Machine Learning Tinnitus Detection for Personalized Treatment
Solution Overview
Problem
Current treatments for tinnitus are limited due to the subjective nature of the condition, making it challenging to diagnose and provide effective treatments.
Innovation Solution
A method and apparatus that utilize machine learning classifiers to analyze patient data based on stimuli, determining an indication of tinnitus and administering appropriate treatments such as electrical stimulation, transcranial magnetic stimulation, or pharmacologic therapy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional subjective assessment methods are used for tinnitus diagnosis, then the diagnostic process is simple, but the measurement precision and reliability are insufficient
Solution Approach 1:
The patent introduces an intermediary machine learning classifier that processes objective physiological signals (EEG, ECG, EMG, GSR) to detect tinnitus. This intermediary layer transforms complex physiological data into reliable diagnostic indicators, achieving high measurement precision without requiring direct complex intervention in the diagnostic process.
Solution Approach 2:
The patent replaces traditional subjective mechanical assessment methods with automated machine learning-based analysis of physiological signals. This substitution enables objective, precise tinnitus detection through computational processing of electrical and physiological data, eliminating reliance on subjective patient reporting.
2Reliability
If personalized treatment plans are developed based on detailed patient analysis, then treatment effectiveness is improved, but the time and complexity of treatment planning increases
Solution Approach 1:
The patent performs preliminary classification of patient profiles and tinnitus characteristics through machine learning analysis before treatment planning. By pre-processing and categorizing patient data using physiological signals, the system prepares personalized treatment recommendations in advance, reducing the time required for actual treatment planning while maintaining high effectiveness.
Solution Approach 2:
The patent changes the parameters used for treatment planning from subjective patient reports to objective physiological signal parameters (EEG frequencies, ECG patterns, EMG activity, GSR levels). This parameter transformation enables rapid, data-driven personalization of treatment plans, improving effectiveness without increasing planning time.
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 approach allows for objective detection and classification of tinnitus, enabling personalized treatment plans that can provide relief to patients with subjective tinnitus.
Implementation Method 1
The electrode may be configured to receive potentials based on a stimulus
Data Source
AI summary
Disclosed is a method. The method may include receiving first data based on a stimulus defined according to a patient. The method may include receiving second data based on the stimulus. The method may include determining an indication of tinnitus. The indication of tinnitus may be based on a comparison between the first data and the second data. The method may include administering a tinnitus treatment on the patient. The treatment may be based on the indication. A tinnitus treatment may include one or more of an biological feedback with the patient, transcranial magnetic stimulation of the patient, surgical insertion of the cochlear implant, the cognitive behavioral therapy, the transcutaneous electrical stimulation, pharmacologic therapy on the patient, or applying a hearing aid to the patient.