Oscillometric Signal Frequency Analysis for Cuff Condition Detection
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Solution Overview
Problem
Current methods for diagnosing peripheral arterial disease (PAD) using oscillometric techniques are hindered by the need for skilled personnel and are prone to errors due to special cuff conditions, such as incorrectly located or loosely applied pressure applicators, which can affect the reliability of the oscillometric signal.
Innovation Solution
A method and system that utilize a pressure applicator, processor, and special conditions evaluation module to analyze the frequency content of oscillometric signals, determining a ratio of frequency components to identify diagnostic classes and correct for special cuff conditions, allowing for reliable PAD diagnosis by personnel without specialized training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If oscillometric techniques are used for PAD diagnosis, then ease of operation is improved (no specialized training needed), but reliability deteriorates due to errors from special cuff conditions
Solution Approach 1:
The system continuously monitors the oscillometric signal and uses feedback mechanisms to detect special cuff conditions. The processor analyzes signal characteristics in real-time and provides feedback to identify when cuff conditions are abnormal, allowing for automatic adjustment or rejection of measurements to maintain reliability while preserving ease of operation.
Solution Approach 2:
The patent replaces manual assessment of cuff conditions with automated electronic detection. Instead of requiring trained personnel to physically assess cuff placement, the system uses electronic sensors and signal processing to automatically detect and identify special cuff conditions, thereby maintaining ease of operation while improving reliability through objective electronic monitoring.
2Measurement precision
If frequency content analysis is performed on oscillometric signals, then measurement precision is improved for detecting PAD severity, but device complexity increases
Solution Approach 1:
The processor is designed to perform multiple functions: it processes the oscillometric signal, analyzes frequency content, detects special cuff conditions, and determines PAD severity all through a single integrated system. This multi-functionality reduces overall device complexity compared to having separate systems for each function, while maintaining high measurement precision for PAD detection.
Solution Approach 2:
The system transforms the oscillometric signal from the time domain to the frequency domain by analyzing frequency content parameters. This parameter transformation enables precise detection of PAD severity through spectral analysis while using computationally efficient algorithms that minimize device complexity. The processor evaluates multiple frequency components and their relationships to identify diagnostic patterns.
3Device complexity
If special cuff conditions are not identified, then device complexity remains low, but loss of information occurs due to unreliable signal analysis
Solution Approach 1:
The system performs preliminary analysis of the oscillometric signal to identify special cuff conditions before proceeding with PAD diagnosis. By detecting abnormal cuff conditions in advance and classifying them into diagnostic categories, the system prevents erroneous information from being generated, thereby avoiding loss of information while maintaining relatively simple device architecture.
Solution Approach 2:
The processor acts as an intermediary between the oscillometric signal and the final PAD diagnosis. It includes an intermediate analysis stage that specifically identifies special cuff conditions and classifies them into diagnostic classes. This intermediary function filters out unreliable signals before they affect the final measurement, preventing information loss without requiring complex additional hardware.
Data Source
AI summary
A method, system and computer program product are provided for evaluating whether an oscillometric signal representative of pressure oscillations in the vasculature of a patient is associated with special conditions that may lead to inadvertently identifying the signal as being indicative of peripheral arterial disease or non-analyzable. In one embodiment, the method includes obtaining an oscillometric signal at a location on an extremity of the patient, determining a ratio using a value associated with a first frequency component of the oscillometric signal and a value associated with a second frequency component of the oscillometric signal, comparing the ratio to a threshold value, associating a first diagnostic class with the oscillometric signal when a first outcome results from comparing the ratio to a threshold value, and associating a second diagnostic class with the oscillometric signal when a second outcome results from comparing the ratio to a threshold value.