Artifact Detection of Wrist Photoplethysmograph Signals

Kaat Vandecasteele

1,2

, Jes

us L

azaro

1,2

, Evy Cleeren

, Kasper Claes

, Wim Van Paesschen

Sabine Van Huffel

1,2

and Borb

ala Hunyadi

1,2

KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems,

Signal Processing and Data Analytics, Leuven, Belgium

imec, Leuven, Belgium

KU Leuven, University Hospital, Department of Neurosciences, Leuven, Belgium

UCB, Brussels, Belgium

Keywords:

Artifact Detection, Photoplethysmography, Wrist, Feature Selection.

Abstract:

There is a growing interest in monitoring of vital signs through wearable devices, such as heart rate (HR). A

comfortable and non-invasive technique to measure the HR is pulse photoplethysmography (PPG) with the

use of a smartwatch. This watch records also triaxial accelerometry (ACM). However, it is well known that

motion and noise artifacts (MNA) are present. A MNA detection method, which classiﬁes into a clean or MNA

segment, is trained and tested on a dataset of 17 patients, each with a recording duration of 24 hours. PPG-

and ACM-derived features are extracted and classiﬁed with a LS-SVM classiﬁer. A sensitivity and speciﬁcity

of respectively 85.50 % and 92.36 % are obtained. For this dataset, the ACM features do not improve the

performance, suggesting that ACM recording could be avoided from the point of view for detecting MNA in

PPG signals during daily life.

1 INTRODUCTION

There is a growing interest in wearable and continu-

ous monitoring of vital signs, such as heart rate (HR).

A comfortable and non-invasive technique to measure

the HR is pulse photoplethysmography (PPG). PPG

makes use of reﬂected or transmitted light through

the skin to measure a pulsatile physiological wave-

form caused by changes in the blood volume due to

a heart beat. Common PPG recording locations in-

clude ﬁngers, ears, toes, forehead or wrist (Allen,

2007). The wrist is a possible recording location. An

advantage is that the PPG sensor can be embedded

in a watch. However, it is well known that motion

and noise artifacts (MNA) can distort the signal (Pet-

terson et al., 2007). Those MNA are caused by 1)

the movement of venous blood as well as other non-

pulsatile components 2) variations in the optical cou-

pling between the sensor and the skin (Barker and

Shah, 1997; Tobin et al., 2002). The MNA can be

particularly challenging when computing derived fea-

tures from the PPG waveform such as HR. Various

design approaches to reduce MNA have been pro-

posed (Li and Warren, 2012). With these improve-

ments, MNA are stil present. Therefore, algorithm-

based MNA reduction methods were proposed (Tor-

res et al., 2016; Lai and Kim, 2015; Fukushima

et al., 2012; Pan et al., 2016; Torres et al., 2016;

Youseﬁ et al., 2012; Ram et al., 2012; Lee et al.,

2010; Temko, 2017; Torres et al., 2016; Kim and

Yoo, 2006). These MNA reduction algorithms op-

erate also on clean parts of the signal, which is un-

necessary computation and can cause distortion of the

signal. Therefore an algorithm which can distinguish

clean parts from MNA is desired. MNA detection al-

gorithms are designed with the use of waveform mor-

phologies (Sukor et al., 2011; Li et al., 2012; Li et al.,

2008; Fischer et al., 2017) or ﬁltered output (Naka-

jima et al., 1996; Karlen et al., 2012). Statistical mea-

sures, such as skewness, kurtosis, shannon entropy

and Renyi’s entropy have been shown useful for auto-

matic detection of MNA (Selvaraj et al., 2011; Krish-

nan et al., 2008). Another approach, using Hjorth pa-

rameters was proposed (Gil et al., 2008). Other meth-

ods were published, which perform classiﬁcation with

a support vector machine (SVM) with time-domain

features (Chong et al., 2014) or time-frequency spec-

trum analysis (Dao et al., 2016) .

To the best of our knowledge, all these methods

are tested and validated for PPG recorded on the ears,

182

Vandecasteele, K., Lázaro, J., Cleeren, E., Claes, K., Paesschen, W., Huffel, S. and Hunyadi, B.

Artifact Detection of Wrist Photoplethysmograph Signals.

