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EP-4512317-B1 - A WEARABLE DEVICE FOR MEASURING A BLOOD PRESSURE OF A WEARER

EP4512317B1EP 4512317 B1EP4512317 B1EP 4512317B1EP-4512317-B1

Inventors

  • HANSE, Iddo Johanan Gideon
  • EBRAHIMKHEIL, Kambiz

Dates

Publication Date
20260513
Application Date
20240612

Claims (15)

  1. A wearable device for processing a photoplethysmography signal wherein said wearable device is configured for emitting light at a plurality of light-wavelengths for measuring a blood pressure of a wearer of said device, wherein the photoplethysmography signal comprises a plurality of pulses and wherein a pulse is the photoplethysmography signal between two consecutive valleys, characterized in that, for at least one light-wavelength, the wearable device is configured for: - creating a photoplethysmography segment by acquiring the photoplethysmography signal within a predefined time window; - collecting at least one biologically vital sign, such as a heart rate of the wearer, a respiration rate of the wearer, or an oxygen saturation rate of the wearer; - collecting at least one waveform-based parameter proportional to a peak amplitude of said photoplethysmography segment; - collecting at least one time-based parameter proportional to a peak time of said photoplethysmography signal or proportional to a peak width of said photoplethysmography segment; - creating a cardiovascular profile vector of the wearer; - calculating a mean squared error between components of the collected profile vector and components of a profile vector belonging to previously predicted cluster of profile vectors; and - estimating a blood pressure of the wearer using the photoplethysmography segments comprised in the cardiovascular profile of the wearer when said mean squared error is smaller than a first threshold value.
  2. The wearable device according to claim 1, characterized in that the wearable device is configured for filtering the photoplethysmography segment, preferably by using a bandpass filter, and using said filtered photoplethysmography segment in the remaining steps of said method.
  3. The wearable device according to claim 1 or 2, characterized in that the wearable device is configured for creating a cardiovascular profile vector of the wearer by concatenating photoplethysmography segments with at least one of: - the collected at least one waveform-based parameter of the wearer; - the collected at least one time-based parameter of the wearer; and - the collected at least one biologically vital sign of the wearer.
  4. The wearable device according to any one of the preceding claims, characterized in that the wearable device is configured for normalizing the collected at least one time-based parameter by multiplying said parameter by a factor inversely proportional to a peak width of the photoplethysmography segment.
  5. The wearable device according to any one of the preceding claims, characterized in that the wearable device is configured to emit light comprising light-wavelengths between 400 nm and 1000 nm.
  6. The wearable device according to any one of the preceding claims, characterized in that the wearable device is configured to emit light comprising: - a light-wavelength of 525 nm; - a light-wavelength of 660 nm; and - a light-wavelength of 880 nm.
  7. The wearable device according to any one of the preceding claims, characterized in that, for at least one photoplethysmography segment, the wearable device is configured for: - calculating at least one of the following values for each pulse of said photoplethysmography segment: ∘ a pulse wave amplitude left by calculating a difference between amplitudes of a first peak and a first valley of the pulse; ∘ a pulse wave amplitude right by calculating a difference between amplitudes of the first peak and a second valley of the pulse; ∘ a pulse wave duration by calculating a difference between a time of the second valley and a time of the first valley of the pulse; ∘ a rise time by calculating a difference between a time of the first peak and a time of the first valley of the pulse; ∘ a systolic-to-diastolic duration ratio by calculating the ratio between a difference between the rise time and a difference between the time of the second valley and the time of the first peak of the pulse; - eliminating a photoplethysmography pulse when: ∘ the rise time of said pulse is outside a predefined first range; or ∘ the pulse wave duration of said pulse is outside a predefined second range; or ∘ a ratio or an inverse ratio between the pulse wave amplitude right and the pulse wave amplitude left is smaller than a second threshold value; or ∘ the systolic-to-diastolic duration ratio is larger than a predefined third threshold value; - calculating a signal quality index of the photoplethysmography segment by calculating a ratio of non-eliminated photoplethysmography pulses over a total number of photoplethysmography pulses comprised in said photoplethysmography segment.
  8. The wearable device according to any one of the preceding claims characterized in that the wearable device is configured for eliminating the photoplethysmography segment when a signal quality index of said photoplethysmography signal is lower than a fourth threshold value.
  9. The wearable device according to claim 8, characterized in that the wearable device is configured for setting said fourth threshold value at 20%.
  10. The wearable device according to any one of the preceding claims, characterized in that the wearable device is configured for: - collecting demographic information of the wearer, such as age, sex, body weight and height; and - adding said demographic information into the cardiovascular profile vector of the wearer.
  11. The wearable device according to any one of the preceding claims, characterized in that the wearable device is configured for grouping profile vectors of a plurality of wearers into a plurality of clusters based on a similarity in at least one of the biologically vital signs and/or based on a similarity in the demographic information of the wearers by using a silhouette coefficient to determine an optimal number of clusters for establishing a K-Means clustering on said profile vectors.
  12. The wearable device according claim 11, characterized in that the wearable device is configured for training a Random Forest model on each cluster of profile vectors by combining a plurality of decision trees and training each decision tree on a separate subset of data of said cluster.
  13. The wearable device according to any one of claims 11-12, characterized in that the wearable device is configured for: - assigning a new wearer to one of the plurality of clusters based on at least one of the biologically vital signs of said new wearer and/or based on the demographic information of the new wearer; and - using the Random Forest model corresponding to said cluster as a starting point, and fine-tuning said starting point to create a cardiovascular profile for the new wearer using a regularization framework such as a Lasso regression, a Ridge regression or an Elastic Net.
  14. The wearable device according to any one of the preceding claims, characterized in that the wearable device is configured for updating the cardiovascular profile of the wearer by performing the steps of the preceding claims on new sets of acquired photoplethysmography segments.
  15. The wearable device according to any one of the preceding claims, characterized in that said wearable device is configured for emitting light onto a skin of a wearer and receiving light emitted from the skin of the wearer wherein the device comprises a computer system configured to perform the operations according to any one of the preceding claims.

Description

A wearable device for measuring a blood pressure of a wearer The invention relates to a wearable device for non-invasively measuring blood pressure using multi-wavelength photoplethysmography (PPG) signals obtained by said wearable device from peripheral blood vessels. General Description Photoplethysmography (PPG) is a non-invasive optical technique that measures blood volume changes in the microvascular bed of tissue. It works by shining a light source, typically an LED, onto the skin and detecting the amount of light that is transmitted or reflected back to a photodetector. This optical signal can be used to derive information about blood flow, heart rate, and other physiological parameters. Blood pressure estimation based on photoplethysmography (PPG) signals has received increasing attention in recent years due to the non-invasive and continuous nature of PPG measurements. There are a variety of algorithms that have been proposed for estimating blood pressure from PPG signals, including methods that utilize waveform-based feature extraction, frequency features of the input signal, and combinations of both. Some of these methods also incorporate other physiological signals, such as electrocardiogram (ECG) and PPG sensors from multiple locations on the body, to obtain an indication of Pulse-Transit Time (PPT), a feature highly correlated with blood pressure. Although accurate, having sensors placed on multiple locations of the body imposes new challenges, such as placement determination and an increased chance of noisy signals. Moajjem Hossain Chowdhurry et al. in their article in Sensors 2020, titled "Estimating Blood Pressure from Photoplethysmogram Signal and Demographic Features using Machine Learning Techniques", proposed a method for estimating blood pressure (BP) based on PPG signals wherein time, frequency and time-frequency domain features are extracted from the PPG signals and fed in to ML algorithms to estimate systolic BP and diastolic BP. US20230082362 and Akuthota Chandra et al. in their publication in IEEE 2022 proposed measuring BP from single PPG. El-Hajj et al. in their publication in biomedical signal processing and control 2020, proposed using machine learning and PPG signal processing to estimate blood pressure. The existing single measurement site PPG-based blood pressure algorithms are based on single-wavelength signal, and use time- and frequency domain features in order to estimate blood pressure. These features are often used as input in machine learning methods to train a model that maps the combination of features to the associated systolic and diastolic blood pressure values. A problem with only using time- and frequency domain features based on the direct ppg signal is a high overlap and correlation of features, making the algorithms prone to overfitting on certain characteristics in the PPG signal. It is an object of the current invention to correct the shortcomings of the prior art and to provide a solution for measuring a blood pressure of a wearer using single-site measurements while providing measurements accuracy better than that of multiple-site measurements. This and other objects which will become apparent from the following disclosure, are provided with a wearable device having the features of one or more of the appended claims. In a first aspect of the invention, the wearable device is configured for processing a photoplethysmography signal and for emitting light at a plurality of light-wavelengths for measuring a blood pressure of a wearer of said device, wherein the photoplethysmography signal comprises a plurality of pulses and wherein a pulse is the photoplethysmography signal between two consecutive valleys, wherein, for each light-wavelength, the wearable device is configured for: creating a photoplethysmography segment by acquiring the photoplethysmography signal within a predefined time window;collecting at least one biologically vital sign, such as a heart rate of the wearer, a respiration rate of the wearer, and an oxygen saturation rate of the wearer;collecting at least one waveform-based parameter proportional to a peak amplitude of said photoplethysmography segment;collecting at least one time-based parameter proportional to a peak time of said photoplethysmography signal or proportional to a peak width of said photoplethysmography segment;creating a cardiovascular profile vector of the wearer;calculating a mean squared error between the collected profile vector and a profile vector belonging to previously predicted cluster of profile vectors, not necessarily profile vectors of the current wearer; andestimating a blood pressure of the wearer using the photoplethysmography segments comprised in the cardiovascular profile of the wearer when said mean squared error is smaller than a first threshold value. Advantageously, the wearable device is configured for filtering the photoplethysmography segment, preferably by using a bandpass filter, and using said