Abstract
Problem: A number of technologies has been developed aiming at improving the availability, opportunity, difficulty of access or efficiency of epilepsy diagnosis based on Electroencephalogram (EEG) data. However, these approaches are not all based on open technologies, neither are they integrated into Electronic Health Record information (EHR) systems to support continuity of care. Objective: To develop an open source EHR system for the management of patient’s information, encounter scheduling, remote registration, and subsequent analysis of EEG data. Methods: The analysis, design, and implementation of the system followed the Scrum framework. The implementation was based on an open source platform for EHR systems named OpenMRS. Results: NeuroEHR supports the provision of Tele-EEG services, integrates patient’s clinical information, and EEG data captured remotely from an EEG device, stores the data in an EEG repository, and allows a neurologist to provide a diagnosis based on clinical and EEG data. Conclusions: The NeuroEHR system is currently being used in the context of the NeuroMoTIC project, in which a pediatric EEG data set is being created and annotated, and some Artificial Intelligence algorithms are being tested to support a telehealth service for the diagnosis of epilepsy.
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Notes
- 1.
Computer Software developed by Neurovirtual, which is distributed with the BWII-EEG device.
- 2.
FDA certified Medical device for the recording of EEG signals.
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Acknowledgements
The work is funded by a grant from the Colombian Agency for Science, Technology, and Innovation Colciencias – under Call 715- 2015- “Convocatoria para Proyectos de Investigación y Desarrollo en Ingenierias”, project: “NeuroMoTIC: Sistema móvil para el Apoyo Diagnóstico de la Epilepsia”, Contract number FP44842-154-2016.
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Molina, E., Salazar-Cabrera, R., López, D.M. (2018). NeuroEHR: Open Source Telehealth System for the Management of Clinical Data, EEG and Remote Diagnosis of Epilepsy. In: Figueroa-García, J., López-Santana, E., Rodriguez-Molano, J. (eds) Applied Computer Sciences in Engineering. WEA 2018. Communications in Computer and Information Science, vol 915. Springer, Cham. https://doi.org/10.1007/978-3-030-00350-0_35
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