DOI: 10.5220/0006594301820189

In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 4: BIOSIGNALS, pages 182-189

ISBN: 978-989-758-279-0

ﬁngers or forehead, but none have been validated on

the wrist. Moreover, these methods are never tested

on 24 hour recordings. In our paper, we propose

an automated computer-based method for the detec-

tion of MNA in PPG signals, recorded with a wrist

watch. The method is validated on 24-hour data

recordings. The classiﬁcation is performed with a

LS-SVM model using previously published features

(Selvaraj et al., 2011; Gil et al., 2008; Chong et al.,

2014), together with new features. Additional to the

PPG signals, most smart watches also record the ac-

celerometry (ACM). Therefore, it is investigated if

the ACM improves the MNA detection method. A

backwards wrapper feature selection is implemented

to determine which features are the most discrimina-

tive ones. The goal of the paper is to provide a reliable

MNA detection method, validated for wrist PPG sig-

nals, and additionally to evaluate whether the ACM

improves the detection performance.

2 METHODOLOGY

The methodology consists of ﬁve parts. Firstly,

the data collection is described. Secondly, the ex-

tracted features from the PPG signal, are summa-

rized. Thirdly, a LS-SVM-based classiﬁer is dis-

cussed. Fourthly, a feature selection method is ex-

plained, which selects the most discriminative fea-

tures. Lastly, the labelling process is explained. With

the use of a reference signal, a label (Clean or MNA)

is given to each PPG segment.

2.1 Data Acquisition

The dataset, used in this experiment, contains record-

ings of 17 epilepsy patients, each with a duration

around 24 hours. The epilepsy patients were recorded

with a wired conventional multi-channel scalp EEG.

Additionally to the standard clinical equipment,

recordings were made with a wearable ECG device:

the 180

eMotion Faros (Bittium Biosignals Ltd,

2017), and a wrist-worn PPG device: the E4 Empatica

wristband (Empatica Inc, 2017). The Faros ECG was

used in a single-channel conﬁguration. The Empatica

E4 measures reﬂective PPG using a green and a red

LED on the wrist. The device returns a single chan-

nel which is obtained by combining the green and the

red channels using Empatica’s proprietary algorithm.

Additional to the 1-channel PPG, the triaxial ACM

is recorded. Considering that the intravenous inser-

tion, used to administer medication in clinical prac-

tice, is routinely placed at the non-dominant wrist, the

patients wore the Empatica watch on the dominant

hand. The sampling rate of the Faros and Empatica

(PPG/ACM) device are respectively 500Hz and 64Hz

(PPG)/32 Hz (ACM).

2.2 Feature Extraction

Features are extracted from both the PPG signal and

ACM signal. Features from literature, which are pre-

viously shown to be useful, are combined with our

own features. First, the PPG signal was preprocessed

by a 5th order bandpass butterworth ﬁlter [0.5 - 12

Hz]. The ACM was preprocessed by a 3th order but-

terworth ﬁlter [0.2 - 10Hz]. The PPG signal and cor-

responding ACM data are segmented in 7 seconds, be-

cause it was reported to be the optimal segment length

for MNA detection (Chong et al., 2014).

2.2.1 PPG

The following features were extracted from the PPG

segments:

• Standard Deviation of Pulse-to-Pulse Interval

(ST D

), Standard Deviation of Pulse-to-Pulse

Amplitude (ST D

amp

), Standard Deviation of Sys-

tolic and Diastolic Ratio (ST D

) and Mean-

Standard Deviation of Pulse Shape (ST D

WAV

which are described in (Chong et al., 2014)

• Kurtosis (K) and Shannon Entropy (SE), which

are described in (Selvaraj et al., 2011)

• Variance (Var) of the signal segment

• The frequency of the ﬁrst/second/third-largest

peak in the power spectrum (FLP/SLP/TLP),

which are expected to be around 1Hz (the HR fre-

quency)/ 2Hz (the ﬁrst harmonic)/ 3Hz (the sec-

ond harmonic) for a clean segment

• Spectral Shannon entropy (SSE), which is the

shannon entropy of the power spectrum

• Hjorth parameters: H1 and H2, which represents

respectively the central frequency and half of the

bandwidth (Gil et al., 2008)

In order to calculate the features ST D

and

ST D

amp

, the location and amplitude of the pulse

peaks are needed. The feature ST D

requires the

systolic and diastolic time, which are respectively the

rising time from valley to peak and the falling time

from peak to the next valley. The feature ST D

WAV

re-

quire the alignment of the pulses in a segment, which

is done with the peak location. To calculate the loca-

tion and amplitude of the pulse peaks and pulse val-

leys, an algorithm developed in (L

azaro et al., 2014) is

used. This algorithm consists of two phases: a linear

ﬁltering transformation and an adaptive thresholding

Artifact Detection of Wrist Photoplethysmograph Signals

183

operation. The ﬁltering step consists of a linear-phase

FIR low-pass-differentiator ﬁlter, which is used to ac-

centuate the abrupt upslopes of the PPG pulses. The

abrupt upslopes correspond to peaks in the ﬁltered

signal, which are detected by an adaptive threshold-

ing operation. Once the peaks in the ﬁltered signal

are found, the maximum and minimum point in the

original PPG signal are found.

