Authors:
V. Ramu Reddy
1
;
Anirban Dutta Choudhury
1
;
Srinivasan Jayaraman
1
;
Naveen Kumar Thokala
1
;
Parijat Deshpande
2
and
Venkatesh Kaliaperumal
3
Affiliations:
1
TCS Research & Innovation Lab, India
;
2
TRDDC, India
;
3
Transdisciplinary University, India
Keyword(s):
Pervasive Healthcare, Type 2, Diabetes Mellitus, Classification, Heart Rate Variability, Fusion, Photoplethysmogram, 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 timedomain
(F1), frequency domain (F2), non-linear features (F3) where as waveform features (F4) 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 F1, F2, F3,and F4, 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 F1 and
F2 and mixture of 100% features from the feature sets F3 and F4, with fusion optimal weights of 0.3 and 0.7,
respectively.
(More)