BLOOD PRESSURE EVALUATION WITH MACHINE LEARNING
A method for baseline blood pressure estimation of a user of a wearable physiological monitor. The method comprising identifying a segment of pulse data related to cardiac activity of the user during a portion of a sleep session, wherein the segment of pulse data is obtained by the wearable physiological monitor; determining, from the segment of pulse data, a resting heart rate value of the user during the portion of the sleep session; identifying a machine learning model trained to receive as input one or more features including an input resting heart rate value obtained during a first time period and predict an indicator of blood pressure during a second time period; and providing the resting heart rate value to the machine learning model to obtain an indicator of baseline blood pressure for the user.
1 . A method for baseline blood pressure estimation of a user of a wearable physiological monitor, the method comprising:
identifying a first segment of pulse data related to cardiac activity of the user during a first portion of a sleep session, wherein the first segment of pulse data is obtained by the wearable physiological monitor;
determining, from the first segment of pulse data, a first resting heart rate value of the user during the first portion of the sleep session;
identifying a machine learning model trained to receive as input one or more features including an input resting heart rate value obtained during a first time period and predict an indicator of blood pressure during a second time period; and
providing the first resting heart rate value to the machine learning model to obtain a first indicator of baseline blood pressure for the user.
2 . The method of claim 1 , wherein the first time period is during nighttime of the sleep session.
3 . The method of claim 1 , wherein the second time period is during daytime on a day following the sleep session.
4 . The method of claim 1 , further comprising:
extracting, from the first segment of pulse data, one or more static features that characterize an average pulse morphology during the first portion of the sleep session.
5 . The method of claim 4 , further comprising providing the one or more static features as input to the machine learning model to obtain the first indicator of baseline blood pressure.
6 . The method of claim 4 , wherein the one or more static features include any one or more of: an average pulse width value; an average maximum acceleration value; an average maximum derivative value; an average time to maximum derivative or acceleration value; an average area under the curve value; an average area without detrending value; an average time between systolic and diastolic peaks value; one or more latent features.
7 . The method of claim 1 , further comprising:
extracting, from the first segment of pulse data, one or more dynamic features that characterize temporal variation in pulse morphology during the first portion of the sleep session.
8 . The method of claim 7 , further comprising providing the one or more dynamic features to the machine learning model to obtain the first indicator of baseline blood pressure.
9 . The method of claim 7 , wherein the one or more dynamic features are generated from a plurality of morphology features extracted from each pulse in the first segment of pulse data.
10 . The method of claim 9 , wherein the plurality of morphology features extracted for a pulse of the first segment of pulse data include any two or more of: a pulse width value; a maximum acceleration value; a maximum derivative value; a time to maximum value; a time to maximum acceleration value; an area under the curve value; an area without detrending value; a time between systolic and diastolic peaks value; an instantaneous pulse rate value; a pulse amplitude value; one or more latent features; a notch metric value indicative of an extent of a dicrotic notch.
11 . The method of claim 10 , wherein the one or more latent features are determined using an encoder-decoder neural network.
12 . The method of claim 10 , wherein the notch metric value is extracted using an encoder-decoder neural network.
13 . The method of claim 12 , wherein the encoder-decoder neural network is a variational autoencoder.
14 . The method of claim 12 , wherein the encoder-decoder neural network is trained on a dataset of synthetic pulses and annotated real pulses, and wherein a reconstruction loss value extracted using the encoder-decoder neural network is indicative of a quality of the pulse.
15 . The method of claim 9 , further comprising:
providing the plurality of morphology features to a Recurrent Neural Network (RNN) to generate the one or more dynamic features, wherein the RNN is trained to output dynamic features from pulse morphology features provided as input.
16 . The method of claim 15 , wherein the RNN is one of a stacked Long Short Term Memory (LSTM) network or a Gated Recurrent Unit (GRU) network.
17 . The method of claim 1 , further comprising providing one or more features that characterize demographic data of the user to the machine learning model to obtain the first indicator of baseline blood pressure.
18 . The method of claim 1 , further comprising:
identifying sleep data that characterizes the first portion of the sleep session in relation to the sleep session; and
providing the sleep data as an additional input to the machine learning model to obtain the first indicator of baseline blood pressure.
19 . The method of claim 18 , wherein the sleep data comprises sleep onset data indicative of a start time of the first portion of the sleep session in relation to a start of the sleep session.
20 . The method of claim 18 , wherein the sleep data comprises sleep stage data.
21 - 101 . (canceled)