Electrophysiological signal analysis and visualization using Cloudwave for epilepsy clinical research.

Area of Science:

• Neurology
• Biomedical Informatics
• Data Science

Background:

• Epilepsy is a common neurological disorder affecting millions globally.
• Electrophysiological recordings like electroencephalogram (EEG) are crucial for epilepsy diagnosis and pre-surgical evaluation.
• Traditional desktop applications hinder real-time, collaborative analysis needed for multi-center studies.

Purpose of the Study:

• To develop a scalable, web-based platform for electrophysiology data visualization and analysis.
• To support real-time, collaborative signal data interaction in multi-center epilepsy research.
• To integrate with existing tools for patient cohort identification and data management.

Main Methods:

• Developed Cloudwave, a web-based platform utilizing scalable open-source cloud computing.
• Integrated Cloudwave with MEDCIS (Multi-modality Epilepsy Data Capture and Integration System) for patient cohort identification.
• Utilized the Epilepsy and Seizure Ontology (EpSO) to underpin Cloudwave and MEDCIS for query composition and retrieval.

Main Results:

• Cloudwave provides a web-based solution for electrophysiology data visualization and analysis.
• The platform supports real-time, collaborative interaction with signal data.
• Integration with MEDCIS and EpSO enhances data management and retrieval capabilities.

Conclusions:

• Cloudwave addresses the limitations of traditional desktop applications for multi-center epilepsy studies.
• The platform facilitates collaborative research and improves data analysis in epilepsy monitoring units.
• Cloudwave is being deployed across multiple epilepsy monitoring units as part of the PRISM project.