristalsis frequencies between unfed and fed larvae, and differentiate peristaltic movements from oscillatory sphincter-like activities across foregut, midgut, and cloaca.

Methodology:

Signal detection algorithm implemented in Python that quantifies waveform deviations from baseline using a running fit function and identifies biological signals without filters or transformations, operating agnostically to data structure and periodicity.