The Pattern and Image Analysis group at Instituto de Telecomunicações, Lisbon, has developed an open-source software library – BioSPPy - which has reached over 500k downloads and more than 470 citations on Google Scholar. These impressive numbers highlight its significant impact within the research community.
In recent years, the proliferation of data collection systems with physiological sensors has led to the generation of vast datasets for various biomedical applications, contributing to advancements in health and wellness technologies. However, this data is often raw and noisy, requiring substantial pre-processing before it can be effectively utilized.
To address this challenge, the group introduced BioSPPy, a comprehensive open-source Python toolbox designed for end-to-end physiological data processing. The toolbox offers a wide range of functionalities, from data loading and noise filtering to feature extraction, all within a user-friendly, semantic, keyword-based input/output system. BioSPPy is accessible to users with varying levels of Python expertise, making it a versatile tool for the broader scientific and biomedical community.
Read also: