EEG Quantum Potential Biomarkers for Objective Neuropsychiatric Diagnosis
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Solution Overview
Problem
Current diagnostic methods for neuro-psychiatric disorders lack objective, repeatable, and automated processes for identifying biomarkers, leading to delayed diagnoses and ineffective treatments due to reliance on subjective assessments and the absence of reliable biomarkers.
Innovation Solution
A system and method using EEG data to compute a quantum potential value through clustering and p-adic representation of EEG datasets, allowing for the diagnosis of neuro-psychiatric disorders by comparing the quantum potential to a threshold to distinguish between the presence and absence of medical states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If subjective assessment methods are used for diagnosing neuro-psychiatric disorders, then the diagnostic process is simple and accessible, but the reliability and objectivity of diagnosis deteriorates
Solution Approach 1:
The patent replaces subjective mechanical assessment with an automated computational system that processes EEG data through clustering algorithms and p-adic quantum potential calculations, eliminating human subjectivity while maintaining diagnostic reliability
Solution Approach 2:
The patent introduces EEG data as an intermediary objective measure between the patient's neurological state and the diagnostic conclusion, using computational algorithms as intermediaries to process this data and generate reliable diagnoses
2Measurement precision
If automated computational methods are used to process EEG data, then diagnostic objectivity and reliability improve, but the complexity of the diagnostic system increases
Solution Approach 1:
The patent segments the complex EEG data processing into distinct stages: data collection, clustering analysis, p-adic representation computation, and quantum potential extraction, making the complex system manageable and systematic
Solution Approach 2:
The patent transforms EEG data through mathematical parameter changes including p-adic number theory transformations and quantum potential calculations, converting raw neural signals into diagnostic biomarkers with enhanced precision
3Loss of time
If traditional diagnostic methods are used, then the system remains simple and accessible, but early detection capability deteriorates
Solution Approach 1:
The patent enables preliminary detection of neuro-psychiatric disorders by analyzing EEG patterns before clinical symptoms fully manifest, using computational algorithms to identify early biomarkers that traditional methods miss
Solution Approach 2:
The patent establishes continuous monitoring of EEG data to detect subtle changes in neural patterns that indicate early disease onset, maintaining constant surveillance rather than relying on intermittent traditional assessments
Data Source
AI summary
There is provided a computer implemented method of diagnosing a medical state associated with a neuro-psychiatric disorder in a subject, comprising: receiving a plurality of EEG datasets, each respective EEG dataset from a respective EEG electrode of a plurality of EEG electrodes monitoring a head of the subject, clustering the plurality of EEG datasets into a plurality of clusters, computing a p-adic representation of the plurality of clusters, extracting a quantum potential value from p-adic representation of the plurality of clusters, and diagnosing the medical state associated with the neuro-psychiatric disorder according to the quantum potential relative to a threshold that separates between presence of the medical state and non-presence of the medical state.