Brainwave Attractor Betti Number Delirium Detection
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Solution Overview
Problem
Current encephalopathy detection techniques, particularly for delirium, face challenges in accuracy due to variable frequency components and noise interference in brainwave data, leading to erroneous or missed detections.
Innovation Solution
A non-transitory computer-readable recording medium stores an encephalopathy determination program that generates attractors from brainwave data using topological data analysis (TDA) and applies persistent homology transform to calculate Betti numbers, enabling accurate delirium detection by analyzing first-order components in the Betti sequence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If frequency analysis or spectrum analysis is used to detect encephalopathy, then detection capability is provided, but detection accuracy deteriorates due to variable frequency components and noise interference
Solution Approach 1:
The patent transforms the analysis parameters from frequency domain characteristics to topological features (Betti numbers) that capture the structural properties of brainwave attractors. This parameter transformation allows the system to detect encephalopathy based on topological invariants that are insensitive to frequency variations and noise, thereby resolving the contradiction between detection capability and detection accuracy
Solution Approach 2:
The patent introduces topological data analysis (TDA) and persistent homology as intermediary methods between raw brainwave data and encephalopathy detection. By using Betti numbers as intermediate topological features, the system bridges the gap between complex brainwave signals and reliable detection outcomes, filtering out noise and frequency variability through topological transformation
2Device complexity
If traditional frequency analysis methods are used, then analysis process is simple, but detection precision deteriorates due to waveform width fluctuations and noise
Solution Approach 1:
The patent changes the analysis parameters from traditional frequency-based metrics to topological invariants (Betti numbers). This parameter transformation maintains analytical tractability while significantly improving detection precision by focusing on topological features that are invariant to waveform distortions and noise
Solution Approach 2:
The patent transitions from analyzing brainwaves in the frequency domain to examining them in the topological dimension. By constructing attractors and computing persistent homology, the system elevates the analysis to a higher dimensional space where topological features provide more robust detection signals
Data Source
AI summary
A detection device generates plural attractors based on brainwave data. Subsequently, the detection device calculates a Betti number by subjecting the attractors to persistent homology transform. The detection device determines an onset of encephalopathy based on a first order component of a Betti sequence calculated based on the Betti number.