2.2.2 ACM

The following features were extracted from each di-

rection and Euclidean norm of the ACM segments:

1. The maximal value of the rectiﬁed segment

(max

,max

and max

norm

)

2. The 90th percentile of the rectiﬁed segment

(90

,90

and 90

norm

)

3. The Variance of the segment (Var

,Var

and

Var

norm

)

4. The mean absolute deviation of the segment

(MAD

,MAD

and MAD

norm

)

5. The norm of the segment (Norm

,Norm

and Norm

norm

)

2.3 LS-SVM based Classiﬁcation of

MNA

The classiﬁcation is done with a LS-SVM (Least

Squares Support Vector Machines) classiﬁer (De Bra-

banter et al., 2003) using a linear kernel. The classiﬁer

is trained and tested within a leave-one-patient-out

cross-validation (LOPO-CV) approach. In this way,

no data of the patient itself is used for the training.

To evaluate the classiﬁer, the sensitivity (proportion

of MNA segments that are correctly classiﬁed), the

speciﬁcity (the proportion of clean segments that are

correctly classiﬁed) and the accuracy are calculated.

The classiﬁer is tested with only PPG features, only

ACC features and all the features together.

2.4 Feature Selection

In order to investigate which features are discrimina-

tive for clean and MNA PPG segments, a backwards

wrapper feature selection method is implemented. In

Figure 1 a ﬂow diagram of the method is shown. At

the start, the total feature set is used to train and test

the classiﬁer within a LOPO-CV approach. As evalu-

ation criteria, the F1-score is calculated, which is the

harmonic mean of the sensitivity and the speciﬁcity.

In a next step, new feature sets are created by leaving

out each feature one time. So, if there are N features,

N new feature sets are created. Again the classiﬁer is

trained and tested with these new feature sets within

a LOPO-CV approach and the F1-scores are calcu-

lated. Next, the maximal F1-score, corresponding to

the best new feature set, is compared with the original

F1-score. If this F1-score is higher or equal, this fea-

ture doesn’t improve the classiﬁcation problem and is

removed. Whole the procedure is calculated again by

starting with the new feature set, corresponding to the

highest F1-score. With every iteration only 1 feature

can be removed. The method will stop when all the

new F1-scores are lower.

Figure 1: Flowchart: Backwards wrapper feature selection.

The feature selection procedure was performed on

the PPG features, ACM features and all the features

together.

2.5 Reference Signal: ECG

In order to train and test a classiﬁer, labels for each

segment are needed. A visual reference is avoided

because it’s very subjective, i.e. different visual in-

spectors annotate the segments differently. Further-

more, it would be very time consuming to annotate a

whole dataset of 408 hours (17*24 hours). Instead,

we performed an automatic labelling procedure based

on the reference electrocardiography (ECG) signal,

similarly as in (Chong et al., 2014). The heart rate

variability (HRV) is calculated, by ﬁnding the loca-

tion of the R-peaks (Varon et al., 2015). For each PPG

segment and corresponding ECG segment, the mean

RR-interval (RR

ECG

) and mean PP-interval (PP

PPG

)

and standard deviation of the RR- and PP-interval

(ST D

ECG

and ST D

PPG

) are calculated. A segment is

classiﬁed as MNA, if |RR

ECG

− PP

PPG

| > 150ms or

|ST D

ECG

− ST D

PPG

| > 100ms. These thresholds are

set empirically based on a subset of the data.

BIOSIGNALS 2018 - 11th International Conference on Bio-inspired Systems and Signal Processing

184

3 RESULTS AND DISCUSSION

3.1 Feature Selection

In table 1, 2 and 3 the selected features are shown in

bold, starting from respectively the PPG, ACM and

all the features. From the newly proposed PPG fea-

tures, 3 features are retained by the feature selection:

the variance, the frequency of the second and third

largest peak in the power spectrum. These 3 features

do have an added value for the MNA detection algo-

rithm. The feature selection process with the ACM

features shows that all features which make use of the

y-direction are left out. The y-direction is the direc-

tion along the lower arm, from wrist towards the el-

bow, as shown in Fig. 2. The accelerometry in this di-

rection doesn’t have an added value for the algorithm

for this dataset.

