Granted Patent B2

US 12,616,384 · App. 17/613,037 · Granted May 5, 2026

Inventors: Pengbo Liu (Shenzhen, CN); Shaochun Zhuang (Shenzhen, CN); Lingjun Zeng (Shenzhen, CN); Zhengpei Chu (Shenzhen, CN)

Assignee: CARDIOSTORY INC.

A61B5/021A61B5/11A61B5/318A61B2562/0219A61B2562/0247A61B2562/0261

Abstract

Claims (76)

1 . A diastolic function assessment method, performed by one or more processors executing one or more computer programs stored in a memory, comprising steps of:

non-invasively and continuously acquiring vibration information on a body surface corresponding to a thoracic cavity of a subject in a supine position through one or more fiber-optic sensors which are connected to the one or more processors; wherein the one or more fiber-optic sensors are configured to be placed under the subject's right shoulder and around the subject's right shoulder scapula, a sensing area of the one or more fiber-optic sensors is at least 20 square centimeters and covers the body surface area of the right shoulder scapula; optical fibers are distributed in the sensing area of the one or more fiber-optic sensors; the one or more fiber-optic sensors are sensitive to changes in vibration displacement; the vibration information contains breathing signals, hemodynamic signals, body motion signals and noise signals; a waveform of the vibration information includes breathing envelopes generated by the breathing signals; the hemodynamic signals, the body motion signals and the noise signals are superposed on the breathing envelopes; a horizontal axis of the waveform represents time, and a vertical axis represents normalized vibration information which is dimensionless;

preprocessing the vibration information to generate hemodynamic related information; comprising:

filtering the vibration information below 2 Hz to remove breathing signals and body motion signals and filtering the vibration information above 45 Hz to remove the noise signals, thereby generating the hemodynamic related information of 2-45 Hz;

performing a first-order differential processing on the hemodynamic related information to generate a first-order differential information with a frequency band of 2-45 Hz, performing a second-order differential processing on the hemodynamic related information to generate a second-order differential information with a frequency band of 2-45 Hz;

generating, by performing energy integration on the hemodynamic related information, vibration energy information comprising a first energy envelope and a second energy envelope which represent energy accumulation during a systolic process and an early diastole of the subject's heart;

synchronizing the hemodynamic related information, or the first-order differential information, or the second-order differential information, and the vibration energy information on the same time axis, and performing heartbeat segmentation;

determining, in one cardiac cycle, a highest peak of the hemodynamic related information, a highest peak of the first-order differential information, or a highest peak of the second-order differential information, wherein the highest peak of the hemodynamic related information or the first-order differential information or the second-order differential information represents a shock caused by blood flowing into an aortic arch after aortic ejection;

determining a target time window; wherein the first energy envelope contains the highest peak of the hemodynamic related information, or the highest peak of the first-order differential information, or the highest peak of the second-order differential information, while the second energy envelope does not have the highest peak of the hemodynamic related information, or the highest peak of the first-order differential information, or the highest peak of the second-order differential information; determine a time duration of the second energy envelope as the target time window;

determining, wave clusters within the target time window of the second-order differential information, as a target wave group;

determining a highest peak on the target wave group of the second-order differential information; determining a rising edge amplitude before the highest peak on the target wave group as a first characteristic value, and determining an amplitude between the same highest peak on the target wave group and a subsequent lowest valley on the target wave group as a second characteristic value;

generating an indicating parameter for assessing a diastolic function of the subject, comprising:

determining a ratio of the second characteristic value to the first characteristic value as the indicating parameter; and

determining an elevated filling pressure state if the indicating parameter is greater than a threshold;

wherein the threshold is 1.405 or depends on a specific people group; and

outputting, through an output device, a result of the assessing.

2 . The method of claim 1 , wherein the hemodynamic related information is:

data in one cardiac cycle; or

data that is superimposed and averaged in a unit of cardiac cycle within a preset time period.

3 . The method of claim 1 , wherein the step of performing heartbeat segmentation comprises:

performing the highest peak or a lowest valley search on the hemodynamic related information, or the first-order differential information, or the second-order differential information at a search interval between 0.6 seconds and 1 second; and

performing the heartbeat segmentation based on the repetitive highest peaks or the repetitive lowest valleys on the hemodynamic related information or the first-order differential information or the second-order differential information.

4 . The method of claim 1 , wherein the step of performing heartbeat segmentation comprises:

acquiring electrocardiography (ECG) data through an electrocardiogram sensor when acquiring the vibration information; and

performing heartbeat segmentation on the hemodynamic related information or the first-order differential information or the second-order differential information based on the ECG data.

