Respiratory Rate Prediction From PPG Using Neural Network
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Solution Overview
Problem
Current methods for estimating respiratory rate from photoplethysmogram (PPG) signals lack accuracy and efficiency, particularly in noisy environments and with artifacts, affecting the reliability of non-invasive health monitoring devices.
Innovation Solution
An artificial neural network model is employed, comprising convolutional layers to identify and clean PPG signals, followed by a Fast Fourier Transform (FFT) layer to extract fundamental and harmonic frequencies, and a dense decoding layer to predict respiratory rates, improving accuracy and reducing processing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional FFT or peak detection algorithms are used to extract respiratory rate from PPG signal, then the processing method is simple, but the accuracy is reduced due to noise and artifacts
Solution Approach 1:
The patent segments the PPG signal processing into distinct functional blocks: convolutional layers for spatial feature extraction and noise filtering, FFT layer for frequency domain transformation, and dense decoding layer for respiratory rate prediction. This segmentation allows each component to specialize in specific tasks, improving overall accuracy while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent introduces an intermediary neural network processing layer between the raw PPG signal and the final respiratory rate extraction. This intermediary layer (comprising convolutional and dense layers) acts as a mediator that cleans and transforms the noisy PPG signal into a refined representation before respiratory rate calculation, thereby improving measurement accuracy without directly modifying the original signal.
2Reliability
If noise and artifacts are not removed from PPG signal, then the processing time is reduced, but the reliability of respiratory rate estimation deteriorates
Solution Approach 1:
The patent applies preliminary noise filtering and artifact removal through convolutional layers before the main respiratory rate extraction process. By performing this cleaning action in advance, the subsequent FFT and decoding operations work with already-refined signals, improving reliability while minimizing the time loss of the main processing pipeline through parallel and sequential optimization.
Solution Approach 2:
The patent replaces traditional mechanical signal filtering methods with neural network-based convolutional layers that automatically learn and apply optimal filtering operations. This substitution enables more effective noise and artifact removal while maintaining efficient processing speeds, as the neural network operations can be optimized for rapid execution on hardware accelerators.
3Measurement precision
If a comprehensive neural network model with multiple layers is used, then the accuracy of respiratory rate prediction is improved, but the device complexity increases
Solution Approach 1:
The complex neural network model is segmented into three distinct layers with specific functions: convolutional layers for feature extraction and noise filtering, FFT layer for frequency domain analysis, and dense decoding layer for respiratory rate prediction. This segmentation reduces perceived complexity by providing clear functional boundaries and enabling independent optimization of each component.
Solution Approach 2:
The neural network model is designed with multi-functionality where the same architecture can process different types of PPG signals and adapt to various respiratory rate ranges. The convolutional layers serve multiple purposes including noise filtering, feature extraction, and signal normalization, thereby reducing overall system complexity through component multiplicity rather than requiring separate specialized modules.
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 proposed solution enhances the accuracy of respiratory rate predictions by reducing noise and artifacts, leading to more reliable and efficient respiratory rate estimation in non-invasive health monitoring devices.
Implementation Method 1
A PPG is an optically obtained plethysmogram which can be used to detect blood volume changes in the microvascular bed of living tissue
Implementation Method 2
A pulse oximeter which illuminates the skin and measures changes in light absorption
Implementation Method 3
a fast Fourier transform (FFT) layer to convert the PPG signal to a frequency domain representation and identify fundamental and harmonic frequencies
Data Source
AI summary
A technology for obtaining a respiratory rate from a photoplethysmogram (PPG) signal. In one example, an artificial neural network model can be trained to predict a respiratory rate using a training dataset containing PPG data. The artificial neural network model can include a first series of convolutional layers to remove artifacts from a PPG signal, a fast Fourier transform (FFT) layer to convert the PPG signal to PPG frequency representations, and a dense layer to decode the PPG frequency representations to respiratory rate predictions. After training the artificial neural network model, PPG data generated by a pulse oximeter monitor can be obtained, and the PPG data can be input to the artificial neural network model. The artificial neural network model outputs a respiratory rate prediction, wherein the respiratory rate prediction represents the respiratory rate obtained from the PPG signal.