Non-invasive cardiac health assessment system and method for training a model to estimate intracardiac pressure data
The present disclosure relates to cardiac health assessment system for use with a handheld electronic device for assessing cardiac health of a user and a method for assessing cardiac health of a user. The disclosure further relates to systems and methods for training a machine learning model to estimate intracardiac pressure data.
1 . A cardiac health assessment system for use with a handheld electronic device (HED) for assessing cardiac health of a user, characterized in the cardiac health assessment system comprising:
an electronic device case (EDC) having a shape adapted to secure the handheld electronic device with the EDC, wherein the EDC comprises a sensor signal-enhancing material to amplify auscultation signals; and
a circuit board configured within the EDC and electrically connected with a plurality of sensors, wherein the plurality of sensors comprise:
a plurality of soundwave transducers to capture cardiac audio signals indicative of the cardiac health of the user;
a plurality of photoplethysmography (PPG) sensors to capture visual data pertaining to tissue colour and/or blood flow;
a plurality of Inertial Measurement Unit (IMU) sensors to capture seismic and auscultation signals indicative of the cardiac health of the user; and
a microcontroller connected to the circuit board to transmit cardiac health data received from the plurality of sensors to a computing device over a network, wherein the computing device comprises a processor configured to:
receive, in one or more temporal windows, representations of one or more signals recorded by the EDC, of at least one of the plurality of IMU sensors, the plurality of PPG sensors, and the plurality of soundwave transducers;
detect features of the one or more signals from at least one or more portions of the received representations falling within each of the one or more temporal windows; and
identify patterns of the features of respective sensors from within the at least one or more portions based on at least a classification model or a regression model.
2 . The cardiac health assessment system according to claim 1 , wherein the processor is further configured to estimate, based on the regression model, intracardiac pressure and/or left ventricular ejection fraction.
3 . The cardiac health assessment system according to claim 1 , wherein the EDC is configured to capture cardiac health data of the user when positioned against the chest of the user.
4 . The cardiac health assessment system according to claim 1 , wherein the classification model is trained to detect irregularities from one or more of the following health conditions: ischemic cardiomyopathy, aortic stenosis, aortic regurgitation, mitral stenosis, and mitral regurgitation.
5 . The cardiac health assessment system according to claim 1 , wherein the EDC further comprises a temperature sensor configured to detect variations in chest skin surface temperature.
6 . The cardiac health assessment system according to claim 1 , wherein one or more soundwave transducers of the plurality of soundwave transducers are configured to transmit soundwaves into the user's body to deflect soundwaves arising from a plurality of physiological processes comprising intracardiac blood pressure and heart movements and return soundwave data to be analyzed with the classification model and the regression model.
7 . The cardiac health assessment system according to claim 1 , wherein the EDC further comprises a lens configured to envelop a camera of the handheld electronic device (HED), wherein the lens is configured to block external light when the HED shines a light onto skin of the user that is used to record one or more images thereof, wherein the one or more images are analyzed based on machine learning for providing insights into the cardiac health of the user.
8 . The cardiac health assessment system according to claim 1 , further comprising a separate handheld electronic device (HED), wirelessly connected with the handheld electronic device and comprising a HED wireless transceiver configured to establish a communication with the computing device to transmit cardiac health data therebetween, wherein the processor of the computing device is configured to:
detect, based on the classification model, an abnormal cardiac activity based on a plurality of parameters that includes one or more indications of hypertension, atrial fibrillation, and myocardial ischemia; and
estimate, based on the regression model, intracardiac pressure and/or left ventricular ejection fraction.
9 . The cardiac health assessment system according to claim 1 , further comprising a plurality of electrodes, wherein the plurality of electrodes comprise:
a first ECG electrode placed on an outer surface of the EDC; and
a second ECG electrode and a third electrode placed on each side of the EDC to facilitate a thumb and fingers of the user to be placed on the EDC having the shape that is adapted to secure the handheld electronic device, wherein the plurality of electrodes are configured to capture data indicative of the cardiac health of the user, wherein the processor is configured to transmit data indicative of cardiac function from the handheld electronic device to a clinician computing device over the network for remote diagnostic analysis using machine learning.
10 . The cardiac health assessment system according to claim 9 , further comprising a plurality of printed circuit boards (PCBs) to accommodate a plurality of sensing units with a plurality of dimensions.
11 . The cardiac health assessment system according to claim 1 , wherein the circuit board comprises a memory to store the classification model, the regression model, and a plurality of instructions pertaining to a cardiac monitoring application.
12 . The cardiac health assessment system according to claim 8 , wherein the processor is further configured to detect abnormal pulmonary health activity based on the plurality of parameters by deploying a pulmonary disease classification model.
13 . The cardiac health assessment system according to claim 1 , wherein the plurality of sensors further comprises a hydration monitoring sensor configured to compute a hydration metric of a body tissue of the user.
14 . The cardiac health assessment system according to claim 12 , wherein the processor is further configured to estimate lung fluid levels based on the plurality of parameters by deploying a lung fluid estimation model when the EDC has collected data from a thoracic region of the user.
15 . The cardiac health assessment system according to claim 1 , wherein the processor is further configured to compare an estimated intracardiac pressure with one or more earlier estimations of intracardiac pressure of the user.
16 . The cardiac health assessment system according to claim 1 , wherein the processor is further configured to estimate, based on the classification model, coronary artery disease risk.