Performance analysis of classifiers in detection of CVD using PPG signals

• Main Authors: Rajaguru, Harikumar, Shankar, M. Gowri, Nanthakumar, S. P., Murugan, I. Arul
• Format: Conference Proceeding
• Language: English
• Subjects: Algorithms; Blood vessels; Circulatory system; Classifiers; Fluorescence; Probabilistic models; Regression; Regression analysis
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/5.0125137

To measure the circulatory system of skin using infrared light, PPG is extensively utilized. PPG has a lot of innate focal points like affordability, non-prominent in nature and goes probably as a versatile demonstrative gadget. It helps in the assessment of circulatory strain, oxygen inundation levels, heart yield and for administering distinctive other autonomic components of the body. For the amazing screening of different pathologies, PPG fills in as a huge promising framework. The improvement of blood in the vessel which spreads from the heart to the toes and fingertips is reflected by the PPG signals. In this paper, PPG signals are utilized for a singular patient who is encountering cardio vascular issues. For the PPG signals, the Firefly clusters and Dragonfly selector algorithms is utilized. In this study an in depth analysis of classifiers in detection of cardiovascular diseases (CVD) is done with the help of capnobase database. The following classifiers are used in this paper linear regression, Nonlinear Regression, Logistic regression, Bayesian linear discriminant classifier (BDLC) and Gaussian Mixture Model (GMM) and firefly. The plan and execution of a PPG are very economical and have a simple support. It is also used for the peripheral blood fluorescent and venous filling time. Results show that the Linear Regression classifier exceeds the other five classifiers in terms of accuracy (65.85%), F1 score (68.18%), MCC (0.316), Jaccard metric (51.72%), and error rate (34.15%).