PerDMCS: Weighted Fusion of PPG Signal Features for Robust and

Efﬁcient Diabetes Mellitus Classiﬁcation

V. Ramu Reddy

, Anirban Dutta Choudhury

, Srinivasan Jayaraman

, Naveen Kumar Thokala

Parijat Deshpande

and Venkatesh Kaliaperumal

TCS Research & Innovation Lab, Bangalore, India

TCS Research & Innovation Lab, Kolkata, India

TRDDC, Pune, India

Institute of Ayurveda and Integrative Medicine, School of Health Sciences, Transdisciplinary University, Bangalore, India

Keywords:

Pervasive Healthcare, Type 2, Diabetes Mellitus, Classiﬁcation, Heart Rate Variability, Fusion, Photoplethys-

mogram, Pulse Oximeter, Morphological.

Abstract:

Non-invasive detection of Diabetes Mellitus (DM) has attracted a lot of interest in the recent years in pervasive

health care. In this paper, we explore features related to heart rate variability (HRV) and signal pattern of the

waveform from photoplethysmogram (PPG) signal for classifying DM (Type 2). HRV features includes time-

domain (F

), frequency domain (F

), non-linear features (F

) where as waveform features (F

) are one set of

features such as height, width, slope and durations of pulse. The study was carried out on 50 healthy subjects

and 50 DM patients. Support Vector Machines (SVM) are used to capture the discriminative information

between the above mentioned healthy and DM categories, from the proposed features. The SVM models

are developed separately using different sets of features F

, F

,and F

, respectively. The classiﬁcation

performance of the developed SVM models using time-domain, frequency domain, non-linear and waveform

features is observed to be 73%, 78%, 80% and 77%. The performance of the system using combination of

all features is 82%. In this work, the performance of the DM classiﬁcation system by combining the above

mentioned feature sets with different percentage of discriminate features from each set is also examined.

Furthermore weight based fusion is performed using conﬁdence values obtained from each model to ﬁnd the

optimal set of features from each set with optimal weights for each set. The best performance accuracy of

89% is obtained by scores fusion where combinations of mixture of 90% features from the feature sets F

and

and mixture of 100% features from the feature sets F

and F

, with fusion optimal weights of 0.3 and 0.7,

respectively.

1 INTRODUCTION

Diabetes is a malfunction of glucose-insulin regula-

tory system that leads to onset of various complica-

tions. It has been recognized as fourth leading cause

of death in developed countries (Tabish, 2007). From

the recorded data in health centres worldwide it is

predicted that it is reaching epidemic proportions in

many developing and newly industrialized nations. In

terms of diabetic population, the top three countries

in the world are China, India and USA (Collabora-

tion et al., 2016). In India it has shot up from 11.9

million in 1980 to 64.5 million in 2014. Interna-

tional Diabetes Federation (IDF), has raised a serious

alarm for India by saying that nearly 52% of Indians

are not aware that they are suffering from high blood

sugar and it is expected to cross 100 million mark

by 2030

. Risk of cardiovascular disease (CVD) is

two or four times greater for diabetic individuals than

normal ones and there is a trend in increased risk of

cardiac mortality

However, till date there is limited

medical equipment and awareness of the severity of

this disease largely aggravated due to prevalence of

bad diet, no physical exercise, abnormal body weight,

and use of tobacco. Furthermore, the symptoms of

cardiac patients and diabetes patients are similar due

http://ccebdm.org/news.php

www.world-heart-feaderation.org/cardiovascular-disease-

risk-factors/diabetes/

Reddy V., Dutta Choudhury A., Jayaraman S., Kumar Thokala N., Deshpande P. and Kaliaperumal V.

PerDMCS: Weighted Fusion of PPG Signal Features for Robust and Efﬁcient Diabetes Mellitus Classiﬁcation.

DOI: 10.5220/0006297205530560

to change in the arterial stiffness and hence likely

to be mis-classiﬁed. The symptoms of this disease

are high blood sugar include frequent urination, in-

creased thirst, and increased hunger. If left untreated

it results in long-term complications include heart dis-

ease, stroke, chronic kidney failure, foot ulcers, and

damage to the eyes.

These problems are addressed by few existing so-

lutions; such as C-peptide test, fasting plasma glucose

test, GAD antibodies test, Hba1c test, oral glucose

tolerance test, type-2 diabetes indication test (Asso-

ciation et al., 2015). It should be noted that most of

the above-mentioned technique are either invasive or

minimal invasive (ﬁnger prick) in nature. This study

aims to identify the individual’s diabetic status by as-

sessing the vascular pulse function and other vital fea-

tures by using the non-invasive PPG signal. In ad-

dendum, continuous monitoring of diabetes patients

can aid in assisting the short and long-term compli-

cation risks as well. Hence, there is an inherent de-

mand to explore the feasibility for the continuous,

non-invasive monitoring and estimation of the type

2 diabetes. In (Schroeder et al., 2005), (Seyd et al.,

2012) researchers had explored diabetes detection us-

ing HRV features from the time domain, in (Elgendi,

2012) Mohamed has explored PPG features and its

applications where as Rohan et al has identiﬁed HRV

and pulse waveform features for identifying coronary

artery disease (Banerjee et al., 2016).

Non-invasive, quick, easy, low cost and on time

recognizingdiabetes with simple method and portable

technology for the primary care and community-

based clinical settings is the main goal of researchers

in this area. The pulse plethysmogram technology

has been used in a wide range of commercially avail-

able medical devices for measuring oxygen satura-

tion, blood pressure and cardiac output (Allen, 2007).

Due to change in glucose level, the amount of blood

volume in the ﬁnger changes, this variation can be

measured by PPG. When a ﬁxed source of infrared

radiation is used, the variation of blood volume act

as a phototransistor and the receive signal is changed.

This is why we use the PPG signal for identifying the

diabetic subjects. In this work a low-cost FDA ap-

proved pulse oximeter is used to collect vital physio-

logical signal such as PPG signal from ﬁnger. Heart

rate variability (HRV) features from the time domain

along with some useful features related to shape of the

pulse (morphological information) are extracted from

PPG to discriminate healthy and diabetic subjects. In

addition to the above features we have also explored

other HRV features extracted from the frequency do-

main, non-linear and poincare features. Our data-

driven approach enables visualization of PPG signals

and captures speciﬁc features such as heart rate vari-

ability (HRV) and features related to shape of the

pulse from PPG signal related to change in blood ﬂow

which in turn caused due to change arterial stiffness

due to diabetes. We have developed pervasive dia-

betes mellitus classiﬁcation system (PerDMCS) and

has achieved an accuracy of 82%, sesitivity 84% and

speciﬁcity 90% using above mentioned features. Fur-

ther weight based fusion technique is proposed for

more robust detection of diabetes. Field data shows

this method works properly and achieved an improved

accuracy of 89% with sensitivity and speciﬁcity of

90% and 88%, respectively.

2 DIABETES MELLITUS

DATASET

In this study, we have collected data from 50 con-

ﬁrmed diabetic patients and 50 healthy subjects. Di-

abetic subjects were aged between 34 ± 10 years

where as healthy subjects were aged between 41 ±

13. The subjects are selected from IAIMS Research

Center located in Bangalore, India. Experimentalpro-

tocol has been approved by the hospital ethical com-

mittee. PPG data were collected from right hand

index ﬁnger of each subject for 5 minutes using a

USB based commercial pulse oximeter (Contec CMS

50D+

) at 60 Hz.

3 PRE-PROCESSING

The collected PPG data is fed as an input to the pre-

processing block to obtain accurate RR intervals as

an output. This is achieved by following the sequence

of steps like baseline removal, peak detection and re-

moval of outliers obtained due to motion artifacts.

The signals obtained across each stage are also de-

picted in Fig. 1, where Fig. 1(a) represents raw PPG

signal obtained during data collection and Fig. 1(b) is

the corresponding PPG signal obtained after baseline

drift removal followed by peak detection. The region

where erroneous peak is obtained due to motion arti-

fact is marked with an elliptical region in Fig. 1(b).

