Physiological research relies on accurate data extraction from recording systems such as LabChart. While these systems provide essential tools for data collection, processing large and complex datasets can remain time consuming and often requires manual validation of breaths or cardiac cycles. Manual validation introduces variability due to human judgment, fatigue, and the inability to reprocess large datasets efficiently. To streamline this process, we present PhysioMerge, an automated tool for robust physiological data extraction. PhysioMerge allows users to implement customizable checks to validate signals against predefined criteria, flag anomalies, and preserve valid segments, reducing the need for subjective manual assessment. The software also supports advanced waveform morphology analysis, extracting features such as peak prominence, curvature, and slopes that complement conventional analyses. Flexible data segmentation by time, intervention, or study phase, combined with batch processing capabilities, allows researchers to efficiently handle large datasets and perform consistent, high-precision analyses.
.
