Neural network automated invasive arterial pressure extraction
AssigneeThe Regents of the University of Michigan
InventorsZachary Sharpe, Mohamad Hakam Tiba, Cindy H. Hsu
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Abstract
The methods and systems described herein relate to automatically analyzing arterial pressure waveforms during cardiopulmonary resuscitation using neural networks and other machine learning models or processing methods. The methods and systems may differentially label diastolic and systolic blood pressure coming from chest compression-induced arterial waveforms versus spontaneous heartbeats. In one aspect, a portable device receives arterial pressure data in real time and makes predictions of the systolic pressure and/or diastolic pressure using an on-board machine learning model.
Claims20
A computer-implemented method of determining arterial blood pressure and related properties, comprising:
- providing, by one or more processors, a blood pressure input vector comprising a plurality of arterial blood pressure measurements of a patient during a time period to a machine learning model, causing the machine learning model to:
- normalize the plurality of arterial blood pressure measurements into a normalized blood pressure vector,
- convolve, using a first set of one or more filters, the normalized blood pressure vector into a first feature map,
- convolve, using a second set of one or more dilated filters, the first feature map into a second feature map, wherein the second set of one or more dilated filters are dilated at a first dilation rate,
- convolve, using a third set of one or more dilated filters, the second feature map into a third feature map, wherein the third set of one or more dilated filters are dilated at a second dilation rate that is greater than the first dilation rate,
- convolve, using a fourth set of one or more dilated filters, the third feature map into a probability matrix, wherein the probability matrix comprises probabilities for a plurality of classifications for one or more of the plurality of arterial blood pressure measurements, and
- generate, from the probability matrix, an output vector comprising indications of predicted classifications for the one or more of the plurality of arterial blood pressure measurements;
- and
- outputting, by the one or more processors based on the output vector, an indication of thearterial blood pressure to a user.
A portable computing device for determining arterial blood pressure and related properties, comprising:
- one or more processors;
- and
- one or more non-transitory memories coupled to the one or more processors and storing instructions that when executed by the one or more processors, cause the one or more processors to:
- provide a blood pressure input vector comprising a plurality of arterial blood pressure measurements of a patient during a time period to a machine learning model, causing the machine learning model to:
- normalize the plurality of blood pressure measurements into a normalized blood pressure vector,
- convolve, using a first set of one or more filters, the normalized blood pressure vector into a first feature map,
- convolve, using a second set of one or more dilated filters, the first feature map into a second feature map, wherein the second set of one or more dilated filters are dilated at a first dilation rate,
- convolve, using a third set of one or more dilated filters, the second feature map into a third feature map, wherein the third set of one or more dilated filters are dilated at a second dilation rate that is greater than the first dilation rate,
- convolve, using a fourth set of one or more dilated filters, the third feature map into a probability matrix, wherein the probability matrix comprises probabilities for a plurality of classifications for arterial blood pressure measurements, and
- generate, from the probability matrix, an output vector comprising indications of predicted classifications for the arterial blood pressure measurements, and
- output, based on the output vector, an indication of the arterial blood pressure to a user.
A computer-implemented method of training a machine learning model to predict arterial blood pressure, comprising:
- providing, by one or more processors, a labeled training dataset to one or more machine learing models or other processing methods, wherein the labeled training dataset comprises a plurality of blood pressure data points and a plurality of labeled classifications;
- receiving, by the one or more processors, predicted classification outputs from the one or more machine learning models;
- calculating, by the one or more processors, a loss metric by comparing the predicted classification outputs to the plurality of labeled classifications;
- adjusting, by the one or more processors using the loss metric, one or more weights and/or biases of the one or more machine learning models;
- and
- compressing, by the one or more processors, a selected one of the one or more machine learning models into a compressed machine learning model.
Dependencies are inferred from the claim text.