Open AccessDiagnostic measure to quantify loss of clinical components in multi‐lead electrocardiogram
Author(s) -
Tripathy R.K.,
Sharma L.N.,
Dandapat S.
Publication year - 2016
Publication title -
healthcare technology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.45
H-Index - 19
ISSN - 2053-3713
DOI - 10.1049/htl.2015.0011
Subject(s) - principal component analysis , measure (data warehouse) , pearson product moment correlation coefficient , spearman's rank correlation coefficient , rank correlation , pattern recognition (psychology) , correlation coefficient , mean squared error , wavelet , correlation , computer science , artificial intelligence , mathematics , statistics , data mining , geometry
In this Letter, a novel principal component (PC)‐based diagnostic measure (PCDM) is proposed to quantify loss of clinical components in the multi‐lead electrocardiogram (MECG) signals. The analysis of MECG shows that, the clinical components are captured in few PCs. The proposed diagnostic measure is defined as the sum of weighted percentage root mean square difference (PRD) between the PCs of original and processed MECG signals. The values of the weight depend on the clinical importance of PCs. The PCDM is tested over MECG enhancement and a novel MECG data reduction scheme. The proposed measure is compared with weighted diagnostic distortion, wavelet energy diagnostic distortion and PRD. The qualitative evaluation is performed using Spearman rank‐order correlation coefficient (SROCC) and Pearson linear correlation coefficient. The simulation result demonstrates that the PCDM performs better to quantify loss of clinical components in MECG and shows a SROCC value of 0.9686 with subjective measure.