Ensemble Model for ECG-Based Mental Stress Classification

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Solution Overview

Problem

Existing stress assessment methods, such as questionnaire-based approaches, are unreliable due to individual variations and reluctance to answer questions, making it difficult to accurately determine mental stress levels.

Innovation Solution

A mental stress classification apparatus and method using an ensemble model that extracts feature vectors from electrocardiogram (ECG) signals and classifies them using a mixture model of a support vector machine and a naive Bayes.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If questionnaire-based stress analysis is used, then the assessment process is simple and easy to implement, but the reliability and accuracy of stress determination is low due to individual variations and reluctance to answer questions

Engineering Contradiction:

Improveease of implementationVSAvoidreliability of stress assessment

Core Design Contradiction:

Ease of operationVSReliability

Solution Approach 1:

The patent replaces the mechanical/questionnaire-based stress assessment system with a physiological signal-based system using ECG signals and machine learning algorithms. This substitution eliminates the need for subjective user responses while providing objective, reliable stress level classification through automated analysis of cardiac electrical activity patterns.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Device complexity

If single machine learning model is used for stress classification, then the system complexity is low, but the classification accuracy is insufficient

Engineering Contradiction:

Improvesystem complexityVSAvoidclassification accuracy

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The patent merges multiple machine learning models (SVM, Naive Bayes, and other classifiers) into an ensemble model that combines their individual classification results. This integration allows the system to leverage the strengths of each model while compensating for their individual weaknesses, achieving superior classification accuracy for stress level determination without requiring excessive system complexity.

Inventive Principle:

Principle #5Merging (Combining)

Data Source

PatentUS20250057454A1Mental stress classification apparatus and method using ensemble model

Publication Date: 2025.02.20 IND ACADEMIC COOP FOUND YONSEI UNIV

AI summary

A mental stress classification apparatus using an ensemble model includes a processor and a storage medium on which one or more programs configured to be executable by the processor are recorded. The processor is configured to extract a feature vector of an electrocardiogram (ECG) signal and classify the extracted feature vector using the ensemble model, wherein the ensemble model is a mixture model of a support vector machine and a naive Bayes.