  Polysomnography (PSG) data is recorded and stored in various formats
depending on the recording software. Although the PSG data can usually be
exported to open formats, such as the European Data Format (EDF), they are
limited in data types, validation, and readability. Moreover, the exported data
is not harmonized, which means different datasets need customized preprocessing
to conduct research on multiple datasets. In this work, we designed and
implemented an open format for storage and processing of PSG data, called the
Sleeplab format (SLF), which is both human and machine-readable, and has
built-in validation of both data types and structures. SLF provides tools for
reading, writing, and compression of the PSG datasets. In addition, SLF
promotes harmonization of data from different sources, which reduces the amount
of work needed to apply the same analytics pipelines to different datasets. SLF
is interoperable as it utilizes the file system and commonly used file formats
to store the data. The goal of developing SLF was to enable fast exploration
and experimentation on PSG data, and to streamline the workflow of building
analytics and machine learning applications that combine PSG data from multiple
sources. The performance of SLF was tested with two open datasets of different
formats (EDF and HDF5). SLF is fully open source and available at
https://github.com/UEF-SmartSleepLab/sleeplab-format.