Subsequently, peaks are calculated to obtain the RR

intervals and the outliers of RR intervals are then re-

moved. The steps involved in pre-processingstage are

brieﬂy discussed as follows:

• Baseline Removal: The baseline removal on the

PPG signal is carried out using beads technique

(Ning et al., 2014).

http://www.contecmed.com/

• Peak Detection: The peak detection is carried out

on the baseline removal of PPG signal using peak

detection algorithm.

• Outlier Removal: After peak detection, RR in-

tervals are computed from peak to peak intervals.

The outlier RR intervals resulted due to motion ar-

tifacts are removed using the percentage ﬁlter. As

mentioned in

if the percentage change of suc-

ceeding RR interval from the preceding RR inter-

val is greater than 20% can be treated as an outlier.

00:00:00 00:00:20

Time (hh:mm:ss)

Amp. (mV)

00:00:00 00:00:20

Time (hh:mm:ss)

Amp. (mV)

00:00:00 00:00:20

0.6

0.8

1.2

1.4

1.6

1.8

Time (hh:mm:ss)

RR (s)

00:00:00 00:00:20

0.6

0.8

1.2

1.4

1.6

1.8

Time (hh:mm:ss)

RR (s)

Drift removal PPG

Peaks

raw PPG

RR after Outlier removal

(a)

(c)

(d)

Figure 1: (a) Raw PPG signal, (b) Baseline drift PPG signal

After preprocessing different features were ex-

tracted as shown in Table 1.

4 NUMERICAL RESULTS

In this work, Support Vector Machines (SVM) are ex-

plored to discriminate the diabetic and healthy sub-

jects. SVM classiﬁcation is an example of super-

vised learning. SVMs are useful due to their wide

applicability for classiﬁcation tasks in many signal

processing applications such as emotion recognition

(Koolagudi et al., 2010), crowd noise and activ-

ity classiﬁcations (Reddy et al., 2013) (Reddy and

Chattopadhyay, 2014), and physiological signal based

CAD detection (Banerjee et al., 2016). A classiﬁ-

cation task usually involves training and testing data

which consist of some data instances. In the training

set, each instance contains one target class label and

many attributes. The main goal of SVM for classiﬁ-

cation problem is to produce a model which predicts

target class label of data instances in the testing set,

given only the attributes. The SVM model was de-

veloped as-one against-rest principle by using feature

vectors derived from the intended class as positive ex-

amples and the feature vectors derived from the other

class as negative examples.

Radial Basis Function (RBF) is used in this work.

This is because RBF, unlike linear kernel can handle

http://circ.ahajournals.org/content/93/5/1043

the case where the relationship between the class la-

bels and attributes is non-linear. Another advantage

of RBF kernel is its universal approximation prop-

erties. Also, it offers good generalization as well

as good performance in solving practical problems

(Reddy et al., 2014). The basic architecture of dia-

betes mellitus classiﬁcation system using SVMs with

above mentioned features is shown in Fig. 2.

Figure 2: Architecture of Pervasive Diabetes Mellitus Clas-

siﬁcation System (PerDMCS).

In this study, 100 subjects of data is used out of

which 50 subjects are healthy and 50 are diabetic. We

have used 5-fold validation technique where 4 folds

are used for training and remaining fold is used for

testing.

In this work, we ﬁrst analyzed the capability of

individual feature sets F

, F

and F

for discrimi-

nating the diabetes and healthy. Five DMCS systems

are developed, which are summarized as follows:

1. PerDMCS-1: Pervasive diabetes mellitus classiﬁ-

cation system using F

features.

2. PerDMCS-2: Pervasive diabetes mellitus classiﬁ-

cation system using F

features.

3. PerDMCS-3: Pervasive diabetes mellitus classiﬁ-

cation system using F

features.

4. PerDMCS-4: Pervasive diabetes mellitus classiﬁ-

cation system using F

features.

5. PerDMCS-5: Pervasive diabetes mellitus classiﬁ-

cation system using combination of all features.

Performance of the pervasive DMCSs using the

features discussed earlier, is represented in the form

of a consolidated confusion matrix as shown in Table

2. The diagonal elements of the confusion matrix rep-

resent the correct classiﬁcation performance of class.

Non-diagonal elements indicate the performance of

Table 1: List of Features.

Name Description

DM Range

mean± std

Non DM Range

mean± std

Time Domain Features (F

)

meanNN mean values of NN intervals (ms) 787 ± 133 735 ± 111

medianNN median values of NN intervals (ms) 787 ± 134 735 ± 114

SDNN standard deviation of NN intervals (ms) 26.58 ± 14.13 41.85 ± 18.45

RMSSD root mean square of successive 27.89 ± 15.27 34.81 ± 16.75

NN differences (ms)

NN50 total # of of successive NN 4.52 ± 6.9 9.14 ± 9.62

intervals differing by ≥ 50 ms

pNN50 percentage of successive NN 0.07 ± 0.1 0.12 ± 0.13

intervals differing by ≥ 50 ms

HRVti Ratio of number of all NN 3.39 ± 1.32 4.89 ± 1.71

intervals to maximum number

Frequency domain Features (F

) (Welch, 1967)(Billman, 2007)

aVLF raw area of VLF (0-0.04 Hz) band (ms

) 100 ± 42 294 ± 97

aLF raw area of LF (0.04-0.15 Hz) band (ms

) 134 ± 25 351 ± 39

aHF raw area of HF (0.15-0.5 Hz)band (ms

) 143 ± 29 284 ± 92

aTotal total raw area of VLF, LF and HF bands 892 ± 116 2062 ± 177

LFHF ratio of LF and HF areas 1.49 ± 1.79 2.1 ± 1.83

nLF normalized LF area w.r.t to LF+HF 0.15 ± 0.08 0.2 ± 0.08

nHF normalized HF area w.r.t to LF+HF 0.15 ± 0.08 0.14 ± 0.07

%VLF relative VLF area w.r.t to total area 12.04 ± 7.34 12.59 ± 7.89

% LF relative LF area w.r.t to total area 13.15 ± 7.08 18.03 ± 7.6

% HF relative HF area w.r.t to total area 13.79 ± 7.99 12.66 ± 7.01

peakVLF freq. of highest power in VLF band 0.02 ± 0.02 0.02 ± 0.02

peakLF freq. of highest power in LF band 0.02 ± 0.03 0.03 ± 0.03

peakHF freq. of highest power in HF band 0.14 ± 0.08 0.09 ± 0.08

Non-linear Features (F

)

pSD1 Poincar´e SD i.e., standard deviation of points 19.86 ± 10.89 24.77 ± 11.93

perpendicular to the axis of line-of-identity

pSD2 Poincar´e SD i.e., standard deviation of points 31.43 ± 17.66 53.31 ± 24.19

along the axis of line-of-identity

sampEN sample entropy estimates 1.57 ± 0.45 2.04 ± 0.48

1.4 ± 0.42 1.73 ± 0.46

1.33 ± 0.42 1.66 ± 0.5

alpha detrended ﬂuctuation analysis i.e., slope of 0.4 ± 0.31 0.5 ± 0.31

log-log plot of integrated RR vs window size 0.84 ± 0.26 0.98 ± 0.23

0.81 ± 0.28 1.04 ± 0.27

0.43 ± 0.29 0.44 ± 0.3

0.89 ± 0.7 0.85 ± 0.68

0.34 ± 0.88 0.65 ± 0.86

Waveform Features (F

)

meanFS mean value of falling slopes 0.03 ± 0.01 0.03 ± 0.01

meanRS mean value of rising slopes 0.07 ± 0.02 0.09 ± 0.02

meanPWp75 mean value of pulse widths at 75% 0.18 ± 0.02 0.15 ± 0.03

meanPWp50 mean value of pulse widths at 50% 0.29 ± 0.04 0.27 ± 0.06

meanPWp25 mean value of pulse widths at 25% 0.46 ± 0.06 0.45 ± 0.07

meanCT mean value of crest times 0.21 ± 0.03 0.17 ± 0.03

meanDT mean values of diastolic times 0.58 ± 0.11 0.56 ± 0.1

meanPH mean values of pulse heights 50.46 ± 9.5 52.91 ± 7.65

meanPI mean values of pulse intervals 0.79 ± 0.13 0.73 ± 0.11

misclassiﬁcation. Columns 3-4 indicate the perfor-

mance of the PerDMCS systems. Other performance

measurements like true positive, false positive, true

negative, false negative, precision, recall, sensitivity ,

speciﬁcity and overall model accuracy are presented

in Table 3.

Analysis: From Tables 2 and 3, it is observed

that feature sets F

, F

, and F

have discrimina-

tory information related to diabetes. It is also ob-

served that the diabetes is well discriminated com-

pared to healthier subjects using shape related fea-

tures i.e., F

, whereas using non-linear information

i.e. F

, classiﬁcation of healthier shows better per-

formance compared to diabetes. From this we can

hypothesize that both F

and F

are complementary

in nature, and if integrated can lead into better clas-

siﬁcation. Though combination of all features (i.e.

PerDMCS-5) yields the best model accuracy, one can

observe that PerDMCS-3 outperforms PerDMCS-5 in

some of the performance measurements (True Posi-

tive, True Negative, Precision and Speciﬁcity). This

is due to the inclusion of unimportant features from

and F

. Results indicate that there is a scope of

minute features selection from individual feature sets.

Hence for improving performance of the entire sys-

tem, different fusion technologies are explored.

5 WEIGHTED FUSION:

PROPOSED METHOD

5.1 Features Fusion

In this study, the fusion at feature level is performed

by concatenation of the different percentage of dis-

criminative features from each set i.e., F

to F

. The

concatenation process of features is carried out as fol-

lows.

1. The features are ranked using correlation of fea-

tures and labels (Hall, 2000).

2. Different percentage of features are selected from

ranked features. In this work, we have explored

top 50% features to 100% with increments of 10%

i.e., 6 variations such as 50%, 60% 70%, 80%,

90% and 100% most discriminative features.

3. Finally we have concatenated different percentage

of features from each set to build the PerDMCS

model.

Different technologies of features level fusion are

employed e.g. different percentages from each do-

main separately (One vs One vs One vs One), differ-

ent percentage from the combinations of two feature

Table 2: Performance of pervasive diabetes mellitus classi-

ﬁcation systems developed using different features. The en-

tries in the table indicate the subjects of classiﬁcation. Act:

Actual, Pred: Predicted.

PerDMCS

Pred.

Act.

Diabetic Healthy

PerDMCS Diabetic 34 11

1 Healthy 16 39

PerDMCS Diabetic 37 9

2 Healthy 13 41

PerDMCS Diabetic 38 8

3 Healthy 12 42

PerDMCS Diabetic 40 13

4 Healthy 10 37

PerDMCS Diabetic 42 10

5 Healthy 8 40

sets and different percentages from each of the rest

(Two vs One vs One) etc. The comparisons of differ-

ent feature level fusions are presented in 3.

As shown in 3(a), 24 (out of 1296) feature com-

binations result in accuracy of 83%. In Fig. 3(b), it

can be observed that the model accuracy is improved

slightly i.e., 2% for some different feature combina-

tions compared to combination of all individual fea-

tures as shown in Table 3. Here, 8 feature combina-

tions results in high accuracy of 84% and which is

1% improvement compared to the earlier combina-

tion. The best model accuracy remains similar 84%

(for the combination of 50% features from F

and

90% features from (F

+ F

) as shown in Fig. 3(c)).

