Respiratory Event Detection With Overlapping CNN Voting
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Solution Overview
Problem
Current methods for diagnosing sleep apnea, such as polysomnography and automated systems, are time-consuming, costly, and prone to inaccuracies due to variability in signal quality and patient-specific characteristics, leading to false positives and negatives.
Innovation Solution
A system integrating advanced machine learning models, particularly one-dimensional convolutional neural networks, with localized event labeling and a voting mechanism to enhance respiratory event detection by analyzing overlapping signal segments and aggregating predictions, ensuring consistent and accurate event counting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation by trained professionals is used for respiratory event detection, then detection accuracy can be maintained, but the process becomes time-consuming and costly
Solution Approach 1:
The patent uses machine learning models to create automated copies of manual annotation expertise. The system trains ML models on manually annotated data to replicate the detection capabilities of trained professionals, thereby maintaining accuracy while eliminating the time-consuming manual process.
Solution Approach 2:
The patent replaces the mechanical process of manual annotation with automated machine learning systems. The ML models process physiological signals automatically, substituting human expert judgment with computational algorithms that can operate continuously without fatigue.
2Productivity
If threshold-based automated systems are used for respiratory event detection, then processing speed is improved, but detection accuracy deteriorates due to inability to capture signal complexity
Solution Approach 1:
The patent transitions from fixed threshold parameters to adaptive machine learning models that learn from data patterns. The ML models adjust their detection criteria based on the complexity and variability of physiological signals, maintaining both speed and accuracy.
Solution Approach 2:
The patent represents a phase transition from simple threshold-based detection to sophisticated machine learning-based detection. This phase change enables the system to handle complex, non-linear signal patterns while maintaining automated processing speed.
3Productivity
If automated detection systems are used, then processing efficiency is improved, but reliability deteriorates due to false positives and false negatives from signal variability and artifacts
Solution Approach 1:
The patent incorporates feedback mechanisms where the system continuously learns from annotated data to improve its detection accuracy. The ML models are trained on labeled examples and can adjust their parameters to reduce false positives and false negatives, thereby improving reliability while maintaining efficiency.
Solution Approach 2:
The patent performs preliminary training of machine learning models on annotated data before deployment. This preliminary action ensures the models are pre-calibrated to handle signal variability and artifacts, improving reliability from the outset while maintaining automated processing efficiency.
4Measurement precision
If machine learning models are applied to respiratory event detection, then detection accuracy can be improved, but system complexity increases
Solution Approach 1:
The patent segments the complex machine learning system into manageable components: data collection modules, feature extraction modules, classification modules, and evaluation modules. This segmentation reduces overall system complexity by making each component independent and easier to develop, test, and maintain.
Solution Approach 2:
The patent designs the machine learning system to perform multiple functions: detecting respiratory events, classifying event types, and providing diagnostic support. This multi-functionality consolidates what would otherwise require multiple separate systems into a single unified platform, reducing overall complexity.
Data Source
AI summary
The present invention relates to a system and method for the enhanced detection and quantification of respiratory events, such as apneas and hypopneas, particularly useful in diagnosing and managing sleep-disordered breathing conditions. This system integrates sophisticated machine learning algorithms, specifically one-dimensional convolutional neural networks (1D CNNs), with advanced signal processing techniques to analyze physiological signals. It focuses on the detecting respiratory event with a localized portion of the input segment, a novel approach that increases specificity in event detection. The system segments physiological signals into overlapping segments, each analyzed by the machine learning model to generate a prediction score. A unique aspect of this invention is the application of a dual-threshold mechanism: a ‘Model threshold’ for initial event identification and a ‘Vote threshold’ for confirming events through an aggregate voting process of overlapping segment predictions. This innovative approach ensures high accuracy and reliability in event detection and counting.