iNPH Prediction Model Using New/Old Stimulus BCI EEG Analysis
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Solution Overview
Problem
Current methods for diagnosing Idiopathic Normal Pressure Hydrocephalus (iNPH) are inefficient and lack consistency, leading to delayed and ineffective treatment due to the similarity of symptoms with other neurological disorders.
Innovation Solution
A method and apparatus using a new/old stimulus Brain-Computer Interface (BCI) paradigm to construct an iNPH prediction model, which involves pre- and post-Lumbar Tap Test EEG signal data analysis to identify event-related potential features, specifically P600 amplitude features, to differentiate iNPH patients from others.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional clinical tests (Lumbar Tap Test, Infusion test, External Lumbar Drainage, ICP monitoring) are used for iNPH diagnosis, then diagnostic capability is provided, but lack of consistency and clear diagnostic parameters leads to inefficient diagnosis and delayed treatment
Solution Approach 1:
The patent transforms the diagnostic approach by changing from traditional clinical test parameters to EEG signal parameters. Specifically, it uses event-related potential features (P600 amplitude) extracted from EEG signals obtained during new/old stimulus BCI paradigm experiments. This parameter transformation enables objective, consistent measurement of cognitive function changes that can be quantitatively analyzed to diagnose iNPH.
Solution Approach 2:
The patent replaces the mechanical/physical clinical tests (Lumbar Tap Test, ICP monitoring) with an electrophysiological measurement system. By using EEG signals and brain-computer interface technology, the system substitutes direct mechanical intervention with non-invasive electrical signal measurement, providing consistent and repeatable diagnostic data without the variability inherent in manual clinical assessments.
2Reliability
If multiple specialists are consulted due to symptom diversity and differentiation difficulty, then comprehensive evaluation is achieved, but diagnostic efficiency decreases and timely treatment is delayed
Solution Approach 1:
The patent creates a universal diagnostic tool that can be applied across different patient presentations and clinical scenarios. The new/old stimulus BCI paradigm experiment combined with EEG analysis provides a single, standardized assessment method that works for diverse iNPH cases, eliminating the need for multiple specialized tests and consultations while maintaining diagnostic accuracy.
Solution Approach 2:
The patent creates a standardized diagnostic protocol that can be replicated consistently across different patients and settings. By using the same new/old stimulus BCI paradigm experiment and EEG analysis methodology for all patients, the system produces comparable results that can be directly compared against established criteria, replacing the need for multiple specialists with a reproducible, standardized assessment tool.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The proposed method enables rapid and accurate diagnosis of iNPH by capturing changes in cognitive function through EEG analysis, thereby facilitating timely and effective treatment.
Implementation Method 1
obtain pre-LTT new stimulus EEG signal data and pre-LTT old stimulus EEG signal data for the target population; obtain post-LTT new stimulus EEG signal data and post-LTT old stimulus EEG signal data for the target population
Data Source
AI summary
The current innovation pertains to the medical data processing domain and introduces a method for formulating an iNPH prediction model using a new/old stimulus BCI paradigm. The process involves conducting new/old stimulus BCI paradigm experiments on a target population both before and after the Lumbar Tap Test, obtaining electroencephalogram (EEG) signal data. After preprocessing the EEG signal data, the model extracts features related to new and old stimulus event-related potentials before and after the test, specifically focusing on P600 amplitude features. The iNPH prediction model is then trained based on these event-related potential features. This innovation facilitates quantifying cognitive function improvement, aiding physicians in prompt iNPH diagnosis and enabling timely and effective patient treatment.