In Fig. 3(d), the best model accuracy is 83% for the

combination of 50% features from (F

+ F

) and 80%

features from (F

+ F

). However, the average accu-

racy is slightly less than the best combinations of Two

vs One vs One and Two vs One fusions. Fig. 3(e)

shows that the maximum accuracy achieved in this

fusion approach is 81% and it marks a clear degra-

dation in performance compared to the combinations

mentioned above. Here, among all 144 combinations,

8 feature combinations results in 81% accuracy.

5.2 Scores Fusion

Score level fusion is performed by summing the

weighted conﬁdence scores (evidences) derived from

the different PerDMCSs. The weighing rule for com-

bining the conﬁdence scores of individual modalities

is as follows:

C =

∑

i=1

(1)

Table 3: Objective parameters of different PerDMCSs.

Performance Measurements PerDMCS-1 PerDMCS-2 PerDMCS-3 PerDMCS-4 PerDMCS-5

True Positive 34 37 38 40 42

False Positive 11 9 8 13 10

False Negative 16 13 12 10 8

True Negative 39 41 42 37 40

Precision 0.76 0.80 0.83 0.75 0.81

Sensitivity 0.68 0.74 0.76 0.80 0.84

Speciﬁcity 0.78 0.82 0.84 0.74 0.80

Model Accuracy 0.73 0.78 0.80 0.77 0.82

where C is the weighted conﬁdence score, w

and c

are weight and conﬁdence score of the i

modality,

and m indicates number of modalities used for com-

bining the scores. In this work, we have combined dif-

ferent modalities as described in section VI-A, such as

two modalities (Two vs One, Two vs Two and Three

vs One), three modalities (Two vs One vs One) and

four modalities (One vs One vs One vs One).

In our study, for two modality systems one of the

weights (w

) is varied in steps of 0.1 from 0 to 1, and

the other weight is determined using the formula: w

= 1- w

such that total weight w

+ w

= 1. Hence, we

get 11 combinations of weighing factors. Similarly,

for three modality systems two of the weights (w

and

) are varied in steps of 0.1 from 0 to 1 and the other

weight is determined using the formula: w

= 1- w

such that total weight w

+ w

= 1 and w

≥ 0.

Hence, we get 60 combinations of weighting factors.

For four modality systems three of the weights (w

, w

and w

) are varied in steps of 0.1 from 0 to 1 and the

other weight is determined using the formula: w

1- w

-w

such that total weight w

+ w

= 1 and w

≥ 0. The classiﬁcation performance of

the combined system for various combinations of the

weighting factors are as follows.

5.2.1 One vs One vs One vs One

It is observed that out of 313632 fusion of score com-

binations only 12 instances found to produce high-

est accuracy of 85%. The accuracy of fusion based

models at score level is performed slightly better than

the models obtained by simple feature level combina-

tions.

5.2.2 Two vs One vs One

It is observed that out of 77760 fusion of score com-

binations only 3 instances found to produce highest

accuracy of 85%. The accuracy of fusion based mod-

els at score level is performed slightly better than

the models obtained by simple feature level combi-

nations.

5.2.3 Two vs One

It is observed that out of 4752 fusion of score com-

binations only 20 instances found to produce highest

accuracy of 84%. The accuracy of fusion based mod-

els at score level is not improved compared to the ac-

curacy obtained by simple feature level combinations.

5.2.4 Two vs Two

It is observed that out of 1188 fusion of score com-

binations only 2 instances found to produce highest

accuracy of 89%. The accuracy of fusion based mod-

els at score level is performed better than the models

obtained by simple feature level combinations.

5.2.5 Three vs One

It is observed that out of 1584 fusion of score com-

binations only 14 instances found to produce highest

accuracy of 82%. The accuracy of fusion based mod-

els at score level is performed slightly better i.e.1%

than the models obtained by simple feature level com-

binations. However, still the accuracy is very low

compared to other feature level and score level fusion

models.

From the results it is found that the fusion of

scores performed better than the models simple fea-

ture level fusion. Among all the models the highest

accuracy is obtained for score level fusion of Two vs

Two combinations. The best accuracy is observed

to be 89% for the feature combinations of mixture

of 90% features from the feature sets F

and F

and

mixture of 100% features from the feature sets F

and

, with optimal weights of 0.3 and 0.7, respectively.

