EP-4734837-A1 - END-TO-END CUFFLESS BLOOD PRESSURE MONITORING USING ECG AND PPG SIGNALS

Abstract

A method for performing cuffless blood pressure (BP) measurement, including: obtaining a first physiological signal and a second physiological signal associated with a user; providing the first physiological signal as an input to a first transformer model; providing the second physiological signal as an input to a second transformer model; providing an output of the first transformer model and an output of the second transformer model as inputs to a third transformer model; providing an output of the third transformer model to at least one BP estimation model; and generating an estimated BP value corresponding to the first physiological signal and the second physiological signal based on an output of the at least one BP estimation model

Inventors

• BETTAPALLI NAGARAJ, Suhas
• SAIDUTTA, Yashas Malur
• SRINIVASA, Rakshith Sharma
• CHO, Jaejin
• LEE, CHING-HUA
• YANG, CHOUCHANG
• SHEN, YILIN
• JIN, HONGXIA

Assignees

• Samsung Electronics Co., Ltd.

Dates

Publication Date

20260506

Application Date

20240920

Claims (15)

1. A method for performing cuffless blood pressure (BP) measurement, the method comprising: obtaining a first physiological signal and a second physiological signal associated with a user; providing the first physiological signal as an input to a first transformer model; providing the second physiological signal as an input to a second transformer model; providing an output of the first transformer model and an output of the second transformer model as inputs to a third transformer model; providing an output of the third transformer model to at least one BP estimation model; and generating an estimated BP value corresponding to the first physiological signal and the second physiological signal based on an output of the at least one BP estimation model.
2. The method of claim 1, wherein the first physiological signal comprises an electrocardiogram (ECG) signal associated with the user, and wherein the second physiological signal comprises a photoplethysmogram (PPG) signal associated with the user.
3. The method of claim 1, wherein the first transformer model, the second transformer model, the third transformer model, and the at least one BP estimation model are trained using a pre-training process and a user-specific training process, wherein the pre-training process is performed using a first training dataset corresponding to a plurality of users, and wherein the user-specific training process is performed based on a second training dataset corresponding to the user.
4. The method of claim 3, wherein at least one of the pre-training process and the user-specific training process is performed using a weighted contrastive loss function comprising a similarity metric.
5. The method of claim 4, wherein the similarity metric indicates a similarity between a first ground truth BP value corresponding to a first training sample and a second ground truth BP value corresponding to a second training sample.
6. The method of claim 4, wherein the similarity metric is used to cluster training samples included in at least one of the first training dataset and the second training dataset in an embedding space.
7. The method of claim 1, wherein the at least one BP estimation model comprises a systolic BP (SBP) estimation model and a diastolic BP (DBP) estimation model.
8. The method of claim 7, further comprising: providing the output of the third transformer model to the SBP estimation model; generating an estimated SBP value based on an output of the SBP estimation model; providing the output of the third transformer model to the DBP estimation model; and generating an estimated DBP value based on an output of the DBP estimation model, wherein the estimated BP value comprises the estimated SBP value and the estimated DBP value.
9. An electronic device for performing cuffless blood pressure (BP) measurement, the electronic device comprising: a first sensor configured to obtain a first physiological signal from a user; a second sensor configured to obtain a second physiological signal from the user; and at least one processor configured to: provide the first physiological signal as an input to a first transformer model; provide the second physiological signal as an input to a second transformer model; provide an output of the first transformer model and an output of the second transformer model as inputs to a third transformer model; provide an output of the third transformer model to at least one BP estimation model; and generate an estimated BP value corresponding to the first physiological signal and the second physiological signal based on an output of the at least one BP estimation model.
10. The electronic device of claim 9, wherein the first physiological signal comprises an electrocardiogram (ECG) signal associated with the user, and wherein the second physiological signal comprises a photoplethysmogram (PPG) signal associated with the user.
11. The electronic device of claim 9, wherein the first transformer model, the second transformer model, the third transformer model, and the at least one BP estimation model are trained using a pre-training process and a user-specific training process, wherein the pre-training process is performed using a first training dataset corresponding to a plurality of users, and wherein the user-specific training process is performed based on a second training dataset corresponding to the user.
12. The electronic device of claim 11, wherein at least one of the pre-training process and the user-specific training process is performed using a weighted contrastive loss function comprising a similarity metric.
13. The electronic device of claim 12, wherein the similarity metric indicates a similarity between a first ground truth BP value corresponding to a first training sample and a second ground truth BP value corresponding to a second training sample.
14. The electronic device of claim 12, wherein the similarity metric is used to cluster training samples included in at least one of the first training dataset and the second training dataset in an embedding space.
15. A non-transitory computer-readable medium storing instructions which, when executed by at least one processor of a device for performing cuffless blood pressure (BP) measurement, cause the device to: obtain a first physiological signal and a second physiological signal associated with a user; provide the first physiological signal as an input to a first transformer model; provide the second physiological signal as an input to a second transformer model; provide an output of the first transformer model and an output of the second transformer model as inputs to a third transformer model; provide an output of the third transformer model to at least one BP estimation model; and generate an estimated BP value corresponding to the first physiological signal and the second physiological signal based on an output of the at least one BP estimation model.

