Kernel-Based Oximeter for Automated Pathological Condition Detection
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Solution Overview
Problem
Pulse oximeters often require expert interpretation to detect pathological conditions, and there is a need for automated systems to classify oxygen saturation data accurately, especially in cases where trends indicative of respiratory distress or other conditions are not immediately apparent.
Innovation Solution
A pulse oximeter system utilizing a kernel-based classifier, trained on expert-annotated data, employs a support vector machine (SVM) to classify oxygen saturation data as normal or pathological, allowing for the detection of conditions like airway instability and other respiratory issues through a nonlinear transform of statistical parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If pulse oximetry is used to monitor physiological characteristics, then blood oxygen saturation and blood flow characteristics can be measured, but expert interpretation is still required to detect pathological conditions
Solution Approach 1:
The patent creates a virtual copy of expert physiological interpretation through a trained neural network model. The model learns from annotated training data containing expert classifications of oxygen saturation patterns, enabling automated reproduction of expert-level pathological condition detection without requiring actual human experts to continuously interpret each patient signal.
Solution Approach 2:
The patent replaces the mechanical process of human expert visual inspection and interpretation with an automated computational system. The neural network algorithm processes oxygen saturation data electronically, substituting human cognitive processing with machine learning-based pattern recognition that can consistently apply diagnostic criteria without fatigue or subjectivity.
2Productivity
If manual expert interpretation is used for classifying oxygen saturation data, then pathological conditions can be identified, but time consumption and operational burden increase
Solution Approach 1:
The monitoring system performs self-service by automatically classifying oxygen saturation data without requiring continuous human intervention. The trained neural network independently processes patient signals, makes diagnostic classifications, and generates alerts autonomously, freeing healthcare professionals from the repetitive task of continuous manual monitoring while maintaining high detection accuracy.
Solution Approach 2:
The patent implements preliminary action through pre-training the neural network on extensive annotated datasets before deployment. The model learns diagnostic patterns in advance during the training phase, enabling it to immediately apply learned knowledge to new patient data without requiring real-time expert guidance or complex operational procedures during actual monitoring.
3Measurement precision
If complex algorithms are used to improve classification accuracy, then detection precision increases, but computational requirements and system complexity increase
Solution Approach 1:
The patent applies parameter changes by transforming the input oxygen saturation data through feature extraction and dimensionality reduction techniques before classification. The system converts raw pulse oximetry signals into standardized statistical parameters and patterns, optimizing the data representation to enhance classification precision while keeping the computational complexity manageable through efficient parameter transformation rather than overly complex algorithmic structures.
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 system effectively classifies oxygen saturation data, enabling the detection of pathological conditions without constant expert oversight, providing timely alerts for potential health issues and improving monitoring accuracy.
Implementation Method 1
Pulse oximeters typically utilize a noninvasive sensor that transmits electromagnetic radiation, such as light, through a patient's tissue and that photoelectrically detects the absorption and scattering of the transmitted light in such tissue
Implementation Method 2
A linear discriminator is composed having a non-linear transform that accepts as input the statistics resulting from the statistical analysis and a pulse oximeter is programmed to compute the linear discriminator using a kernel function
Data Source
AI summary
A method of manufacturing a pulse oximeter configured to classify patient data is disclosed. The method includes collecting a set of sample data and classifying the sample data as either pathological or normal using human expertise. The method also includes generating statistics representative of the saturation traces. A linear discriminator is composed having a non-linear transform that accepts the statistics as input and a pulse oximeter is programmed to compute the linear discriminator using a kernel function.