Figure 2: ACM axis.

It might be that motion in the y-direction, which

is along the direction of the lower arm, causes less

MNA than the other directions. Another reason can

be that this direction is less present during daily mo-

tion compared to the other directions. The feature se-

lection process with all the features shows that 4 from

the 13 PPG-derived and 10 from the 20 ACM-derived

features are removed. It seems that the ACM-features

have an added value, but this added value is only mi-

nor. With all the resulting features, a F1-score of

0.8841 is obtained. If 1 of those PPG-derived features

is removed, a F1-score between 0.8474 and 0.8836

is obtained. If one of the ACM-derived features is

removed, a F1-score between 0.8831 and 0.8841 is

obtained. So leaving out a ACM-feature would only

decrease the F1-score slightly.

3.2 Classiﬁcation Performance

In Figure 3 the sensitivities (Sens), speciﬁcities

(Spec) and accuracies (Acc) are plotted for all the pa-

tients. The sensitivity (speciﬁcity) is the proportion of

MNA (clean) segments that are correctly classiﬁed.

Table 1: Feature selection PPG.

STD

, STD

amp

, STD

and STD

WAV

K and SE

Var

FLP, SLP and TLP

SSE

H1 and H2

Table 2: Feature selection ACC.

max

, max

and max

norm

, 90

and 90

norm

Var

, Var

and Var

norm

MAD

, MAD

and MAD

norm

Norm

, Norm

and Norm

norm

Table 3: Feature selection ALL.

STD

, STD

amp

, STD

and STD

WAV

K and SE

Var

FLP, SLP and TLP

SSE

H1 and H2

max

, max

and max

norm

, 90

and 90

norm

Var

, Var

and Var

norm

MAD

, MAD

and MAD

norm

Norm

, Norm

and Norm

norm

The accuracy is the proportion of segments that are

correctly classiﬁed. In Table 4 the average values ±

standard deviations are shown.

The performance with PPG features is similar as

the performance with all features. Adding the ACM

features to the classiﬁcation does not increase the per-

formance. The reason is that the PPG signal has al-

ready enough information on itself. The MNA seg-

ments show large differences with clean segments,

which is illustrated in Figure 4.

By using only the ACM features, a low sensitivity

is obtained. This is due to the fact that not all kind

of MNA are caused by wrist motion. For example

subtle ﬁnger motion, which is shown in Figure 5, or

bad positioning of the sensor cause MNA, but there is

no corresponding ACM activation.

In table 5 previous experiments and results from

literature are summarized. In all the studies reﬂective

PPG with infrared light is recorded. The dataset and

sensor type are different for each study, which makes

it difﬁcult to compare quantitatively the results.

Artifact Detection of Wrist Photoplethysmograph Signals

185

5 10 15

Patients

100

Sensitivity(%)

5 10 15

Patients

100

Specificity(%)

5 10 15

Patients

100

Accuracy(%)

ALL

PPG

ACM

Figure 3: Classiﬁcation performance.

Table 4: Classiﬁcation performance.

PPG ACM ALL

Sens (%) 85.50 ± 8 58.04 ± 18 85.50 ± 7

Spec (%) 91.84 ± 5 93.01 ± 4 92.36 ± 4

Acc (%) 90.33 ± 2 76.23 ± 11 90.23 ± 3

Figure 4: Example: Clean (blue) and MNA (red) PPG seg-

ments with 3 axis ACM.

3.3 Overall Data Quality

In total 44.26% ± 16.20% of the data is labeled

as MNA based on the reference signal, 43.38% ±

15.62% of the data is detected as MNA by the algo-

rithm. This means that close to half of the measured

data are contaminated with MNA, so that the heart

rate variability cannot be reliably estimated by the al-

gorithm (L

azaro et al., 2014) from the recorded sig-

nals. Note that our dataset was acquired from patients

in a hospital environment. The patients are contin-

uously monitored with wired EEG, which limit their

mobility. Outside the hospital these percentages are

probably even higher.

5 10 15 20 25 30 35 40 45

time(s)

-150

-100

-50

100

Figure 5: Example: Clean (blue) and MNA, caused by ﬁn-

ger motion, (red) PPG.

3.4 Limitations and Further Work

The algorithm is tested on a data set, recorded in the

hospital. The algorithm should also be tested on daily

life data, recorded outside the hospital.

