APPARATUS AND METHOD FOR PREDICTING HYPOTENSION BASED ON ARTERIAL PRESSURE WAVELET TRANSFORMATION, AND METHOD FOR TRAINING HYPOTENSION PREDICTION MODEL

20240186005

18/438563

February 12, 2024

There is provided an apparatus for predicting hypotension of a subject. The apparatus comprises a memory configured to store one or more instructions and a pre-trained hypotension prediction model; and a processor configured to execute the one or more instructions stored in the memory, wherein the instructions, when executed by the processor, cause the processor to: determine an arterial blood pressure data of the subject, input the arterial blood pressure data of the subject into the hypotension prediction model, and determine whether the subject has hypotension using an output result of the hypotension prediction model.

THE ASAN FOUNDATION (Seoul, KR), NATIONAL CANCER CENTER (Goyang-si, KR), UNIVERSITY OF ULSAN FOUNDATION FOR INDUSTRY COOPERATION (Ulsan, KR)

1. An apparatus for predicting hypotension of a subject, comprising: a memory configured to store one or more instructions and a pre-trained hypotension prediction model; and a processor configured to execute the one or more instructions stored in the memory, wherein the instructions, when executed by the processor, cause the processor to: determine an arterial blood pressure data of the subject, input the arterial blood pressure data of the subject into the hypotension prediction model, and determine whether the subject has hypotension using an output result of the hypotension prediction model, wherein the hypotension prediction model is trained to determine whether a training subject has hypotension for a training arterial blood pressure data of the training subject.

2. The apparatus of claim 1, wherein the hypotension prediction model is trained by a training input data including a plurality of intervals of the training arterial blood pressure data of the training subject and a label data including whether or not hypotension has occurred for each of the plurality of intervals of the training arterial blood pressure data.

3. The apparatus of claim 2, wherein the hypotension prediction model includes: a first layer trained to extract trend data for each of the plurality of intervals of the training arterial blood pressure data of the training subject by wavelet-transforming the plurality of intervals of the training arterial blood pressure data of the training subject; and a shapelet data generation module configured to generate a shapelet data corresponding to the trend data.

4. The apparatus of claim 3, wherein the hypotension prediction model is trained to low-pass filter the training arterial blood pressure data using training parameters trained in the first layer.

5. The apparatus of claim 3, wherein the hypotension prediction model includes: a second layer trained to assign a weight to at least one of intervals among the plurality of intervals of the training arterial blood pressure data of the training subject; and a third layer trained to calculate similarity feature value between shapelet data of the at least one interval and the trend data on the basis of the assigned weight.

6. The apparatus of claim 3, wherein the processor is configured to input the arterial blood pressure data of the subject to the hypotension prediction model, and to calculate similarity between trend data for each of the plurality of intervals of the arterial blood pressure data of the subject and the shapelet data generated by the shapelet data generation module.

7. The apparatus of claim 6, wherein the processor is configured to calculate hypotension probability for the calculated similarity of the trend data for each of the plurality of intervals of the arterial blood pressure data of the subject using a logistic regression layer.

8. A method for training a hypotension prediction model performed by an electric device including a processor, comprising: preparing a training data including a training input data and a label data, wherein the training input data includes a plurality of intervals of the training arterial blood pressure data of a training subject and, the label data includes whether or not hypotension has occurred for each of the plurality of intervals of the training arterial blood pressure data of the training subject; inputting the training input data into the hypotension prediction model; training the hypotension prediction model to extract trend data for each of the plurality of intervals of the training arterial blood pressure data of the training subject by wavelet-transforming the plurality of intervals of the training arterial blood pressure data of the training subject; and generating a shapelet data corresponding to the trend data.

9. The method of claim 8, wherein the training of the hypotension prediction model to extract trend data includes updating training parameters trained in a first layer such that the hypotension prediction model low-pass filters the training arterial blood pressure data of the training subject.

10. The method of claim 8, further comprising: training the hypotension prediction model to assign a weight to at least one of intervals among the plurality of intervals of the training arterial blood pressure data of the training subject; and training the hypotension prediction model to calculate similarity feature values between shapelet data of the at least one interval and the trend data on the basis of the assigned weight.

11. A method for predicting hypotension performed by an apparatus for predicting hypotension of a subject, the method comprising: preparing a pre-trained hypotension prediction model; determining an arterial blood pressure data of the subject; inputting the arterial blood pressure data of the subject into the hypotension prediction model, and determining whether the subject has hypotension using an output result of a hypotension prediction model, wherein the hypotension prediction model is pre-trained to determine whether a training subject has hypotension for a training arterial blood pressure data of the training subject.

12. The method of claim 11, wherein the hypotension prediction model is trained by a training input data including a plurality of intervals of the training arterial blood pressure data of the training subject and a label data including whether or not hypotension has occurred for each of the plurality of intervals of the training arterial blood pressure data.

13. The method of claim 12, wherein the hypotension prediction model includes: a first layer trained to extract trend data for each of the plurality of intervals of the training arterial blood pressure data of the training subject by a wavelet-transforming the plurality of intervals of the training arterial blood pressure data of the training subject; and a shapelet data generation module configured to generate a shapelet data corresponding to the trend data.

14. The method of claim 13, wherein the determining of whether the subject has hypotension includes inputting the arterial blood pressure data of the subject to the hypotension prediction model, and calculating similarity between the trend data for each of the plurality of intervals of the arterial blood pressure data of the subject and the shapelet data generated by the shapelet data generation module.

15. The method of claim 14, wherein the determining of whether the subject has hypotension includes calculating hypotension probability for the calculated similarity of the trend data for each of the plurality of intervals of the arterial blood pressure data of the subject using a logistic regression layer.

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