O FFLINE P ROCESSING

Raw body acceleration and PPG data were collected by LabVIEW and stored in a file

Statistical analyses were performed using the SAS software environment.

8.3.1.EXTRACTION OF SpO2 AND HRMEASUREMENTS

Algorithms for SpO2 and HR extraction algorithms were written in Matlab based on

software described by Johnston [12].

SpO2 Readings

The SpO2 Differential algorithm developed by Johnston [12] incorporated a routine to

reject PPG signal derivatives that were equivalent to 0. In an effort to reduce the effects

of motion artifacts, the SpO2 Differential algorithm was modified to remove derivative

values that were less than a threshold value. The threshold was based on the mean signal

derivative value of the AC component of the PPG signals. The modified software was

designed to reject outlying signal derivative values since erroneous signal derivatives

occurred during motion.

Averaging of HR Readings

HR readings were obtained in software by averaging several IHR measurements. The

Peak Count-Based Averaging scheme that was devised by Johnston [12] was dependent

on IHR peak count. Therefore, fluctuations in HR level caused variations in averaging

window size which resulted in inconsistent HR measurements. This averaging routine

was modified by implementing the Time-Based Averaging algorithm which was based on

a constant-length time averaging window. This modification was implemented in order to

R and IR Based HR Algorithm

The Signal Derivative algorithm devised by Johnston [12] extracted HR readings from

PPG signals by processing the AC component of the IR signal. In order to obtain

potentially more accurate HR readings during jogging, this algorithm was modified to

incorporate the AC component of the R signal. The detection of multiple peaks as well as

missed peaks contributes to inaccurate HR readings. The R and IR Based HR Algorithm

modification was based on the rejection of outlying IHR values measured from either the

R or IR signals. For instance, an IHR measurement extracted from the R signal would be

incorporated in the average HR reading unless the value differed significantly from the

IHR measurement obtained from the IR signal and visa versa.

IHR Threshold

An additional modification was incorporated the calculation of HR in order to extract

more accurate HR readings. Similar to the R and IR Based HR Algorithm, this algorithm

change consisted of calculating more accurate HR readings by removing significantly

outlying IHR values. A threshold based on the average HR reading was utilized to

classify outlying IHR readings. Therefore, if an IHR value was significantly lower or

higher than the average HR reading, the IHR value would not be incorporated in the

calculation of HR.

In addition to implementing the modifications to the SpO2 and HR algorithms, ACC

and PPG data were analyzed to determine the efficacy of obtaining more accurate

readings by making use of ANC software available in Matlab.

8.3.2.ACCELEROMETER-BASED ADAPTIVE NOISE CANCELLATION

offline in Matlab. Analyses included testing the effects of filter order as well as step-size

and forgetting factor parameters of LMS and RLS adaptive algorithms, respectively. Root

mean squared errors (RMSE) were calculated as the mean difference between the

reference measurement and measurement obtained from the custom pulse oximeter

during jogging; RMSE were used as an indication of the performance of the adaptive

filter algorithms.

Accelerometer Axis Selection as a Noise Reference Input Signal

Experiments were performed to determine which axis of the ACC could provide the most

appropriate noise reference input to reduce the effects of motion artifacts. The orthogonal

X, Y, and Z axes, as well as the linear summation (X+Y+Z), were processed as noise

reference inputs of the acceleration axes.

Filter Optimization

The step-size (µ) and filter order (M) of the LMS adaptive filter were varied separately to

determine the optimal filter parameters. Likewise, the forgetting factor (λ) and filter order

(M) were varied to independently determine the optimal parameters of the RLS filter.

Adaptive filter order affects the execution time, learning rate, and RMSE. Separate

studies were performed to determine the optimal type and order for the adaptive filter

algorithms by implementing these algorithms inside the embedded µC.

8.3.3.ACC AND PPGSIGNAL TIME DIFFERENCE

It has been suggested in the literature that significant time differences exist between

body acceleration and PPG signals [59]. The effect of time delays between ACC and PPG

for various time-shifts. To determine the potential effect of time lags between ACC and

PPG signals could have on measurement accuracy, SpO2 and HR measurements were

calculated for the time-shifted signals.

8.3.4.ACCLOCATION

In order to obtain a reference signal for the ACC-based ANC algorithm, an ACC was

incorporated into the forehead-worn PPG sensor. A study was not performed to

determine the effect of ACC placement on various body locations since an inherent

assumption of the ACC-based ANC algorithm is that signal corruption is due to local

perturbations at the skin-sensor interface.