PPG Sleep Apnea Classification Using Respiratory Effort Extraction
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Solution Overview
Problem
Existing Home Sleep Apnea Testing (HSAT) systems, particularly those using peripheral arterial tonometry (PAT), struggle to differentiate between central and obstructive sleep apnea events due to a lack of airflow and respiratory effort channels, relying solely on finger photoplethysmography (PPG) data, which limits their accuracy in diagnosing central sleep apnea (CSA).
Innovation Solution
A method and system that utilizes PPG data from a finger sensor to extract respiratory-effort related information, deriving a signal resembling polysomnography (PSG)-based respiratory effort, and employs machine learning techniques, such as an ensemble of trees classifier, to differentiate between central and obstructive sleep apnea events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If PAT HSAT uses only finger PPG sensor, then device complexity and ease of operation are improved, but measurement precision and reliability of CSA detection deteriorate
Solution Approach 1:
The patent introduces an intermediary processing layer that extracts respiratory effort information from PPG signal variations. This intermediary approach allows the system to derive useful respiratory monitoring capabilities from the limited PPG data without requiring additional sensors, thus maintaining device simplicity while improving measurement precision for CSA detection.
Solution Approach 2:
The system changes the analysis parameters by examining subtle variations in PPG signal characteristics (amplitude, timing, waveform shape) that correlate with respiratory effort. By transforming the interpretation of existing PPG data through different parameter analysis, the system achieves CSA detection capability without adding hardware complexity.
2Ease of operation
If PAT HSAT uses only finger PPG sensor, then ease of operation is improved, but ability to differentiate CSA from OSA deteriorates
Solution Approach 1:
The patent segments the PPG signal into distinct analytical components (respiratory-related amplitude variations, timing patterns, waveform characteristics) and applies different analysis methods to each segment. This segmentation allows the system to extract multiple types of information from a single sensor source, improving the reliability of CSA differentiation while maintaining ease of operation.
Solution Approach 2:
The system adds analytical dimensions by examining PPG data from multiple perspectives (time domain, frequency domain, waveform morphology) rather than relying on a single measurement approach. This multi-dimensional analysis of the same sensor data enables reliable CSA/OSA differentiation without requiring additional sensors or complicating the device operation.
3Device complexity
If PAT HSAT lacks airflow and respiratory effort channels, then device complexity is reduced, but loss of information for respiratory event classification increases
Solution Approach 1:
The patent extracts respiratory effort information by taking out and isolating specific signal components from the PPG data that correlate with respiratory mechanics. By extracting these specific features (amplitude modulations, timing variations), the system compensates for the missing dedicated respiratory effort channel while keeping the device simple.
Solution Approach 2:
The PPG sensor is made universal by demonstrating its ability to provide multiple types of information (oxygen saturation, pulse rate, and respiratory effort) through different analysis methods. This multi-functionality allows a single sensor to replace what would traditionally require multiple specialized sensors, reducing device complexity while minimizing information loss.
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
Enables accurate differentiation and detection of central sleep apnea using PPG data, improving the diagnostic capability of HSAT systems and facilitating referral to in-lab polysomnography for confirmation, thereby enhancing the precision of CSA identification.
Implementation Method 1
relying solely on finger photoplethysmography (PPG) data
Data Source
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AI summary
Apparatus and methods, such as with processing apparatus (102), distinguish sleep disordered breathing (SDB) events from a signal generated by, for example, a photoplethysmography (PPG) sensor. Data representing the signal from the sensor may be received and a PPG-based respiratory signal may be derived from data. First and/or second level features may be computed from the respiratory signal. The first and/or second level features may be evaluated, such as in a classifier. Based on the evaluation, an output indication identifying SDB type may be generated. In some implementations, the apparatus, detects SDB event(s) from a PPG signal. Data representing the signal from the PPG sensor may be received. A vasoconstriction event and/or arrythmia event may be detected from the data. The data representing the PPG signal may be evaluated to generate, based on the evaluation and the detected events of vasoconstriction and/or arrythmia, an output indication identifying SDB event(s).