Multidimensional compensation and improved r-value estimation for pulse oximetry
AssigneeRajant Health Incorporated
InventorJohn Mountney
Life of this patent
Estimating the term…
Abstract
A pulse oximeter that improves the accuracy of blood oxygen saturation measurements by using skin moisture data (e.g., captured by a galvanic skin response or bioimpedance sensor) to account for variations in photoplethysmogram (PPG) caused by changes in the skin moisture level of the user. Additionally, in some embodiments, the pulse oximeter uses a modified Goertzel algorithm to calculate light absorption metrics (e.g., R-values) with higher accuracy and fewer computational resources than prior art devices. Additionally, in some embodiments, the pulse oximeter improves precision by calculating light absorption metrics at more frequency bands than the conventional ratio of red and infrared absorption.
Claims20
A wearable health monitoring device for estimating blood oxygen saturation of a user, comprising:
- a skin moisture sensor that captures skin moisture data indicative of a skin moisture level of the user;
- a skin tone sensor that captures reflectivity data indicative of a skin tone of the user;
- a photoplethysmography (PPG) sensor that captures a photoplethysmogram that includes data indicative of light absorption by the skin of the user;
- and
- a hardware processing unit adapted to:
- calculate the skin moisture level of the user based on the captured skin moisture data;
- quantify the skin tone of the user based on the captured reflectivity data;
- calculate one or more light absorption metrics indicative of the light absorption by the skin of the user;
- and
- estimate the blood oxygen saturation of the user by providing the skin moisture level of the user, the skin tone of the user, and the one or more light absorption metrics to a multivariate model generated by a neural network trained on a dataset of blood oxygen saturation values captured from patients having calculated skin moisture levels, quantified skin tones, and calculated light absorption metrics.
A method for estimating blood oxygen saturation of a user, the method comprising:
- capturing skin moisture data indicative of a skin moisture level of the user;
- capturing reflectivity data indicative of a skin tone of the user;
- capturing a photoplethysmogram (PPG) that includes data indicative of light absorption by the skin of the user;
- calculating the skin moisture level of the user based on the captured skin moisture data;
- quantifying the skin tone of the user based on the captured reflectivity data;
- calculating one or more light absorption metrics indicative of the light absorption by the skin of the user;
- and
- estimating the blood oxygen saturation of the user by providing the skin moisture level of the user, the skin tone of the user, and the one or more light absorption metrics to a multivariate model generated by a neural network trained on a dataset of blood oxygen saturation values captured from patients having calculated skin moisture levels, quantified skin tones, and calculated light absorption metrics.
A method estimating blood oxygen saturation of a user, the method comprising:
- capturing a photoplethysmogram (PPG) signal that includes data indicative of light absorption by the skin of the user;
- bandpass filtering the PPG signal at each of at least two wavelengths to form a bandpass filtered PPG signal at each of the at least two wavelengths;
- estimating a pulsatile frequency of the bandpass filtered PPG signals;
- removing a DC component from each bandpass filtered PPG signal to form a zero-mean AC PPG signal at each of the at least two wavelengths;
- calculating an AC component of each zero-mean AC PPG signal at a target frequency bin using a modified Goertzel algorithm;
- estimating a magnitude of the zero-mean AC PPG signal at each of the at least two wavelengths;
- and
- calculating one or more light absorption metrics based on the estimated magnitudes and the removed DC components at the at least two wavelengths;
- and
- estimating the blood oxygen saturation of the user by providing one or more light absorption metrics to a mathematical model generated by a neural network trained on a dataset of blood oxygen saturation values captured from patients having calculated light absorption metrics.
Dependencies are inferred from the claim text.