Pulse Arrival Time Determination Using Wavelet Signal Processing
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Solution Overview
Problem
Wearable electronic devices face challenges in accurately determining pulse arrival time (PAT) due to low signal-to-noise ratio sensor outputs and high amounts of irrelevant information, which affects the reliability of blood pressure assessment.
Innovation Solution
The method involves filtering and normalizing ECG and optical signals using continuous wavelet transforms and pattern recognition techniques to isolate R-waves and derive PAT, while eliminating hard-thresholding, and utilizing a synchronization index for quality assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional signal processing methods are used on wearable device sensors, then device complexity is reduced, but measurement precision of pulse arrival time deteriorates due to low signal-to-noise ratio
Solution Approach 1:
The patent segments the signal processing into distinct stages: wavelet transform for denoising, R-wave detection for ECG feature extraction, and pulse wave analysis for PAT determination. Each stage processes specific aspects of the signal independently, improving measurement precision while keeping individual processing steps manageable.
Solution Approach 2:
The patent introduces an intermediary processing layer using continuous wavelet transforms and synchronization indices that mediate between the raw low-quality sensor signals and the final PAT measurement. This intermediary layer filters noise and enhances relevant features without requiring complex hardware modifications.
2Measurement precision
If more sophisticated signal processing techniques are applied, then measurement precision of PAT improves, but loss of time for processing increases
Solution Approach 1:
The patent applies preliminary wavelet transform processing to pre-denoise the signals before PAT determination. By preparing the signals in advance with appropriate filtering and normalization, the actual PAT measurement can be performed more quickly and accurately without requiring extensive real-time processing.
Solution Approach 2:
The patent changes the parameter representation of the signals by transforming them into the wavelet domain, where noise and relevant features are separated. This parameter transformation allows for more efficient processing and faster extraction of PAT information from the denoised signals.
3Measurement precision
If simple thresholding methods are used for R-wave detection, then device complexity is reduced, but measurement precision deteriorates due to motion artifacts and noise
Solution Approach 1:
The patent replaces simple mechanical thresholding methods with wavelet-based signal processing and pattern recognition algorithms. This substitution enables more accurate R-wave detection by analyzing the signal in the frequency domain and identifying characteristic patterns, rather than relying on fixed amplitude thresholds that are sensitive to noise and motion artifacts.
Data Source
AI summary
Systems and methods for determining pulse arrival time utilizing sensors coupled to mobile electronic devices are described. A system embodiment includes, but is not limited to, a sensor configured to provide electrocardiogram (ECG) data; an optical sensor configured to provide optical data; and a controller configured to access each of the ECG data and the optical data, the controller configured to: isolate and normalize R-wave information from the ECG data, isolate information associated with the cardiac rhythm from the isolated and normalized R-wave information to provide pulse waves, determine temporal characteristics of the pulse waves, convert and normalize the optical data in a wavelet time-frequency plane, and calculate pulse arrival time utilizing each of the temporal characteristics of the pulse waves and the converted and normalized optical data.