Only one type of sensor is tested (wrist reﬂective

PPG). It should be extended to other sensors. In order

to compare the different studies, the MNA detection

algorithms should be tested on the same data sets.

The MNA detection method, explained in this pa-

per, makes use of a ﬁxed window length of 7s. A

method should be investigated to automatically deter-

mine the length of the MNA.

Other models should be tested for this classiﬁca-

tion problem, for example deep neural networks.

Extracting HR and HRV is more challenging dur-

ing these artifactual segments and further signal pro-

cessing techniques are needed. Further studies must

be elaborated to assess how these MNA affect to the

different HRV indices.

4 CONCLUSIONS

The goal of the paper is to provide a reliable motion

and noise artifact (MNA) detection method for PPG

signals, recorded with a wrist watch. PPG- and ACM-

derived features are extracted and classiﬁed with a

LS-SVM classiﬁer. For this dataset, the ACM fea-

tures do not improve the performance, suggesting that

ACM recording could be avoided from the point of

view for detecting MNAs in PPG signals.

BIOSIGNALS 2018 - 11th International Conference on Bio-inspired Systems and Signal Processing

186

Table 5: Comparison with literature [Rec.=Recording, Acc=Accuracy, Se=Sensitivity, Sp=Speciﬁcity, Results are in %].

No. subjects (Rec. duration) Sensor type Type of movement Results

Sukor et al., 2011

13 (8 min) Finger Hand movements

Acc: 83 ± 11

Se: 89 ± 10

Sp: 77 ± 19

Selvaraj et al., 2011

10 (5 - 20 min) Ear/ Finger/ Forehead Involuntary movements

Acc: 99.0/94.8/93.3

Se: 100/99.3/96.3

Sp: 98.9/93.8/91.9

14 (10 min) Finger Voluntary movements

Acc: 88.8

Se: 86.9

Sp: 98.3

Chong et al., 2014

11 (10 min) Forehead Head movement

Acc: 94.4 ± 3.3

Se: 94.7 ± 3.4

Sp: 94.7 ± 4.5

9 (10 min) Finger Finger movement

Acc: 93.4 ± 3.5

Se: 88.8 ± 7.9

Sp: 96.7 ± 3.0

9 (45 min) Finger/Forehead Stair-climbing

Acc: 93.7 ± 2.7

Se: 93.9 ± 5.0

Sp: 91.4 ± 2.0

Dao et al., 2016

11 (10 min) Forehead Head movement

Acc: 95.7 ± 0.82

Se: 93.0 ± 5.75

Sp: 96.6 ± 1.48

11 (10 min) Finger Finger movement

Acc: 97.5 ± 1.50

Se: 96.4 ± 2.34

Sp: 98.1 ± 1.43

10 Forehead UMMC hospital

Acc: 95.3 ± 1.34

Se: 90.8 ± 2.83

Sp: 98.7 ± 1.07

10 Finger UMMC hospital

Acc: 94.3 ± 1.64

Se: 88.5 ± 2.23

Sp: 96.9 ± 1.86

ACKNOWLEDGEMENTS

SeizeIT is a project realized in collaboration with

imec. Project partners are KU Leuven, UCB Pharma,

Byteﬂies and Pilipili, with project support from

VLAIO (Flanders Innovation and Entrepreneurship)

and Innoviris.

Bijzonder Onderzoeksfonds KU Leuven (BOF):

SPARKLE Sensor-based Platform for the Accu-

rate and Remote monitoring of Kinematics Linked

to E-health #: IDO-13-0358; The effect of perina-

tal stress on the later outcome in preterm babies #:

C24/15/036; TARGID - Development of a novel di-

agnostic medical device to assess gastric motility #:

C32-16-00364. Agentschap Innoveren & Onderne-

men (VLAIO): Project #: STW 150466 OSA +, O&O

HBC 2016 0184 eWatch. iMinds Medical Informa-

tion Technologies: Dotatie-Strategisch basis onder-

zoek (SBO- 2016); ICON: HBC.2016.0167 SeizeIT.

European Research Council: The research leading to

these results has received funding from the European

Research Council under the European Union’s Sev-

enth Framework Programme (FP7/2007-2013) / ERC

Advanced Grant: BIOTENSORS (n

339804). This

paper reﬂects only the authors’ views and the Union

is not liable for any use that may be made of the con-

tained information.

Artifact Detection of Wrist Photoplethysmograph Signals

187

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