IEEE Circuits and Systems Magazine - Q3 2018 - 14
Table III.
Comparison of different PPG-based BP models and algorithms.
Dataset
Calibration
Accuracy(mmHg)
[58]
Non-linear Regression,
(ECG + PPG)
5 Case
A certain time for
each person
ME: SBP(5.4%), DBP(7.7%)
[59]
Linear Regression,
(ECG + PPG), adaptive
Kalman filter
10 subjects
Every 3.3 hours
STD: SBP(1.69), DBP(1.04)
[37]
ACF, linear regression,
(ECG + PPG)
MIMIC, 100
Cases
A certain time for
each person
ME ± STD: ABP(3.06 ± 3.69)
[34]
Iterative linear, (ECG + PPG)
MIMIC, 6
Cases
A certain time for
each person
ME ± STD: SBP(0.13 ± 3.73),
DBP(0.22 ± 4.33 mmHg)
[25]
Moens-Korteweg,
(ECG + PPG)
10 Cases
Once for every 12
hours
ME ± STD: SBP(2.4 ± 5.7)
[41]
SVR, (PPG)
MIMIC
Never
MAD ± STD: SBP(12.38 ± 16.17),
MAP(7.52 ± 9.54 mmHg),
DBP(6.34 ± 8.45 mmHg)
[42]
ANN (PPG)
MIMIC, 5000
Cases
Never
MAD ± STD: SBP(3.24 ± 4.75 mmHg),
MAP(2.16 ± 3.14), DBP(1.79 ± 2.70)
[30]
SVM, (PPG)
UQ, 32 Cases
Never
Classification(20 interval): SBP(98,81
%), DBP(98.21 %)
[43]
Random Forest, (PPG)
410 Cases
Never
MAD (#5,10,15): SBP(62.43%, 86.34%,
90.37%), DBP(79.02%, 90.24%, 93.90%)
[60]
Linear regression, (PPG)
18 subjects
A certain time for
one person
MAD ± STD: SBP(sleeping: 2.68 ± 0.39
mmHg, sleepy: 4.37 ± 1.20
[45]
Windkessel model, (PPG)
UQ, 32 Cases
Once for all
MAD ± STD: SBP(0.78 ± 13.1 mmHg),
DBP(0.59 ± 10.2)
[46]
Harmonic Balancing model,
(PPG)
5 Cases
A certain time for
one person
MAD ± STD: SBP(1.37 ± 7.61 mmHg),
DBP(−1.40 ± 6.00)
Amplitude
Algorithms
1
Pulse Height
0.9 Pulse
Pulse Rate
0.8 Amplitude Difference
0.7
0.6
0.5
0.4
0.3
0.2
Trought Depth Difference
Pulse Length
0.1
0
2
4
6
8
10
12
14
Time/s
Clean PPG
Artifact
Figure 10. Time domain features of the PPG signal and motion artifact effects [32].
extracted features. Non-parametric models try to solve
the low accuracy of parametric models.
Examples of non-parametric learning methods can be
found in [30], [41]-[43]. Reference [30] tries to classify the
SBP and DBP within different prediction ranges with 19
features extracted from the PPG signal using only a sup14
IEEE CIrCUITs AND sYsTEMs MAGAzINE
port vector machine (SVM). The estimation accuracy can
reach 98.81% and 98.21% for SBP and DBP, respectively.
Reference [41] gets its features from both the PPG and ECG
signals. Using support vector regression (SVR), it reaches
grade B for both DBP and mean blood pressure (MBP)
estimations and reaches grade C for SBP according to the
BHS protocol. In [42], an ANN-based learning process is
used to train the features from the PPG signal only. The
results can fulfill the AAMI requirement. Using only the
PPG signal, [43] implements a random forest (RF)-based
prediction model and achieves grade B for both SBP and
DBP by BHS. Even though the non-parametric BP models
exhibit better accuracy than the parametric models, they
suffer from heavy calculations.
Table III compares the different PPG-based BP models
and algorithms. The first five methods in Table III [25],
[34], [37], [58], [59] use both ECG and PPG signals with linear or nonlinear regression. These methods have higher
accuracy, but they require both ECG and PPG signals and
frequent calibration. The methods in [30], [41]-[43] do
not need frequent calibration, and only PPG information
is needed. However, they require excessive training and
ThIrD qUArTEr 2018
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