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Raspberry Pi-Based Data Archival System for Electroencephalogram Signals From the SedLine Root Device.
Pradyumna B Suresha1, Chad J Robichaux2, Tuan Z Cassim3
1From the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia.
Anesthesia and Analgesia
|October 21, 2021
Summary
A Raspberry Pi system automates electroencephalogram (EEG) archiving from surgeries, creating a large, high-quality database with minimal human effort. This enables retrospective analysis, revealing a correlation between alpha power and patient age.
Area of Science:
- Medical Informatics
- Neuroscience
- Biomedical Engineering
Background:
- Retrospective analysis of electroencephalogram (EEG) signals during general anesthesia is vital for understanding brain states.
- Manual database creation for such EEG data is labor-intensive and inefficient.
- A need exists for automated systems to archive EEG signals from operating rooms.
Purpose of the Study:
- To develop and implement a low-cost, Raspberry Pi-based system for automated EEG signal archiving.
- To create a large-scale, HIPAA-compliant EEG database with minimal human involvement.
- To facilitate retrospective analysis of EEG data combined with patient medical records.
Main Methods:
- Developed a Raspberry Pi software package to archive EEG signals from a SedLine Root EEG Monitor to secure cloud storage.
- Collected EEG data from over 500 surgeries, archiving associated operating room numbers.
- Processed archived EEG signals, performed quality checks, and developed a formula for signal proportion calculation.
Main Results:
- Successfully archived EEG signals from over 500 surgeries over 18 months.
- Demonstrated a statistically significant negative correlation between relative alpha power (8-12 Hz) and patient age.
- Confirmed good quality of captured EEG signals suitable for retrospective analysis.
Conclusions:
- The developed system enables large-scale EEG database creation with minimal human intervention.
- The system utilizes low-cost, readily available hardware, making it accessible.
- The project's open-source software encourages wider adoption and contribution to EEG data archiving.
