Medical Waveform Format Encoding Rules Representation of Neurointensive Care Waveform Data

Area of Science:

• Biomedical Engineering
• Health Informatics
• Critical Care Medicine

Background:

• Neurointensive care units generate large volumes of complex patient data.
• Existing data standards are insufficient for high-frequency waveform data in neurocritical care.
• The CHART-ADAPT project aims to develop bedside technology for big data analysis in neurocritical care.

Purpose of the Study:

• To investigate the suitability of the Medical Waveform Format Encoding Rules (MFER) as a data standard for neurointensive care waveform data.
• To address the lack of an agreed data standard for high-frequency physiological signals in neurocritical care.

Main Methods:

• Explored and evaluated various waveform formats (XML, DICOM, MFER) based on data size and standard recognition.
• Assessed MFER's compatibility with existing computing infrastructure (NHS network, integration software).
• Converted seven high-frequency waveform signals (ICP, ECG, ART, CVP, EtCO2, Pleth, Resp) from an 8-hour patient recording into MFER format.

Main Results:

• The MFER format met key requirements for compact data size and recognized standard.
• All seven waveform signals were automatically and successfully converted into MFER format.
• Generated 479 MFER files, each representing one minute of data (181 KB per file).

Conclusions:

• MFER demonstrates potential as a lightweight standard for high-frequency neurointensive care waveform data.
• Further research will involve comparing MFER with other formats and conducting live trials for data streaming.