Ai Anesthesia Depth Index

Overview

The Ai Anesthesia Depth Index leverages EEG signal analysis to accurately quantify the depth of anesthesia. As patients transition from wakefulness to deep anesthesia, EEG signals exhibit distinct patterns—including increased δ-wave activity, burst suppression, and eventual isoelectric states.

Core PrincipleAi calculates a numerical index by analyzing EEG features across multiple dimensions: frequency domain, bispectral analysis, time-domain dynamics, and signal complexity. Multivariate statistical models map these changes to a continuous index, providing real-time, reliable assessment of anesthetic depth.

Clinical Value• Detects key EEG transitions (e.g., burst suppression, δ dominance)• Enables precise anesthesia management• Enhances patient safety and reduces risk of awareness

Technical Advantages

• Precision EEG Signal AcquisitionThe system features high input impedance, low noise (<5 µV), and >120 dB common-mode rejection. Its architecture includes electrode protection, signal conditioning, EMG/EEG filtering, and high-resolution A/D conversion—ensuring accurate EEG signal integrity.

• Robust Algorithm EngineUtilizing advanced time-domain and spectral analysis, Ai calculates key parameters including Signal Quality Index, EMG power, and Burst Suppression Ratio. Integrated with multivariate EEG data modeling, it delivers a reliable, quantitative index for anesthesia depth assessment.

• Parameter Definitions – Ai Anesthesia Monitoring System

Parameters

Description

Ai (Anesthesia Awareness Index)

A multivariate index derived from EEG signal frequency, time-domain, and complexity metrics to quantify consciousness from awake (100) to complete suppression (0). Key thresholds: 100 = awake, 70 = light sedation, 60 = general anesthesia, 40 = deep hypnosis, 0 = isoelectric EEG (no brain activity).

Ai Trend Chart

Real-time graphical representation of Ai index changes throughout the anesthesia process, reflecting sedation and hypnotic depth dynamics.

BSR (Burst Suppression Ratio)

Quantifies the percentage of time EEG shows suppression (low amplitude or flatline) interspersed with bursts— indicative of deep anesthesia.

EMG (Electromyogram)

Displays high-frequency noise and artifacts from muscle activity, which can interfere with EEG interpretation.

SQI (Signal Quality Index)

Displays high-frequency noise and artifacts from muscle activity, which can interfere with EEG interpretation.

Parameters	Description
Ai (Anesthesia Awareness Index)	A multivariate index derived from EEG signal frequency, time-domain, and complexity metrics to quantify consciousness from awake (100) to complete suppression (0). Key thresholds: 100 = awake, 70 = light sedation, 60 = general anesthesia, 40 = deep hypnosis, 0 = isoelectric EEG (no brain activity).
Ai Trend Chart	Real-time graphical representation of Ai index changes throughout the anesthesia process, reflecting sedation and hypnotic depth dynamics.
BSR (Burst Suppression Ratio)	Quantifies the percentage of time EEG shows suppression (low amplitude or flatline) interspersed with bursts— indicative of deep anesthesia.
EMG (Electromyogram)	Displays high-frequency noise and artifacts from muscle activity, which can interfere with EEG interpretation.
SQI (Signal Quality Index)	Evaluates EEG signal quality based on electrode impedance and artifact contamination to ensure data integrity.

Integrated Sensor Design

The system employs a disposable, integrated EEG sensor using a flexible PCB cable and electrode structure. It incorporates intelligent authentication technology to ensure compatibility with the host device and enforce usage time limits, maintaining signal integrity throughout application. The custom-developed conductive electrodes offer excellent conductivity and ultra- low contact impedance, ensuring high-quality EEG signal acquisition.

Robust Clinical Validation Database

The Ai algorithm has been extensively validated through numerous clinical trials, benchmarked against leading international systems such as BIS and Narcotrend. These studies demonstrate that Ai exhibits strong clinical relevance, high consistency, and reliability in assessing anesthesia depth. Statistical analysis confirms a strong correlation between Ai and BIS indices, providing robust support for its clinical application.