5 . The method of claim 1 , wherein the step of filtering the vibration information uses one or more of low-pass filtering, band-pass filtering, Infinite Impulse Response (IIR) filtering, Finite Impulse Response (FIR) filtering, wavelet filtering, zero-phase bidirectional filtering, polynomial smoothing filtering, integral transformation, and differential transformation, to filter the vibration information at least once to generate the hemodynamic related information.

6 . A diastolic function assessment method, performed by one or more processors executing one or more computer programs stored in a memory, comprising steps of:

non-invasively and continuously acquiring the vibration information on the body surface corresponding to a thoracic cavity of a subject in a supine position through the one or more fiber-optic sensors configured to be placed under the subject's right shoulder and around a right shoulder scapula, a sensing area of the one or more fiber-optic sensors is at least 20 square centimeters and covers the body surface area of the right shoulder scapula of the subject; optical fibers are distributed in the sensing area of the one or more fiber-optic sensors;

preprocessing the vibration information to generate hemodynamic related information with a frequency band of 2-45 Hz; comprising: filtering, noise removal and signal scaling;

performing a first-order differential processing on the hemodynamic related information to generate a first-order differential information with a frequency band of 2-45 Hz, or, performing a second-order differential processing on the hemodynamic related information to generate a second-order differential information with a frequency band of 2-45 Hz;

generating, by performing energy integration on the hemodynamic related information, vibration energy information comprising a first energy envelope and a second energy envelope which represent energy accumulation during a systolic process and an early diastole of the subject's heart;

synchronizing the hemodynamic related information, or the first-order differential information, or the second-order differential information, and the vibration energy information on the same time axis, and performing heartbeat segmentation;

determining, in one cardiac cycle, a highest peak of the hemodynamic related information, or a highest peak of the first-order differential information, or a highest peak of the second-order differential information, wherein the highest peak of the hemodynamic related information or the first-order differential information or the second-order differential information represents a shock caused by blood flowing into an aortic arch after aortic ejection;

determining a target time window; wherein the first energy envelope contains the highest peak of the hemodynamic related information, or the highest peak of the first-order differential information, or the highest peak of the second-order differential information, while the second energy envelope does not have the highest peak of the hemodynamic related information, or the highest peak of the first-order differential information, or the highest peak of the second-order differential information; determine a time duration of the second energy envelope as the target time window; and

determining wave clusters within the target time window of the first-order differential information as a target wave group;

determining a highest peak on the target wave group of the first-order differential information; determining a rising edge amplitude before the highest peak on the target wave group as a first characteristic value, and determining an amplitude between the same highest peak on the target wave group and a subsequent lowest valley on the target wave group as a second characteristic value;

generating an indicating parameter for assessing a diastolic function of the subject, comprising:

determining a ratio of the second characteristic value to the first characteristic value as the indicating parameter; and

determining an elevated filling pressure state if the indicating parameter is greater than a threshold;

wherein the threshold is 1.294 or depends on a specific people group; and

outputting, through an output device, a result of the assessing.

7 . The diastolic function assessment method of claim 6 , wherein the step of performing heartbeat segmentation comprises:

performing the highest peak or a lowest valley search on the hemodynamic related information, or the first-order differential information, or the second-order differential information at a search interval between 0.6 seconds and 1 second; and

performing the heartbeat segmentation based on the repetitive highest peaks or the repetitive lowest valleys on the hemodynamic related information or the first-order differential information or the second-order differential information.

8 . The diastolic function assessment method of claim 6 , wherein the step of performing heartbeat segmentation comprises:

acquiring electrocardiography (ECG) data through an electrocardiogram sensor when acquiring the vibration information; and

performing heartbeat segmentation on the hemodynamic related information or the first-order differential information or the second-order differential information based on the ECG data.

9 . The diastolic function assessment method of claim 6 , wherein the step of filtering uses one or more of low-pass filtering, band-pass filtering, Infinite Impulse Response (IIR) filtering, Finite Impulse Response (FIR) filtering, wavelet filtering, zero-phase bidirectional filtering, polynomial smoothing filtering, integral transformation, and differential transformation, to filter the vibration information at least once to generate the hemodynamic related information.

