● Developed a non-invasive blood glucose measurement method by using PPG and ECG signals instead of blood samples ● Proposed a CNN architecture to process raw PPG and ECG signals, and this CNN model achieves 91.9% of zone A (others in B) ● Utilized the flattened output of CNN as the input to XGBoost, and the combined model achieves 93.5% of zone A (others in B) ● We tried to aggregate signals in different frequencies, but the proposed multi-scale CNN model cannot outperform the above models 

1. Non-invasive Blood Glucose Measurement
using PPG and ECG Signals
Kuan-Ting Liu
and
Chun-Ming Chang
twcmchang@gmail.com
Working with Research Center for Applied Sciences, Academia Sinica
Updated at Jan 18, 2018

2. Motivation
● Traditional measurement methods need a blood
sample to analyze, but there are several cons
○ Potential transmission of infectious diseases
○ Need trained personnel
○ Painful and stressful
● This work aims at developing a non-invasive blood
glucose measurement method by only using PPG and
ECG signals instead of blood samples
2

3. PPG (photoplethysmogram) Signal
heart rate
blood pressure
3

4. Electrocardiogram (ECG) Signal
● Here we collect one-lead ECG signal
4

5. Interface
● Sample frequency: 1000 Hz
● Sample time: 60 seconds
● Five signal channels
1. ECG
2. Left-IR
3. Left-R
4. Right-IR
5. Right-R
5

6. Dataset
● For a subject, we collect
a. Two 60-second samples of both PPG and ECG signals
■ Sample frequency = 1000 Hz
■ 60 seconds break between the two samples
b. Profiling information like age, gender, height, weight and so on
c. Present blood glucose concentration, noted as G, in unit of mg/dL
● Total 876 people and 1752 samples (until 2017/09/01)
6

7. Preprocessing: Data Cleaning
● Remove outliers whose G > 200 but not diabetic
● Chop off the first two seconds and the last three
seconds to avoid noises during wearing devices
2 sec 3 sec
7

8. Preprocessing: Signal Filtering
AC+DC
(original)
AC only
High-pass
filter
8

9. Preprocessing: Signal Processing
● Concatenate AC-only signals (10 channels in total)
● Downsample to 200 Hz or 50 Hz to reduce input
dimension (scipy.signal)
● Normalize signals of each channel using robust scaler
(sklearn.preprocessing)
9

10. Preprocessing: Data Augmentation
● Randomly crop five 30-second signals from each
sample as a kind of data augmentation
● Split all subjects into two groups by a threshold, G=200
● Randomly sample 80% data as training from each
group, and the remaining as testing
10
Inputs: 30 seconds signal with 10 channels
in frequency of 200 Hz or 50 Hz

11. ● Reference the architecture proposed by [1]
● Use 11 residual blocks (23 conv layers)
● All conv layers have a filter length of 16
● The number of filers is 64*k, where k starts from 1
and is incremented every 4-th residual block
● In every alternate residual block, we subsamples its
inputs by a factor of 2
● Replace all BN + ReLU by SeLU [2]
● Concatenate profiling information into the flattened
CNN output
CNN Model architecture
concatenate with profiling
flatten
11

12. Training Hyperparameters Setting
● Loss function: MAE (mean absolute error)
● Optimizer: Adam (default setting)
● Initial learning rate = 0.0001
○ Reduce the learning rate by a factor of 10 when validation loss does
not improve in the last 10 epochs
● Signal frequency: 200Hz or 50 Hz
● Epochs: 100
● Batch size: 64
12

13. Evaluation Metrics
1. MAE (mean absolute error)
2. Pearson’s correlation coefficient
3. Clarke error grid analysis
○ 5 zones: A, B, C, D, E
○ Predictions located in Zone A or B would not lead to
inappropriate treatment
4. Parke error grid analysis (a revision of Clarke error grid)
5. FDA standard on invasive blood glucose meters
○ Very hard benchmark for non-invasive blood glucose
○ 95% measurements within 15% error
○ 99% measurements within 20% error
13

