Multicenter data sharing for collaboration in sleep medicine

Sleep is a fundamental biological process crucial for survival and health of (not only) humans. But many circumstances like physiological and mental disorders, environment and lifestyle may affect healthy sleep. To date, 88 different sleep disorders are internationally recognized. They cover a broad field of medical areas. Analysis of human sleep is typically based on multidimensional biosignal recordings, so called polysomnographies (PSG). Therefore research often includes digital signal processing. Clinical sleep research is an inherent multidisciplinary field. Inter-institutional and interdisciplinary collaborations are required to address the complexity of sleep regulation and disturbance. But to date, collaborative sleep research is poorly supported by IT systems. In particular, the management and processing of PSGs is challenging. A large variety of PSG devices, data formats, measurement procedures and quality variations impedes consistent biosignal data processing. In this manuscript we introduce a virtual research platform supporting inter-institutional data sharing and processing. The infrastructure is based on XNAT—a free and open source neuroimaging research platform, a loosely coupled service oriented architecture and scalable virtualization in the back end. The system is capable of local pseudonymization of biosignal data, mapping to a standardized set of parameters and automatic quality assessment. Terms and quality measures are derived from the “Manual for the Scoring of Sleep and Associated Events” of the American Academy of Sleep Medicine (AASM), the de facto standard for diagnostic biosignal analysis in sleep medicine.