Similarly, another best combination is observed to be

for the feature combinations of mixture of 100% fea-

tures from the feature sets F

and F

and mixture of

200 400 600 800 1000 1200

Feature Combinations One Vs One Vs One Vs One

Accuracy (%)

(a) One vs One vs One vs One

200 400 600 800 1000 1200

Feature Combinations Two vs One vs One

Accuracy (%)

(b) Two vs One vs One

50 100 150 200 250 300 350 400

Feature combinations Two vs One

Accuracy (%)

10 20 30 40 50 60 70 80 90 100

Feature combinations Two vs Two

Accuracy (%)

(d) Two vs Two

20 40 60 80 100 120 140

Feature combinations Three vs One

Accuracy (%)

(e) Three vs One

Figure 3: Accuracy of Feature Fusions.

100% features from the feature sets F

and F

, with

optimal weights of 0.2 and 0.8, respectively.

It is noted that the best combination in feature

level fusion at Two vs Two level showed the best

accuracy of 83% for the combination of mixture of

100

150

200

250

# of weight combinations

# of instances with High Accuracy

Accuracy (%)

Maximum

Accuracy= 85%

(a) One vs One vs One vs One

# of weight combinations

# of instances with high accuracy

Accuracy (%)

Maximum

Accuracy= 85%

(b) Two vs One vs One

# of weight combinations

# of instances with High Accuracy

Accuracy (%)

Maximum

Accuracy= 85%

# of weight combinations

# of instances with High Accuracy

Accuracy (%)

Maximum

Accuracy= 89%

(d) Two vs Two

# of weight combinations

# of instances with High Accuracy

Accuracy (%)

Maximum

Accuracy =82%

(e) Three vs One

Figure 4: Accuracy of Score Fusion.

50% features from the feature sets F

and F

and mix-

ture of 80% features from the feature sets F

and F

However, in score level fusion it is not improved. In

score level fusion it picked the different set of fea-

ture combinations and the performance of the system

improved from 83% at feature level to 89% at score

level. It can be seen that the best feature combina-

tion observed in score level fusion exhibits 81% ac-

curacy in feature level fusion from the Fig. 3(d) with

equal weights i.e. 0.5 and 0.5 (6th combination across

# of weight combination). This indicates that fusion

is able to combine the complementary nature of ev-

idence obtained from different sets of features. The

performance measures for the best combination is ob-

served to be same and given in Table 4. From the re-

sults, it is observed that the score based fusion based

PerDMCS system is outperformed compared to indi-

vidual system performances (Table 2).

Table 4: Performance of best pervasive diabetes mellitus

classiﬁcation systems developed using fusion technique.

The entries in the table indicate the subjects of classiﬁca-

tion.

❵

Predicted

Actual

Diabetic Healthy

Diabetic 45 6

Healthy 5 44

6 SUMMARY

In this work, HRV features related to time domain,

frequency domain and non-linear and shape (morpho-

logical) related features extracted from PPG signal

are used to discriminate between diabetic and healthy.

SVMs are used as classiﬁcation models for develop-

ing different PerDMCS systems. The performance of

the PerDMCS systems developed by individual fea-

tures are improved by exploring fusion techniques, by

combining different percentage of discriminate fea-

tures from different combinations of feature sets and

scores of the individual systems and different combi-

nation systems as well. An improvement in classiﬁ-

cation performance of the system is observed at score

level fusion with average classiﬁcation performance

of 89%. This is attributed to the complementary na-

ture of evidence present in the features.

ACKNOWLEDGEMENTS

We are immensely grateful to the subjects for spend-

ing their valuable time and providing the data for this

study. We are thankful to IAIMS Research Center.

Further, we express our gratitude towards Dr. Arpan

Pal and Dr. Balamurali P. for their guidance and sup-

port.

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Allen, J. (2007). Photoplethysmography and its application

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