Estimating blood pressure of a subject using an ecg driven cardiovascular model

Patent metadata

Field	Value
Publication number	US-2023397822-A1
Application number	US-202318332552-A
Country	US
Kind code	A1
Filing date	Jun 9, 2023
Priority date	Jun 10, 2022
Publication date	Dec 14, 2023
Grant date	—

Abstract

This disclosure relates generally to in-silico modeling of hemodynamic patterns of physiologic blood flow. Conventional cardiovascular hemodynamic models depend on neuromodulation schemes (baroreflex autoregulation) and threshold parameters of neuromodulation correlate with physical activities. Thus these models may not work practically for a large set of people due to dependency on prior knowledge of these parameters. The present disclosure enables estimating blood pressure of a subject by estimating cardiac parameters based on the morphology of ECG signal associated with the subject and hence activation delays in cardiac chambers of the in-silico model is reproduced purposefully. In accordance with the present disclosure, the blood pressure of the subject can be estimated using only the ECG signal even if the signal is missed for some time instance(s) or is noisy.

First claim

What is claimed is:

1 . A processor implemented method comprising:
estimating, by an in-silico cardiovascular hemodynamic model via one or more hardware processors, in each cardiac cycle of an electrocardiogram (ECG) signal, cardiac parameters based on morphology of the ECG signal associated with a subject, wherein the cardiac parameters include a continuous heart rate (HR) and a set of compliance parameters, and wherein estimating the set of compliance parameters is based on: (i) a set of PQRST amplitudes; and (ii) time-instances, ([(α p j , t p j ), (α q j , t q j ), (α r j , t r j ), (α s j , t s j ), (α t j , t t j )]; j∈m) for a j th cardiac cycle (∀j∈m) of the ECG signal; generating, by the in-silico cardiovascular hemodynamic model via the one or more hardware processors, a set of compliance functions using the estimated cardiac parameters; sequentially activating, by the in-silico cardiovascular hemodynamic model via the one or more hardware processors, a plurality of cardiac chambers, in a synchronized manner, using the generated set of compliance functions; and estimating blood pressure of the subject, by the in-silico cardiovascular hemodynamic model via the one or more hardware processors, wherein the in-silico cardiovascular hemodynamic model is driven by the ECG signal associated with the subject.

2 . The processor implemented method of claim 1 , wherein the continuous HR is estimated based on the HR associated with a noise-less ECG signal, when the ECG signal is missing or is noisy, and is represented as:

HR
⁡
(
t
)

=

{

h

a
⁢
e

⁢

(

e
⁢
n
⁢
d

)

+

w
⁢

(
t
)

during
⁢

res
⁢
t
⁢
i
⁢
n
⁢
g
⁢

s
⁢
t
⁢
a
⁢
t
⁢
e

[

1
⁢

-

(

t
+
1

)

⁢

e

-

t

τ
k

]

⁢

h

α
⁢
e

(
0
)

+

w
⁡
(
t
)

during
⁢

exe
⁢
r
⁢
c
⁢
i
⁢
s
⁢
i
⁢
n
⁢
g
⁢

s
⁢
t
⁢
a
⁢
t
⁢
e

where, h ae (end), h ae (0) are the HRs at a last and a first instance of capturing the ECG signal respectively, w(t)= (0,σ 2 ) is white-noise with zero-mean, variance of σ 2 =9.26, and τ k =50 sec defines the time constant. σ 2 , and τ k are learnt empirically through the ECG signal using linear regression.

3 . The processor implemented method of claim 1 , wherein the set of compliance parameters of the j th cardiac cycle is estimated as

T
a
j

=

t
p
j

,

d
la
j

=

g

Assignees

• Tata Consultancy Services Ltd

Inventors

• Roy Dibyendu
• Mazumder Oishee
• Sinha Aniruddha
• Khandelwal Sundeep
• Ghose Avik

Classifications

• A61B5/02028 Primary

  Determining haemodynamic parameters not otherwise provided for, e.g. cardiac contractility or left ventricular ejection fraction · CPC title

• A61B5/366

  Detecting abnormal QRS complex, e.g. widening · CPC title

• A61B5/02405

  Determining heart rate variability · CPC title

• A61B5/02108

  from analysis of pulse wave characteristics · CPC title

• A61B5/7278 Primary

  Artificial waveform generation or derivation, e.g. synthesizing signals from measured signals · CPC title