Systems and methods for computationally efficient non-invasive blood quality measurement
A system includes a wearable member configured to receive first energy, generate first signals, select a first subset of the time series data, determine a standard deviation indicating a quality at a low percentile of values associated with first windows of the time series data and a high percentile of values associated with the first windows of the time series data, the signal assessment threshold being including at least the values of the lower percentile, receive second energy, generate second signals containing second time series data from the second energy, select a second subset of the time series data to assess, compare all or part of the second signals to the signal assessment threshold, and if all or part of the second signals include the second time series data that is within the values of the lower percentile, then remove the second signals from further processing.
1. A wearable medical device for monitoring at least one physiological parameter of a patient with improved accuracy by removing noisy signals, comprising:
an energy transmitter disposed in the wearable medical device configured to project energy toward tissue of a patient;
an energy receiver disposed in the wearable medical device configured to detect energy received from the tissue of the patient and generate at least one signal based on the detected energy, the at least one signal comprising time series data;
a processor and associated memory, the memory configured to store executable instructions that are executed by the processor, the instructions being executable to:
receive a first signal from the energy receiver,
identify a first at least one window of the first signal including first time series data,
evaluate a variation among the first time series data in the first at least one window,
create an assessment threshold based on the variation of the first time series data in the first at least one window,
receive a second signal from the energy receiver,
identify a second at least one window of the second signal including second time series data,
compare the second time series data against the assessment threshold, and
remove the second signal from further processing if the second time series data does not pass the assessment threshold.
2. The wearable medical device of claim 1 , wherein the first at least one window includes separate windows for validating red and infrared PPG signals, respectively.
3. The wearable medical device of claim 1 , wherein the assessment threshold comprises at least one of a predetermined low percentile of variability and a predetermined high percentile of variability such that if the second time series data is below the low percentile of variability or above the high percentile of variability, the second signal is removed from further processing.
4. The wearable medical device of claim 3 , wherein the predetermined low and high percentiles of variability are determined by calculating a standard deviation of values in the second at least one window.
5. The wearable medical device of claim 1 , wherein the assessment threshold is re-created based on an evaluation of a variation of the second time series data.
6. The wearable medical device of claim 5 , wherein the assessment threshold is re-created periodically.
7. The wearable medical device of claim 5 , wherein the assessment threshold is re-created if a change of standard deviation of the second time series data exceeds a predetermined amount.
8. The wearable medical device of claim 1 , wherein the first and second signals comprise red and infrared PPG signals.
9. The wearable medical device of claim 1 , wherein the wearable medical device is a wrist-worn device.
10. A method for monitoring at least one physiological parameter of a patient with improved accuracy by removing noisy signals based on signals detected by a wearable medical device, the method comprising:
projecting energy toward tissue of a patient with an energy transmitter disposed in the wearable medical device;
detecting energy received from the tissue of the patient and generating at least one signal based on the detected energy with an energy receiver disposed in the wearable medical device, the at least one signal comprising time series data;
receiving a first signal from the energy receiver by a processor disposed in the wearable medical device;
identifying a first at least one window of the first signal including first time series data;
evaluating a variation among the first time series data in the first at least one window;
creating an assessment threshold based on the variation of the first time series data in the first at least one window;
receiving a second signal from the energy receiver by the processor;
identifying a second at least one window of the second signal including second time series data;
comparing the second time series data against the assessment threshold; and
removing the second signal from further processing if the second time series data does not pass the assessment threshold.
11. The method of claim 10 , wherein the first at least one window includes separate windows for validating red and infrared PPG signals, respectively.
12. The method of claim 10 , wherein the assessment threshold comprises at least one of a predetermined low percentile of variability and a predetermined high percentile of variability such that if the second time series data is below the low percentile of variability or above the high percentile of variability, the second signal is removed from further processing.
13. The method of claim 12 , wherein the predetermined low and high percentiles of variability are determined by calculating a standard deviation of values in the second at least one window.
14. The method of claim 10 , wherein the assessment threshold is re-created based on an evaluation of a variation of the second time series data.
15. The method of claim 14 , wherein the assessment threshold is re-created periodically.
16. The method of claim 14 , wherein the assessment threshold is re-created if a change of standard deviation of the second time series data exceeds a predetermined amount.
17. A computer program product for operating a wearable medical device comprising at least one non-transitory computer-readable medium including one or more instructions that, when executed by at least one processor, cause the at least one processor to:
receive a first signal detected by an energy receiver of the wearable medical device, wherein the first signal is based on energy projected toward tissue of a patient and received from the tissue of the patient by the energy receiver;
identify a first at least one window of the first signal including first time series data;
evaluate a variation among the first time series data in the first at least one window;
create an assessment threshold based on the variation of the first time series data in the first at least one window;
receive a second signal from the energy receiver, wherein the second signal is based on energy projected toward the tissue of the patient and received from the tissue of the patient by the energy receiver;
identify a second at least one window of the second signal including second time series data;
compare the second time series data against the assessment threshold; and
remove the second signal from further processing if the second time series data does not pass the assessment threshold.
18. The computer program product of claim 17 , wherein the first at least one window includes separate windows for validating red and infrared PPG signals, respectively.
19. The computer program product of claim 17 , wherein the assessment threshold comprises at least one of a predetermined low percentile of variability and a predetermined high percentile of variability such that if the second time series data is below the low percentile of variability or above the high percentile of variability, the second signal is removed from further processing.
20. The computer program product of claim 19 , wherein the predetermined low and high percentiles of variability are determined by calculating a standard deviation of values in the second at least one window.
21. The computer program product of claim 17 , wherein the assessment threshold is re-created based on an evaluation of a variation of the second time series data.
22. The computer program product of claim 21 , wherein the assessment threshold is re-created periodically.
23. The computer program product of claim 21 , wherein the assessment threshold is re-created if a change of standard deviation of the second time series data exceeds a predetermined amount.