Time Series and Morphological Feature Extraction for Classifying Coronary Artery Disease from Photoplethysmogram

In this paper we propose a feature extraction algorithm for classifying Coronary Artery Disease (CAD) patients from Photoplethysmogram (PPG) signals. Several domain-independent features, representing inherent properties of a time series are explored in our study. These are combined with Heart Rate V...

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Bibliographic Details
Published in2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 950 - 954
Main Authors Banerjee, Rohan, Bhattacharya, Sakyajit, Alam, Shahnawaz
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2018
Subjects
Classification
Coronary Artery Disease
Diseases
Feature extraction
Feature selection
Heart rate variability
MIMICs
Photoplethysmogram
Time measurement
Time series analysis
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Summary:In this paper we propose a feature extraction algorithm for classifying Coronary Artery Disease (CAD) patients from Photoplethysmogram (PPG) signals. Several domain-independent features, representing inherent properties of a time series are explored in our study. These are combined with Heart Rate Variability (HRV) and other popularly used morphological PPG features. A statistical feature selection algorithm, based on Maximal Information Coefficient (MIC) is applied on MIMIC II dataset for ranking and choosing the optimum features. A second hospital dataset of different patient demography is used for performance evaluation. Results show that, Support Vector Machine (SVM) classifier, designed using the selected features yields average sensitivity and specificity of more than 0.8 in identifying CAD patients and also outperforms two recent prior art approaches when applied on the test dataset.
ISSN:2379-190X
DOI:10.1109/ICASSP.2018.8462604