BioSPPy: A Python toolbox for physiological signal processing
Patrícia Bota,
Rafael Silva,
Carlos Carreiras,
Ana Fred,
Hugo Plácido da Silva
Affiliations
Patrícia Bota
Instituto Superior Técnico, Department of Bioengineering, Universidade de Lisboa, Lisboa, Portugal; Instituto de Telecomunicações, Lisboa, Portugal; Corresponding author at: Instituto Superior Técnico, Department of Bioengineering, Universidade de Lisboa, Lisboa, Portugal.
Rafael Silva
Instituto Superior Técnico, Department of Bioengineering, Universidade de Lisboa, Lisboa, Portugal; Instituto de Telecomunicações, Lisboa, Portugal
Carlos Carreiras
CardioID - Technologies, Lda., Lisboa, Portugal
Ana Fred
Instituto Superior Técnico, Department of Bioengineering, Universidade de Lisboa, Lisboa, Portugal; Instituto de Telecomunicações, Lisboa, Portugal
Hugo Plácido da Silva
Instituto Superior Técnico, Department of Bioengineering, Universidade de Lisboa, Lisboa, Portugal; Instituto de Telecomunicações, Lisboa, Portugal; Lisbon Unit for Learning and Intelligent Systems (LUMLIS), a unit of the European Laboratory for Learning and Intelligent Systems (ELLIS), Lisboa, Portugal
In recent years, the rise of data collection systems featuring physiological sensors has enabled the creation of vast datasets for various biomedical uses, enhancing wellness and quality of life applications. However, data is often available in raw form and noisy, demanding extensive pre-processing to be application-ready. To facilitate such tasks we introduce BioSPPy, a comprehensive open-source Python toolbox designed to facilitate end-to-end physiological data processing, aggregating functions ranging from data loading, to noise filtering and feature extraction. With a user-friendly semantic keyword-based input/output system, it is tailored for all Python expertise levels. As a testament to its impact and significance, to date the BioSPPy code repository has over 430k downloads and more than 470 citations on Google Scholar.