10 . A diastolic function assessment system based on machine learning, comprising:

one or more processors, which are programmed to perform the steps of:

receiving vibration information on a body surface corresponding to a thoracic cavity of a subject in a supine position through one or more fiber-optic sensors which are connected to the one or more processors as input information for training;

analyzing the input information for training to establish an assessment model by machine learning, and;

receiving the vibration information on the body surface corresponding to the subject's thoracic cavity through the one or more fiber-optic sensors; and performing an assessment to the subject's diastolic function by the assessment model;

wherein the one or more fiber-optic sensors are configured to be placed under the subject's right shoulder and around the subject's right shoulder scapula, a sensing area of the one or more fiber-optic sensors is at least 20 square centimeters and covers the body surface area of the right shoulder scapula; optical fibers are distributed in the sensing area of the one or more fiber-optic sensors; the one or more fiber-optic sensors are sensitive to changes in vibration displacement; the vibration information contains breathing signals, hemodynamic signals, body motion signals and noise signals; a waveform of the vibration information includes breathing envelopes generated by the breathing signals; the hemodynamic signals, the body motion signals and the noise signals are superposed on the breathing envelopes; a horizontal axis of the waveform represents time, and a vertical axis represents normalized vibration information which is dimensionless;

wherein the assessment model performs steps of:

preprocessing the vibration information to generate hemodynamic related information; comprising:

filtering the vibration information below 2 Hz to remove breathing signals and body motion signals and filtering the vibration information above 45 Hz to remove the noise signals, thereby generating the hemodynamic related information of 2-45 Hz;

performing a first-order differential processing on the hemodynamic related information to generate a first-order differential information, or, performing a second-order differential processing on the hemodynamic related information to generate a second-order differential information;

generating, by performing energy integration on the hemodynamic related information, vibration energy information comprising a first energy envelope and a second energy envelope which represent energy accumulation during a systolic process and an early diastole of the subject's heart;

synchronizing the hemodynamic related information, or the first-order differential information, or the second-order differential information, and the vibration energy information on the same time axis, and performing heartbeat segmentation;

determining, in one cardiac cycle, a highest peak of the hemodynamic related information, or a highest peak of the first-order differential information, or a highest peak of the second-order differential information, wherein the highest peak of the hemodynamic related information or the first-order differential information or the second-order differential information represents a shock caused by blood flowing into an aortic arch after aortic ejection;

determining a target time window; wherein the first energy envelope contains the highest peak of the hemodynamic related information, or the highest peak of the first-order differential information, or the highest peak of the second-order differential information, while the second energy envelope does not have the highest peak of the hemodynamic related information, or the highest peak of the first-order differential information, or the highest peak of the second-order differential information; determine a time duration of the second energy envelope as the target time window; and

determining, wave clusters within the target time window of the first-order differential information or the second-order differential information, as a target wave group;

determining a highest peak on the target wave group of the first-order differential information or the second-order differential information; determining a rising edge amplitude before the highest peak on the same target wave group as a first characteristic value and determining an amplitude between the same highest peak on the same target wave group and a subsequent lowest valley on the same target wave group as a second characteristic value; and

generating an indicating parameter for assessing a diastolic function of the subject, comprising:

determining a ratio of the second characteristic value to the first characteristic value as the indicating parameter; and

determining an elevated filling pressure state if the indicating parameter is greater than a threshold;

wherein the threshold is 1.294 when determining the first characteristic value and the second characteristic value based on the first-order differential information; and the threshold is 1.405 when determining the first characteristic value and the second characteristic value based on the second-order differential information; or

the threshold depends on a specific people group.

11 . The diastolic function assessment system based on machine learning of claim 10 , wherein the step of performing heartbeat segmentation comprises:

performing the highest peak or a lowest valley search on the hemodynamic related information, or the first-order differential information, or the second-order differential information at a search interval between 0.6 seconds and 1 second; and

performing the heartbeat segmentation based on the repetitive highest peaks or the repetitive lowest valleys on the hemodynamic related information or the first-order differential information or the second-order differential information.

12 . The diastolic function assessment system based on machine learning of claim 10 , wherein the step of performing heartbeat segmentation comprises:

acquiring electrocardiography (ECG) data through an electrocardiogram sensor when acquiring the vibration information; and

performing heartbeat segmentation on the hemodynamic related information or the first-order differential information or the second-order differential information based on the ECG data.

13 . The diastolic function assessment system based on machine learning of claim 10 , wherein the step of filtering the vibration information uses one or more of low-pass filtering, band-pass filtering, Infinite Impulse Response (IIR) filtering, Finite Impulse Response (FIR) filtering, wavelet filtering, zero-phase bidirectional filtering, polynomial smoothing filtering, integral transformation, and differential transformation, to filter the vibration information at least once to generate the hemodynamic related information.

Assignments (2)

Continuity (1)

References Cited (23)