Description

END-TO-END CUFFLESS BLOOD PRESSURE MONITORING USING ECG AND PPG SIGNALS The disclosure relates to blood pressure (BP) monitoring, and more particularly to end-to-end cuffless BP monitoring using multimodal physiological signals. Hypertension is a significant health concern and is a major contributor to cardiovascular diseases. As a result, blood pressure readings, including systolic blood pressure (SBP) values and diastolic blood pressure (DBP) values, are important cardiovascular health indicators. Many approaches to blood pressure (BP) monitoring, such as oscillometric techniques and auscultatory techniques, rely on cuff-based devices to measure BP values. Although reliable, they are not suited for continuous monitoring and are not convenient to set up and use. These limitations have fueled research into more convenient, noninvasive methods, such as cuffless BP monitoring techniques. Many cuffless BP monitoring techniques leverage electrocardiogram (ECG) and photoplethysmogram (PPG) signals, but are constrained by the need for handcrafted feature extraction. This may limit the generalizability of such models to diverse populations. The above and other aspects, features, and advantages of certain embodiments of the disclosure will be more apparent from the following description taken in conjunction with the accompanying drawings, in which: FIG. 1 illustrates example components of an electronic device in accordance with some embodiments of the disclosure; FIG. 2A illustrates a first example (a pair of earbuds) of the electronic device in accordance with some embodiments of the disclosure; FIG. 2B illustrates example components of the first example of the electronic device in accordance with some embodiments of the disclosure; FIG. 3A illustrates a second example (a watch) of the electronic device in accordance with some embodiments of the disclosure; FIG. 3B illustrates example components of the second example of the electronic device in accordance with some embodiments of the disclosure; FIG. 4A illustrates a third example (a ring) of the electronic device in accordance with some embodiments of the disclosure; FIG. 4B illustrates example components of the third example of the electronic device in accordance with some embodiments of the disclosure; FIGS. 5A to 5E illustrate example structures of a sensor for detecting biometric information according to one or more example embodiments; FIG. 6 illustrates an example system having a fourth example (a computer) of the electronic device with remotely located electronic devices and their sensors in accordance with some embodiments of the disclosure; FIG. 7A illustrates an example structure of a model for performing cuffless BP monitoring in accordance with some embodiments of the disclosure; Fig. 7B is a flowchart illustrating an example process for performing cuffless BP monitoring in accordance with some embodiments of the disclosure; FIG. 8 illustrates an example overview of a training process for a model for performing cuffless BP monitoring in accordance with some embodiments of the disclosure; FIG. 9A illustrates an example training process for training a model for performing cuffless BP monitoring in accordance with some embodiments of the disclosure; Fig. 9B is a flowchart illustrating an example training process for training a model for performing cuffless BP monitoring in accordance with some embodiments of the disclosure; and Fig. 10 is a flowchart illustrating an example process for performing cuffless BP monitoring in accordance with some embodiments of the disclosure. The terms as used in the disclosure are provided to merely describe specific embodiments, not intended to limit the scope of other embodiments. Singular forms include plural referents unless the context clearly dictates otherwise. The terms and words as used herein, including technical or scientific terms, may have the same meanings as generally understood by those skilled in the art. The terms as generally defined in dictionaries may be interpreted as having the same or similar meanings as or to contextual meanings of the relevant art. Unless otherwise defined, the terms should not be interpreted as ideally or excessively formal meanings. Even though a term is defined in the disclosure, the term should not be interpreted as excluding embodiments of the disclosure under circumstances. The electronic device according to one or more embodiments may be one of various types of electronic devices. In some embodiments of the disclosure, the electronic devices may include a portable communication device (e.g., a smartphone), a computer device, a portable multimedia device, a portable medical device, a camera, or a wearable device. The electronic devices are not limited to those described above, in accordance with some other embodiments of the disclosure. The disclosure and the terms used therein are not intended to limit the technological features set forth herein to particular embodiments