High-Frequency Respiratory Flow Analysis for Snore Detection
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Solution Overview
Problem
Existing methods for detecting snoring during sleep apnea syndrome (SAS) are not accurate enough, making it difficult to effectively identify snoring events.
Innovation Solution
A medical device and method that utilizes high-frequency data analysis from respiratory flow rates to detect snoring by selecting specific periods within the respiratory cycle, applying filters to isolate relevant data, and comparing against reference data to confirm snoring occurrences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If simple flow rate comparison method is used, then device complexity is reduced, but measurement precision of snore detection deteriorates
Solution Approach 1:
The detection method is segmented into multiple processing stages: generating high frequency data from flow rate data, dividing into multiple periods, calculating evaluation values for each period, selecting optimal periods, and performing frequency analysis. This segmentation transforms a simple comparison method into a multi-stage sophisticated detection system that achieves high accuracy without requiring complex hardware.
Solution Approach 2:
The invention transitions from analyzing flow rate data in the time domain to analyzing it in the frequency domain by generating high frequency data and performing spectral analysis. This dimensional change enables the detection of snore characteristics that are not apparent in simple time-domain flow rate comparisons, significantly improving detection accuracy.
2Measurement precision
If high frequency data analysis with multiple periods is used, then measurement precision of snore detection is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary actions by generating high frequency data from flow rate data before the actual snore detection. It also pre-divides the data into multiple periods and calculates evaluation values in advance, selecting optimal periods for analysis. These preliminary processing steps organize the data structure to facilitate accurate frequency analysis while maintaining manageable system complexity.
Solution Approach 2:
The invention applies partial action by selecting only certain periods (those with evaluation values meeting predetermined criteria) for frequency analysis rather than analyzing all periods. This selective approach reduces the computational burden and system complexity while maintaining high detection accuracy by focusing resources on the most relevant data segments.
3Loss of time
If flow rate data is analyzed without high frequency transformation, then processing time is reduced, but measurement precision deteriorates
Solution Approach 1:
The invention extracts the high frequency components from the original flow rate data by generating high frequency data through spectral transformation. This extraction isolates the relevant snore-related frequency information from the broader flow rate signal, enabling accurate detection without requiring analysis of the entire frequency spectrum, thus balancing processing time and accuracy.
Data Source
AI summary
A medical device includes a generator configured to generate high frequency data from flow rate data representing a time series of a flow rate of respiration of a patient, a determiner configured to determine, for each of a plurality of periods, an evaluation value of the high frequency data in each period, a selector configured to select one period from among the plurality of periods on the basis of the evaluation values of the plurality of periods, and a detector configured to detect occurrence of a snore of the patient by analyzing a part of the selected one period in the flow rate data.