Heart and Brain Waveform Analysis for Objective Mental State Detection
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Solution Overview
Problem
Clinical diagnosis of mental states is subjective and varies between clinicians, lacking objective characterization and efficient, widespread testing methods.
Innovation Solution
A system that analyzes heart and brain waveforms to derive biometric parameters, using computational models to objectively classify mental states, providing a more efficient and widespread diagnostic tool.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If clinical assessment and opinion based on interviews and questionnaires are used to diagnose mental states, then diagnostic capability is provided, but subjectivity and variability between clinicians increase
Solution Approach 1:
The patent replaces the mechanical system of human clinical assessment with an automated computational system that processes biometric data. Machine learning models analyze physiological signals (heart rate variability, brain waves, skin conductance) to objectively determine mental states, eliminating human subjectivity while providing quantifiable, reproducible diagnostic results.
Solution Approach 2:
The patent introduces biometric parameters as an intermediary between the patient's physiological state and the diagnostic conclusion. These intermediate measurements (heart rate variability, brain wave patterns, skin conductance levels) serve as objective mediators that translate physical physiological data into mental state classifications, bridging the gap between biology and psychology.
2Measurement precision
If individual-by-individual assessment by clinicians is performed, then accurate mental state determination is achieved, but cost and time consumption increase
Solution Approach 1:
The patent enables the diagnostic system to serve itself by using automated data collection and analysis. The system automatically collects biometric data through sensors, processes the data through pre-trained machine learning models, and generates diagnostic results without requiring continuous human intervention. This self-service capability maintains diagnostic accuracy while dramatically improving throughput and reducing costs.
Solution Approach 2:
The patent performs preliminary actions by pre-training computational models on large datasets of biometric measurements and corresponding mental states. These pre-trained models are then ready to rapidly assess new patients without requiring clinicians to perform time-consuming manual assessments. The preliminary computational work enables fast, scalable diagnosis while maintaining consistency across all assessments.
3Measurement precision
If comprehensive biometric analysis is performed to objectively characterize mental states, then diagnostic accuracy is improved, but measurement and analysis complexity increase
Solution Approach 1:
The patent segments the complex task of mental state diagnosis into multiple independent biometric measurements. Instead of attempting to measure mental state directly, the system separately measures heart rate variability, brain wave patterns, and skin conductance levels, then combines these segmented measurements through computational models to arrive at the final diagnosis. This segmentation makes the overall measurement process more manageable and objective.
Solution Approach 2:
The patent transforms complex physiological signals into simplified quantitative parameters that can be processed by computational models. Heart rate variability is converted from raw ECG signals into time-domain and frequency-domain parameters; brain waves are transformed from continuous EEG signals into spectral power measurements in different frequency bands. These parameter changes enable objective, quantifiable analysis while reducing measurement complexity.
Data Source
AI summary
In general, the subject matter described in this disclosure can be embodied in methods, systems, and program products for identifying a value of a heart rate variability metric that indicates a variation in a heart waveform of a patient; identifying a value of a brain activity metric that indicates a type of electrical activity represented by a brain waveform of the patient; providing values for a collection of metrics to a computational model, the values for the collection of metrics including the value for the heart rate variability metric and the value for the brain activity metric; and receiving, from the computational model as a result of having provided the values for the collection of metrics to the computational model, an indication of mental state of the patient.