14. CNN (50 Hz)
14
Dropout = 0.5

15. CNN (200 Hz)
Dropout = 0.3, L2 = 1e-6
15

16. CNN flatten output to XGB (50 Hz)
16
Dropout = 0.5

17. CNN flatten output to XGB (200 Hz)
17
Dropout = 0.3, L2 = 1e-6

18. Results
● CNN flatten output with XGBoost improves 1~2%
compared to CNN
Model Hz MAE Cor Zone A Zone B Other
CNN 50 9.423 0.6363 91% 9% 0%
CNN flatten output
with XGBoost
50 9.558 0.5873 92% 8% 0%
CNN 200 10.854 0.6601 91.9% 8.1% 0%
CNN flatten output
with XGBoost
200 10.101 0.6768 93.5% 6.5% 0%
18

19. Multi-scale CNN
Combine signals in different frequencies
19

20. Multi-scale CNN architecture
1000 Hz
down-sampling
200 Hz
(6000, 10)
local CNN concatenation
(1200, 128)
CNN model in
page 11
100 Hz
(3000, 10)
50 Hz
(1500, 10)
25 Hz
(750, 10)
(600, 128)
(300, 128)
(150, 128)
dim
(2250, 128)
20

21. Local CNN architecture
conv1D
conv1D
Maxpool
BN
ReLU
filter size = (3, 1), 64 filters
filter size = (3, 1), 128 filters
pool size = (5, 1)
21

22. Multi-scale CNN (200, 100, 50, 25 Hz)
22

23. Multi-scale CNN flatten output to XGB
23

24. Results
Model Hz MAE Cor Zone A Zone B Other
CNN 50 9.423 0.6363 91% 9% 0%
CNN flatten output
with XGBoost
50 9.558 0.5873 92% 8% 0%
CNN 200 10.854 0.6601 91.9% 8.1% 0%
CNN flatten output
with XGBoost
200 10.101 0.6768 93.5% 6.5% 0%
M-CNN 200,100,50,25 12.403 0.5109 88.3% 11.4% 0.3%
M-CNN flatten
output with
XGBoost
200,100,50,25 10.313 0.6742 93.2% 6.5% 0.3%
24

25. Conclusions
● Developed a non-invasive blood glucose measurement method
by using PPG and ECG signals instead of blood samples
● Proposed a CNN architecture to process raw PPG and ECG signals,
and this CNN model achieves 91.9% of zone A (others in B)
● Utilized the flattened output of CNN as the input to XGBoost, and
the combined model achieves 93.5% of zone A (others in B)
● We tried to aggregate signals in different frequencies, but the
proposed multi-scale CNN model cannot outperform the above
models
25

26. Reference
1. P. Rajpurkar, A. Y. Hannun, M. Haghpanahi, C. Bourn, Andrew Y. Ng,
Cardiologist-Level Arrhythmia Detection with Convolutional Neural
Networks,https://arxiv.org/abs/1707.01836, submit on 6 July 2017
2. Zhicheng Cui, Wenlin Chen, Yixin Chen, Multi-Scale Convolutional Neural
Networks for Time Series Classification, https://arxiv.org/abs/1603.06995,
submit on 11 May 2016
3. Hidalgo JI, Colmenar JM, Kronberger G, Winkler SM, Garnica O, Lanchares
J., Data Based Prediction of Blood Glucose Concentrations Using Evolutionary
Methods, https://link.springer.com/article/10.1007/s10916-017-0788-2,
submit on 08 August 2017
4. https://en.wikipedia.org/wiki/Clarke_Error_Grid
26

27. Clarke error grid
● Zone A are those values within 20% of the reference sensor;
● Zone B contains points that are outside of 20% but would not lead to inappropriate treatment;
● Zone C are those points leading to unnecessary treatment;
● Zone D are those points indicating a potentially dangerous failure to detect hypoglycemia or
hyperglycemia;
● Zone E are those points that would confuse treatment of hypoglycemia for hyperglycemia and
vice versa;
FDA standard on invasive blood glucose meters
● 95% measurements within 15% error
● 99% measurements within 20% error
Appendix
27