Optimizing the 12-lead electrocardiogram: a data driven approach to locating alternative recording sites.
BACKGROUND
Despite its widespread use, the limitations of the 12-lead electrocardiogram (ECG) are undisputed. The main deficiency is that just a small area of the precordium is interrogated and for some abnormalities information may be transmitted to a region of the body surface where information is not recorded. In this study, we attempted to optimize the 12-lead ECG by using a data-driven approach to suggest alternate recording sites.


METHODS
A sequential lead selection algorithm was applied to a set of 744 body surface potential maps (BSPMs), consisting of recordings from subjects with myocardial infarction, left ventricular hypertrophy, and no apparent disease. A number of scenarios were investigated in which pairs of precordial leads were repositioned; these pairs were V3 and V5, V4 and V5, and V4 and V6. The algorithm was also used to find optimal positions for all 6 precordial leads.


RESULTS
Through estimation of entire surface potential distributions it was found that each of the scenarios, with 2 leads repositioned, captured more information than the standard 12-lead ECG. The scenario with V4 and V6 repositioned performed best with a root mean square error of 22.3 microvolts and a correlation coefficient of 0.967. This configuration also fared favorably when compared to the scenario where all 6 precordial leads were repositioned as optimizing all 6 leads offered no significant improvement.


CONCLUSIONS
This study demonstrated the use of a lead selection algorithm in enhancing the 12-lead ECG. The results also indicated that repositioning just 2 precordial leads can provide the same level of information capture as that observed when all precordial leads are optimally placed.
Finlay
Dewar D
DD
School of Computing and Mathematics, Faculty of Engineering, University of Ulster, Belfast, Northern Ireland, UK. d.finlay@ulster.ac.uk
Nugent
Chris D
CD
Kors
Jan A
JA
van Herpen
Gerard
G
Donnelly
Mark P
MP
McCullagh
Paul J
PJ
Black
Norman D
ND
D000465:Algorithms; D018780:Body Surface Potential Mapping / Q000379:methods*; D016208:Databases, Factual; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D017379:Hypertrophy, Left Ventricular / Q000175:diagnosis*; D016247:Information Storage and Retrieval / Q000379:methods; D009203:Myocardial Infarction / Q000175:diagnosis*; D011785:Quality Assurance, Health Care / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity