Measuring the hypnotic depth of anaesthesia based on the EEG signal using combined wavelet transform, eigenvector and...

Area of Science:

• Neuroscience
• Biomedical Engineering
• Signal Processing

Background:

• Monitoring hypnotic depth of anaesthesia (DoA) is crucial for patient safety.
• Existing indices like the Bispectral Index (BIS) have limitations in responsiveness.
• Electroencephalogram (EEG) signals contain valuable information about brain states during anaesthesia.

Purpose of the Study:

• To develop and validate a novel index for measuring hypnotic depth of anaesthesia (DoA) using EEG signals.
• To compare the performance of the new index against the established BIS index.
• To assess the responsiveness and agreement of the new index with clinical observations.

Main Methods:

• Utilized combined Wavelet transform, eigenvector, and normalization techniques on EEG signals.
• Applied eigenvector method to create a feature function from discrete wavelet transform (DWT) coefficients.
• Optimally determined Daubechies wavelet and ranking value p for anaesthesia state identification.
• Implemented a statistical normalization process to re-scale data and compute the ZDoA index.
• Validated the ZDoA index against real anaesthetised EEG data and the BIS index.

Main Results:

• The proposed ZDoA index generally parallels the BIS index in measuring hypnotic depth.
• ZDoA demonstrated faster reaction times than BIS during transitions between consciousness and unconsciousness.
• A Bland-Altman analysis showed 95.23% agreement between the ZDoA and BIS indices.
• The ZDoA index exhibited responsive trends consistent with clinical observations.

Conclusions:

• The ZDoA index is a promising new tool for monitoring hypnotic depth of anaesthesia.
• ZDoA offers comparable and potentially improved responsiveness over the BIS index.
• The developed index shows high agreement and clinical relevance for anaesthesia monitoring.