System and methods for adaptive noise quantification in dynamic biosignal analysis
An adaptive noise quantification system and associated methods are disclosed for use in the dynamic biosignal analysis of a user. In at least one embodiment, the system includes a biosignal sensor positioned and configured for obtaining and transmitting data related to a select at least one vital of the user as a biosignal, and a motion sensor positioned and configured for obtaining and transmitting data related to a motion level of the user as a motion signal. A computing device is configured for receiving and processing the biosignal and motion signal.
1. A method for adaptive noise quantification in dynamic biosignal analysis of a user, the method comprising the steps of:
capturing, via an at least one motion sensor, an at least one motion signal containing data related to a motion level of the user, including accelerations in each of an orthogonal X direction (“ACC x ”), Y direction (“ACC y ”) and Z direction (“ACC z ”);
the at least one motion sensor transmitting the at least one motion signal to a computing device;
capturing, via an at least one biosignal sensor, an at least one biosignal containing data related to a select at least one vital of the user;
the at least one biosignal sensor transmitting the at least one biosignal to the computing device;
the computing device calculating at least one of a motion index (“MI”) and a median absolute deviation (“MAD”) based on the at least one motion signal, wherein the motion index is calculated using the formula
MI
=
∑
i
=
1
n
ACC
→
n
,
ACC
→
=
ACC
x
2
+
ACC
y
2
+
ACC
z
2
,
and wherein the median absolute deviation is calculated using the formula
MAD
=
∑
i
=
1
n
ACC
→
i
-
ACC
→
_
n
,
ACC
→
_
=
modulus
average
value
;
the computing device determining a motion stage of the user based on at least one of the motion index and median absolute deviation;
the computing device extracting the select at least one vital from the biosignal;
the computing device calculating, for each select at least one vital, a noise descriptor set comprising at least one of a morphological descriptor set and an environmental descriptor set;
the computing device generating a noise level estimation of the select at least one vital based on at least one of the noise descriptor set and the motion stage of the user; and
the computing device calculating a noise level of the biosignal based on the noise level estimation of the select at least one vital.
2. The method of claim 1 , further comprising the step of positioning the at least one motion sensor on a wearable device worn by the user.
3. The method of claim 2 , wherein the step of positioning the at least one motion sensor further comprises the step of positioning an at least one tri-axial accelerometer on the wearable device worn by the user.
4. The method of claim 1 , further comprising the step of positioning the at least one biosignal sensor on a wearable device worn by the user.
5. The method of claim 4 , wherein the step of positioning the at least one biosignal sensor further comprises the step of positioning an at least one electrocardiogram sensor on the wearable device worn by the user.
6. The method of claim 1 , further comprising the step of the computing device grouping the at least one motion signal based on motion stages, thereby allowing the computing device to further estimate, identify and separate out noise while performing dynamic biosignal analysis.
7. The method of claim 1 , further comprising the step of the computing device removing any baseline, power line, and high frequency interferences in the biosignal.
8. The method of claim 1 , further comprising the step of the computing device selecting a noise estimator, based on the motion stage of the user, for generating the noise level estimation of the biosignal.
9. A method for adaptive noise quantification in dynamic biosignal analysis of a user, the method comprising the steps of:
implementing each of a biosignal processing module and a biosignal analysis module in memory on a computing device;
capturing, via an at least one motion sensor, an at least one motion signal containing data related to a motion level of the user, including accelerations in each of an orthogonal X direction (“ACC x ”), direction (“ACC y ”) and Z direction (“ACC z ”);
the at least one motion sensor transmitting the at least one motion signal to a computing device;
capturing, via an at least one biosignal sensor, an at least one biosignal containing data related to a select at least one vital of the user;
the at least one biosignal sensor transmitting the at least one biosignal to the computing device;
the biosignal processing module extracting the select at least one vital from the biosignal;
the biosignal processing module calculating at least one of a motion index (“MI”) and a median absolute deviation (“MAD”) based on the at least one motion signal, wherein the motion index is calculated using the formula
MI
=
∑
i
=
1
n
ACC
→
n
,
ACC
→
=
ACC
x
2
+
ACC
y
2
+
ACC
z
2
,
and wherein the median absolute deviation is calculated using the formula
MAD
=
∑
i
=
1
n
ACC
→
i
-
ACC
→
_
n
,
ACC
→
_
=
modulus
average
value
;
the biosignal processing module determining a motion stage of the user based on at least one of the motion index and median absolute deviation;
the biosignal analysis module calculating a noise descriptor set comprising at least of one of a morphological descriptor set and an environmental descriptor set;
the biosignal analysis module generating a noise level estimation of the select at least one vital based on the noise descriptor set and the motion stage of the user; and
the biosignal analysis module calculating a noise level of the biosignal based on the noise level estimation of the select at least one vital.
10. The method of claim 9 , further comprising the steps of:
implementing a biosignal preprocessing module in memory on a computing device; and
the biosignal preprocessing module removing any baseline, power line, and high frequency interferences in the biosignal.
11. The method of claim 9 , further comprising the step of the biosignal analysis module selecting a noise estimator, based on the motion stage of the user, for generating the noise level estimation of the biosignal.
12. An adaptive noise quantification system for use in dynamic biosignal analysis of a user, the system comprising:
an at least one biosignal sensor positioned and configured for obtaining data related to a select at least one vital of the user, and transmitting said data as an at least one biosignal;
an at least one motion sensor positioned and configured for obtaining data related to an motion level of the user, and transmitting said data as an at least one motion signal, said data including accelerations in each of an orthogonal X direction (“ACC x ”), Y direction (“ACC y ”) and Z direction (“ACC z ”);
an at least one computing device configured for receiving and processing the at least one biosignal and motion signal;
a biosignal processing module residing in memory on the computing device and configured for extracting the select at least one vital from the biosignal, calculating at least one of a motion index (“MI”) and a median absolute deviation (“MAD”) based on the at least one motion signal, wherein the motion index is calculated using the formula
MI
=
∑
i
=
1
n
ACC
→
n
,
ACC
→
=
ACC
x
2
+
ACC
y
2
+
ACC
z
2
,
and wherein the median absolute deviation is calculated using the formula
MAD
=
∑
i
=
1
n
ACC
→
i
-
ACC
→
_
n
,
ACC
→
_
=
modulus
average
value
,
and determining a current motion stage of the user based on at least one of the motion index and median absolute deviation; and
a biosignal analysis module residing in memory on the computing device and configured for calculating a noise descriptor set, comprising at least one of a morphological descriptor set and an environmental descriptor set.
13. The adaptive noise quantification system of claim 12 , wherein the at least one biosignal sensor is an electrocardiogram sensor positioned and configured for obtaining data related to a heart activity of the user.
14. The adaptive noise quantification system of claim 12 , wherein the at least one motion sensor is a tri-axial accelerometer.
15. The adaptive noise quantification system of claim 12 , wherein the at least one biosignal sensor is positioned on a wearable device worn by the user.
16. The adaptive noise quantification system of claim 12 , wherein the at least one motion sensor is positioned on a wearable device worn by the user.
17. The adaptive noise quantification system of claim 12 , wherein the at least one biosignal sensor and at least one motion sensor are integrated in an at least one sensor array.
18. The adaptive noise quantification system of claim 12 , further comprising a biosignal preprocessing module residing in memory on the computing device and configured for discarding select portions of the biosignal.