PPG Signal Analysis for Cardiovascular Patient Using Correlation Dimension and Hilbert Transform Based Classification

Harikumar Rajaguru and Sunil Kumar Prabhakar
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Harikumar Rajaguru: Bannari Amman Institute of Technology, Department of ECE
Sunil Kumar Prabhakar: Bannari Amman Institute of Technology, Department of ECE

A chapter in New Trends in Computational Vision and Bio-inspired Computing, 2020, pp 1103-1110 from Springer

Abstract: Abstract To estimate the blood flow of skin employing infrared light, Photoplethysmography (PPG) is widely utilized. PPG has a lot of inherent advantages like inexpensiveness, non-invasive in nature and acts as a versatile diagnostic tool. It aids in the measurement of blood pressure, oxygen saturation levels, cardiac output and for managing various other autonomic functions of the body. For the effective screening of different pathologies, PPG serves as quite a promising technique. The movement of blood in the vessel which propagates from the heart to the toes and finger tips is reflected by the PPG signals. In this paper, PPG signals are utilized for a single patient who is suffering from cardio vascular problems. For the PPG signals, the Correlation Dimension (CD) and Detrend Fluctuation Analysis (DFA) concepts are applied and then Hilbert Transform is computed to it. After computing Hilbert transform, the signals are later classified with the help of three classifiers such as K Nearest Neighbours (KNN) Classifier, Firefly classifier and Trace Ratio Criterion based Linear Discriminant Analysis (TR-LDA) classifier to classify the cardiovascular risk of the patient. Results show that an average classification accuracy of 95.83% is obtained when TR-LDA is utilized, 93.75% is obtained when Firefly is obtained and 83.59% is obtained when KNN is utilized.

Keywords: PPG; DFA; KNN; TR-LDA (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-41862-5_112

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DOI: 10.1007/978-3-030-41862-5_112

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