Wearable health monitoring device
A wearable patient device is provided that includes one or more sensors. The one or more sensors can record one or both of ECG information or phonocardiographic information. The sensor information can be used to determine the blood pressure of a monitored individual, including on a continuous basis. Blood pressure can be determined using one or both of a determined time to empty or fill one or more heart chambers or first and second blood velocities. Vital sign information can be provided to a monitoring individual, including graphical representations of trend information.
1. A method comprising:
placing a monitoring device on a subject, the monitoring device comprising a housing having a front surface and a rear surface, a communication component, and at least one lead connecting at least a first hardware sensor to the monitoring device, wherein the rear surface and the at least one lead are secured to a body of the subject and the at least one hardware sensor comprises an ECG sensor or an acoustic sensor;
receiving data from the at least a first hardware sensor, the at least a first hardware sensor recording data associated with cardiac function of the subject;
from the data, determining a time taken to empty one or more chambers of the heart of blood to provide an empty time;
providing at least one heart dimension parameter, the at least one heart dimension parameter representing a physical dimension of a heart;
from the empty time and the at least one heart dimension parameter, determining a blood pressure value for the subject;
transmitting the blood pressure value or the data from the monitoring device to a provider device using the communication component; and
causing the blood pressure value to be output for display on the provider device to a monitoring individual.
2. The method of claim 1 , wherein the sensor data consists of ECG data.
3. The method of claim 1 , wherein the sensor data consists of phonocardiographic data.
4. The method of claim 1 , wherein the empty time is determined based on a difference between a time at which ventricular contraction begins and a time of onset of ventricular/atrial relaxation.
5. The method of claim 1 , wherein the blood pressure value is determined as:
Pressure
=
256
L
η
SV
3
4
π
r
4
T
Fill
where η is blood viscosity (in mmHg), L is the length of the aorta (m), SV is the stroke volume (m 3 ), r is the aortic radius (m), and T Fill is the ventricular empty time.
6. The method of claim 5 , further comprising applying a correction factor to the blood pressure value, the correction factor being determined using a machine learning algorithm.
7. The method of claim 1 , further comprising:
measuring acoustic signals produced by the subject's heart;
determining a stroke volume correction factor from the measured acoustic signals; and
applying the stroke volume correction factor to the blood pressure value.
8. A method comprising:
placing a monitoring device on a subject, the monitoring device comprising a housing having a front surface and a rear surface, a communication component, and at least one lead connecting at least a first hardware sensor to the monitoring device, wherein the rear surface and the at least one lead are secured to a body of the subject and the at least one hardware sensor comprises an ECG sensor or an acoustic sensor;
receiving data from the at least a first hardware sensor, the at least a first hardware sensor recording data associated with cardiac function of the subject;
by the monitoring device, from the data, determining a time taken to empty one or more chambers of the heart of blood to provide an empty time;
by the monitoring device, from the empty time and at least one heart dimension parameter stored by the monitoring device, the at least one heart dimension parameter representing a physical dimension of a heart, determining a blood pressure value for the subject;
transmitting the blood pressure value from the monitoring device to a provider device using the communication component; and
causing the blood pressure value to be output for display on the provider device to a monitoring individual.
9. The method of claim 8 , wherein the sensor data consists of ECG data.
10. The method of claim 8 , wherein the sensor data consists of phonocardiographic data.
11. The method of claim 8 , wherein the empty time is determined based on a difference between a time at which ventricular contraction begins and a time of onset of ventricular/atrial relaxation.
12. The method of claim 8 , wherein the blood pressure value is determined as:
Pressure
=
256
L
η
SV
3
4
π
r
4
T
Fill
where η is blood viscosity (in mmHg), L is the length of the aorta (m), SV is the stroke volume (m 3 ), r is the aortic radius (m), and T Fill is the ventricular empty time.
13. The method of claim 12 , further comprising applying a correction factor to the blood pressure value, the correction factor being determined using a machine learning algorithm.
14. The method of claim 8 , further comprising:
measuring acoustic signals produced by the subject's heart;
determining a stroke volume correction factor from the measured acoustic signals; and
applying the stroke volume correction factor to the blood pressure value.
15. A method comprising:
placing a monitoring device on a subject, the monitoring device comprising a housing having a front surface and a rear surface, a communication component, and at least one lead connecting at least a first hardware sensor to the monitoring device, wherein the rear surface and the at least one lead are secured to a body of the subject and the at least one hardware sensor comprises an ECG sensor or an acoustic sensor;
receiving data from the at least a first hardware sensor, the at least a first hardware sensor recording data associated with cardiac function of the subject;
transmitting the data from the monitoring device to a provider device using the communication component;
at the provider device, from the data, determining a time taken to empty one or more chambers of the heart of blood to provide an empty time;
at the provider device, from the empty time and at least one heart dimension parameter stored on the provider device, the at least one heart dimension parameter representing a physical dimension of a heart, determining a blood pressure value for the subject; and
causing the blood pressure value to be output for display on the provider device to a monitoring individual.
16. The method of claim 15 , wherein the sensor data consists of ECG data.
17. The method of claim 15 , wherein the sensor data consists of phonocardiographic data.
18. The method of claim 15 , wherein the empty time is determined based on a difference between a time at which ventricular contraction begins and a time of onset of ventricular/atrial relaxation.
19. The method of claim 15 , wherein the blood pressure value is determined as:
Pressure
=
256
L
η
SV
3
4
π
r
4
T
Fill
where η is blood viscosity (in mmHg), L is the length of the aorta (m), SV is the stroke volume (m 3 ), r is the aortic radius (m), and T Fill is the ventricular empty time.
20. The method of claim 19 , further comprising applying a correction factor to the blood pressure value, the correction factor being determined using a machine learning algorithm.
21. The method of claim 15 , further comprising:
measuring acoustic signals produced by the subject's heart;
determining a stroke volume correction factor from the measured acoustic signals; and
applying the stroke volume correction factor to the blood pressure value.