Photoplethysmography-based blood pressure monitoring device
A blood pressure monitoring device includes a wrist strap, a case, and a display. The strap portion is adapted to fasten the device to the wrist without occluding blood flow. At least one sensor modality is arranged within the case for obtaining sensor data from an artery in the wrist. In a preferred embodiment photoplethysmography sensors are incorporated in the case to generate PPG waveform data. A processor within the case is operable to compute mean arterial pressure, and optionally diastolic and systolic blood pressure based on extracted and computed features of the sensor data. Related methods and systems are also described.
1. A blood pressure monitoring device for computing mean arterial pressure of a user comprising:
a case and a strap adapted to hold the case against the wrist of the patient;
a first PPG sensor within the case and aimed at an artery in the wrist when the case is strapped to the wrist;
a second PPG sensor spaced from the first PPG sensor within the case and aimed at the artery in the wrist when the case is strapped to the wrist; and
a processor arranged within the case and operable to:
compute a plurality of features from PPG waveform data generated by the PPG sensors; and
compute the mean arterial pressure (MAP) based on the plurality of features; and wherein the processor is further programmed to, after the plurality of features are computed, assess signal quality wherein assessing signal quality comprises computing a reference template, comparing a beat morphology of each pulse to the reference template, identifying low-quality features based on the comparing step, and excluding the low-quality features from the plurality of features used in the MAP computing step.
2. The blood pressure monitoring device of claim 1 , wherein computing MAP is further based on a predetermined proportionality coefficient associated with the user, wherein the proportionality coefficient is initially computed and based on measuring blood pressure using a second type of blood pressure measurement device.
3. The blood pressure monitoring device of claim 1 , wherein one of the plurality of features comprises: diastolic velocity, systolic velocity, systolic volume, diastolic volume, diastolic distance, systolic distance, heart rate, diastolic time, and/or systolic time.
4. The blood pressure monitoring device of claim 1 , wherein the processor is further operable to compute diastolic blood pressure (DBP).
5. The blood pressure monitoring device of claim 1 , wherein the processor is further operable to compute systolic blood pressure based on the computed the MAP and DBP.
6. The blood pressure monitoring device of claim 1 , wherein the case further houses a battery, memory, and PPG electronics.
7. The blood pressure monitoring device of claim 1 , further comprising a trained machine learning model for determining the MAP based on the plurality of features extracted from the PPG waveform data.
8. The blood pressure monitoring device of claim 1 , further comprising a display, and wherein the strap, case and display collectively form a wrist watch-like shape.
9. The blood pressure monitoring device of claim 1 , further comprising a location module for alerting the user to an optimal position on the wrist to strap the case thereto as the user adjusts the position of the case along the user's wrist.
10. The blood pressure monitoring device of claim 1 , wherein the MAP computing is performed without use of ECG data.
11. The blood pressure monitoring device of claim 1 , wherein the processor is further programmed to compute a confining threshold BP range after the MAP computing step, and to recompute the MAP based on the confining threshold BP range.
12. The blood pressure monitoring device of claim 11 , wherein the processor is further programmed to determine the confining threshold BP range based on computing BP error and confidence level.
13. The blood pressure monitoring device of claim 1 , wherein the reference template is based on calculating a statistical value of the beat morphologies of all the pulses within a time window.
14. The blood pressure monitoring device of claim 13 , wherein the comparing a beat morphology of each pulse to the reference template comprises calculating a cross-correlation value between the beat morphology of each pulse and the reference template.
15. The blood pressure monitoring device of claim 14 , wherein the identifying low-quality features is based on whether the cross-correlation value is smaller than a threshold value.
16. A method for monitoring mean arterial pressure (MAP) of a person based on PPG data comprising:
arranging a first PPG sensor and a second PPG sensor on the skin, optionally a wrist, of the person, wherein the second PPG sensor is spaced a fixed distance from the first PPG sensor;
obtaining PPG data from the first PPG sensor and the second PPG sensor corresponding to blood flow in a vessel of the person;
extracting a plurality of features from the PPG data; and
computing the mean arterial pressure (MAP) based on the plurality of features; and wherein the method further comprising, after the extracting step, assessing the signal quality wherein assessing signal quality comprises computing a reference template, comparing a beat morphology of each pulse to the reference template, identifying low-quality features based on the comparing step, and excluding the low-quality features from the plurality of features used in the MAP computing step.
17. The method of claim 16 , further comprising sending the MAP to a portable computing device.
18. The method of claim 16 , wherein computing the MAP is performed without use of ECG data.
19. The method of claim 16 , further comprising computing diastolic blood pressure (DBP).
20. The method of claim 16 , further comprising computing systolic blood pressure.
21. The method of claim 16 , further comprising alerting the user to an optimal position for location of the first PPG sensor and second PPG sensor as the user adjusts and moves the first PPG sensor and second PPG sensor along the skin of the person.
22. The method of claim 16 , wherein one of the plurality of features comprises: diastolic velocity, systolic velocity, systolic volume, diastolic volume, diastolic distance, systolic distance, heart rate, diastolic time, and/or systolic time.
23. The method of claim 16 , wherein the step of computing MAP is performed using a BP algorithm, and wherein the BP algorithm is calibrated using an ancillary BP measuring device, optionally a pressure cuff-type device.
24. The method of claim 23 , wherein the computing MAP is performed compression-free subsequent to being calibrated.
25. The method of claim 23 , wherein the computing MAP is performed continuously subsequent to being calibrated.
26. The method of claim 16 , further comprising computing a confining threshold BP range after the MAP computing step, and recomputing the MAP based on the confining threshold BP range.
27. The method of claim 26 , wherein the confining threshold BP range is determined based on computing BP error and confidence level.
28. The method of claim 16 , wherein computing the reference template is performed by calculating a statistical value of the beat morphologies of all the pulses within a time window.
29. The method of claim 28 , wherein comparing a beat morphology of each pulse to the reference template comprises calculating a cross-correlation value between the beat morphology of each pulse and the reference template.
30. The method of claim 29 , wherein the identifying low-quality features is based on whether the cross-correlation value is smaller than a threshold value.
31. The method of claim 30 , wherein PPG data is collected for 1 minute, and the time window is 10 seconds.