Microelectrode arrays (MEAs) are widely used in cardiac electrophysiology to record extracellular field potentials from cardiomyocytes with high temporal and spatial resolution. These recordings enable the analysis of critical cardiac parameters such as conduction velocity and arrhythmic events by evaluating the electrical and morphological characteristics of electrograms (EGMs) to study cardiac function and drug effects. Typically, researchers develop their own custom programs to analyse recordings, often requiring frequent manual interventions that are time-consuming, error-prone, and lack consistency across studies.
In this paper, we present an open-source, Python-based toolkit for automated electrophysiological analysis of MEA recordings, designed to streamline data preprocessing, feature extraction, visualisation, and batch analysis. The toolkit automatically detects stimulation signals, extracts cardiac cycles with optional EGM averaging, and computes activation and repolarisation times. It extracts multiple features from EGMs based on voltage and duration, while also identifying poor-quality electrodes and atypical EGMs using configurable criteria. In addition to summarised tables of measurements, interpolation techniques are applied to generate final output maps, including Activation Maps, Conduction Maps, and Feature Maps. With a focus on reproducibility, transparency, and ease of use, this toolkit offers a robust alternative to manual processing. It enables cardiac researchers to efficiently analyse MEA data at scale, while allowing extension or adaptation of analysis modules to suit specific experimental needs.