Supplementary Materials: Blood Glucose Level Regression for Smartphone PPG Signals Using Machine Learning - MDPI
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Supplementary Materials: Blood Glucose Level Regression for
Smartphone PPG Signals Using Machine Learning
Tanvir Tazul Islam, Md Sajid Ahmed, Md Hassanuzzaman, Syed Athar Bin Amir and Tanzilur Rahman *
An overview of the experimental details is provided in Figure S1. Video data from
five different subjects (aged between 22 to 35) using three different smartphones has been
collected to observe the variations in acquired signal caused by the subjects and quality of
data acquisition system (phone + camera). Three trials from each subject were collected –
giving a total of 45 trials to test the proposed model responsible for data preprocessing
and feature analysis. All the trials after converting into their respective PPG signals have
gone through same preprocessing and feature analysis techniques irrespective of the sub-
ject and phone camera type used in acquisition.
Figure S1. (Left) Proposed Model Flow Diagram and (Right) Data Acquisition procedure using
smartphone camera.
The following Figures shows the complete preprocessing method from video to PPG
conversion, then baseline correction step, smoothing step and automatic detection of
peaks from the signal. The peaks function takes a one-dimensional array and finds all the
local maxima by simple comparison of neighboring values in the PPG signal.
Appl. Sci. 2021, 11, 618. https://doi.org/10.3390/app11020618 www.mdpi.com/journal/applsci
Appl. Sci. 2021, 11, 618 2 of 5
Figure S2. (Left) Corrected baseline PPG Signal of 5 Different Subjects from SP3 (a) Sub-1 (b) Sub-2 (c) Sub-3 (d) Sub-4 (e)
Sub-5. (Right) Complete Procedure from Raw PPG Signal to Feature Extraction; (a) Raw PPG (b) Corrected Baseline ALS
and Savitzky-Golay filter (c) Gaussian Smoothing Applied (d) Feature Analysis (Peak-Detection).
Figure S3. Feature analysis (Peak-Detection) (a) SP1 Signal Peaks (b) SP2 Signal Peaks (c) SP3 Sig-
nal Peaks.
Figure S4. Extracting Features(peak-detection) after being preprocessed with 1st and 2nd order
derivatives.
We used the baseline corrected and smoothed signal for detecting peaks. Optionally,
a subset of these peaks can be selected by specifying conditions for a peak’s properties.
The PPG signals were further analyzed through 1st and 2nd derivative feature extraction
which proved to be key in highly accurate glucose level estimation in our previous work
[1]. Figure S4 shows the peak analysis done on 1st and 2nd derivative signal of SP3. From
Table S1, it can be seen the model is able to detect the 2nd derivative peak with very few
misses and false positives across all the subjects.
Appl. Sci. 2021, 11, 618 3 of 5
Table S1. 2nd Derivative Features Extracted from PPG signals acquired (One Plus 6T).
2nd Deriva-
tive TP
Subjects Trials Miss FP
Detected
Peaks
1st 156 out of 157 156 1 0
Subject 1 2nd 161 out of 161 161 0 0
3rd 160 out of 162 160 2 0
1st 160 out of 162 160 2 0
Subject 2 2nd 162 out of 162 162 0 0
3rd 164 out of 165 164 1 0
1st 175 out of 178 175 3 0
Subject 3 2nd 179 out of 179 179 0 0
3rd 188 out of 190 188 2 0
1st 144 out of 146 144 2 0
Subject 4 2nd 136 out of 138 136 2 0
3rd 151 out of 153 151 2 0
1st 165 out of 167 165 3 1
Subject 5 2nd 164 out of 168 164 4 2
3rd 165 out of 166 165 1 0
Figure S5. (Top Left) Intentional finger movement at 20, 30 and 40 seconds, (Top Right) PPG sig-
nalacquired while flash on, (Bottom Left) PPG signal acquired whilethe flash is off, (Bottom
Right) PPG signal acquired and extracted from Red Channel.
Appl. Sci. 2021, 11, 618 4 of 5
Figure S6. (a) A sample Raw PPG signal with high-frequency noises and (b) its filtered version
through Gaussian Filter, baseline issues are still evident.
Figure S7. (a) A sample Raw PPG signal with high baseline variations and (b) its ALS corrected
version no baseline problems, high frequency noises are still evident.
Cross Validation (CV) Experiment
We performed K-Fold cross validation experiments on all the models presented in
the manuscript. Statistical approaches i.e., PCR and PLS models were built with different
feature sets for 5-fold and 10-fold cross validations. The lowest SEP among the different
PCR models was recorded with 35.30 mg/dL and 32.99 mg/dL for 5-fold and 10-fold CVs
respectively. In both cases (K = 5, 10) the minimum SEPs were found for PCR model built
with DelT features. As for PLS models, the lowest SEPs were recorded as 34.33 mg/dL and
33.82 mg/dL for 5-fold and 10-fold CVs respectively. The detail results are provided in
Table S2.
Table S2. K-Fold CV of different approaches for PCR and PLS.
PCR PLS
Appl. Sci. 2021, 11, 618 5 of 5
Features 5-Fold 10-Fold 5-Fold 10-Fold
SEP (mg/dL) 33.30 32.99 34.33 34.18
DelT
No. of PC 3 7 2 2
SEP (mg/dL) 34.22 34.27 37.86 36.96
1st Derivative
No. of PC 7 10 2 2
1st Derivative SEP (mg/dL) 35.36 33.91 35.80 34.87
Characteristics
No. of PC 2 2 2 2
Points
2nd Deriva- SEP (mg/dL) 34.43 33.69 39.49 38.88
tive No. of PC 12 14 2 2
2nd Deriva- SEP (mg/dL) 33.99 33.45 34.38 33.82
tive Charac-
No. of PC 15 12 2 2
teristics Points
Similar experiments were also performed on the Machine Learning Models (SVR and
RF). With SVR models optimized by tuning the epsilon value, we achieved lowest SEP of
28.99 mg/dL for the model trained with 1st Derivative features. As for RFR models opti-
mized through tuning the number of trees, we achieved lowest SEP of 28.5 mg/dL Results
are detailed in the in Table S3.
Table S3. K-Fold CV of different approaches for SVR and RFR.
SVR RFR
Features 5-Fold 10-Fold 5-Fold 10-Fold
1st Derivative 28.99 29.01 28.56 28.56
2nd Derivative 39.76 39.61 36.47 36.6
2nd Derivative
Characteristics 39.76 39.61 38.17 37.93
Points
Reference
1. Chowdhury, T.T.; Mishma, T.; Osman, S.; Rahman, T. Estimation of blood glucose level of type-2 diabetes patients using
smartphone video through PCA-DA. Proceedings of the 6th International Conference on Networking, Systems and Security—
NSysS ’19, Dhaka, Bangladesh, 17–19 December 2019, 104–108, doi: 10.1145/3362966.3362983.
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