Parallel support vector architectures for taxonomy of radial pulse morphology

Karamchandani, S and Desai, U B and Merchant, S N and Jindal, G D (2013) Parallel support vector architectures for taxonomy of radial pulse morphology. Signal, Image and Video Processing, 7 (5). pp. 975-990. ISSN 1863-1703

Full text not available from this repository. (Request a copy)

Abstract

Application of impedance plethysmography (IP) for impedance measurement is the paradigm in assessment of central and peripheral blood flow. We propose the expediency of IP to unearth hidden patterns from Plethysmographic observations on a radial pulse. The variability analysis in one thousand control and disease subjects evolves an archetype of eight different morphological patterns. The peripheral pulse waveforms not only characterize the physiology of control subjects, but also define the morphology of patients suffering from Myocardial Infarction, cirrhosis of liver, and disorder of lungs. Diverse parallel support vector machine (pSVM) topologies are designed as an aid to the physician for multiclass pattern recognition problem. Besides a lowest confusion coefficient (0.133), the PCA-based pSVM classifier offers a comparatively higher generalized correlation coefficient and κ value of 0.6586 and 0.8407, respectively. However, the ROC characteristics and the benchmark parameters suggest that wavelet-based pSVM is the optimum classifier with a sensitivity of 85.33 %, an elevated MCC (0.69), and a least upper bound on the expected error. pSVM stands out as a model classifier as compared with identified indices such as, Fishers Ratio, Morphology Index, and Heart rate variability.

[error in script]

IITH Creators: