Raspberry Pi-Based Data Archival System for Electroencephalogram Signals From the SedLine Root Device

Abstract

BACKGROUND:The retrospective analysis of electroencephalogram (EEG) signals acquired from patients under general anesthesia is crucial in understanding the patient's unconscious brain's state. However, the creation of such database is often tedious and cumbersome and involves human labor. Hence, we developed a Raspberry Pi-based system for archiving EEG signals recorded from patients under anesthesia in operating rooms (ORs) with minimal human involvement. METHODS: Using this system, we archived patient EEG signals from over 500 unique surgeries at the Emory University Orthopaedics and Spine Hospital, Atlanta, for about 18 months. For this, we developed a software package that runs on a Raspberry Pi and archives patient EEG signals from a SedLine Root EEG Monitor (Masimo) to a secure Health Insurance Portability and Accountability Act (HIPAA) compliant cloud storage. The OR number corresponding to each surgery was archived along with the EEG signal to facilitate retrospective EEG analysis. We retrospectively processed the archived EEG signals and performed signal quality checks. We also proposed a formula to compute the proportion of true EEG signal and calculated the corresponding statistics. Further, we curated and interleaved patient medical record information with the corresponding EEG signals. RESULTS:We retrospectively processed the EEG signals to demonstrate a statistically significant negative correlation between the relative alpha power (8-12 Hz) of the EEG signal captured under anesthesia and the patient's age. CONCLUSIONS: Our system is a standalone EEG archiver developed using low cost and readily available hardware. We demonstrated that one could create a large-scale EEG database with minimal human involvement. Moreover, we showed that the captured EEG signal is of good quality for retrospective analysis and combined the EEG signal with the patient medical records. This project's software has been released under an open-source license to enable others to use and contribute. (Anesth Analg 2022;134:380-8) KEY POINTS• Question: Is it possible to automate the process of archiving patient electroencephalogram (EEG) signal from a Sedline Root monitor and interleave patient medical information with the EEG recordings to do retrospective EEG analysis? • Findings: The EEG archiving process can be semiautomated by utilizing a Raspberry Pi-based data archival system, and the patient medical information can be interleaved with the EEG recordings using the operating room (OR) number of the captured EEG records. • Meaning: With this system, we can create large database (>500 patients) of EEG recordings with associated patient medical records with minimal human labor for retrospective analysis.

An Edge Computing and Ambient Data Capture System for Clinical and Home Environments

“…The implementation on a state-of-the-art extensible edge computing system at a relatively low cost provides a high degree of flexibility in the design. The bill-of-materials and open-source code to replicate the work described here have been made publicly available under an open-source license [73].…”

“…A picture illustrating the hardware components of the system is shown in Figure 1. The bill-of-materials, along with the total cost for the hardware components and the system dimensions, are provided in our open-sourced Github repository [73]. We now describe the system's individual hardware components along with the associated software.…”