Inter-lead correlation analysis for automated detection of cable reversals in 12/16-lead ECG. Issue 134 (October 2016)

• Record Type: Journal Article
• Authors: Jekova, Irena; Krasteva, Vessela; Leber, Remo; Schmid, Ramun; Twerenbold, Raphael; Müller, Christian; Reichlin, Tobias; Abächerli, Roger
• Is Part Of: Computer methods and programs in biomedicine. Issue 134(2016)
• Page Start: 31
• Page End: 41
• Publication Date: 2016-10
• Subjects: ECG electrode interchange -- Lead swap -- ECG pattern progression -- Cross-correlation analysis -- Limb leads -- Precordial leads; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2016.06.003
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A novel method for detection of reversals in limb and precordial (standard, right and posterior) ECG leads is presented. Inter-lead cross correlation follows the ECG pattern progression within specific lead sets. Test-validation on three independent clinical ECG databases shows a robust performance on a large population. The performance could be highly rated among published studies for detection of 12-lead ECG reversals. The detection of right and posterior lead reversals in 16-lead ECG is unveiled. Abstract: Background and objective: A crucial factor for proper electrocardiogram (ECG) interpretation is the correct electrode placement in standard 12-lead ECG and extended 16-lead ECG for accurate diagnosis of acute myocardial infarctions. In the context of optimal patient care, we present and evaluate a new method for automated detection of reversals in peripheral and precordial (standard, right and posterior) leads, based on simple rules with inter-lead correlation dependencies. Methods: The algorithm for analysis of cable reversals relies on scoring of inter-lead correlations estimated over 4s snapshots with time-coherent data from multiple ECG leads. Peripheral cable reversals are detected by assessment of nine correlation coefficients, comparing V6 to limb leads: (I, II, III, –I, –II, –III, –aVR, –aVL, –aVF). Precordial lead reversals are detected by analysis of the ECG pattern cross-correlation progression within lead sets (V1–V6), (V4R, V3R, V3, V4), and (V4, V5, V6, V8, V9). Disturbed progression identifies the swapped leads. Results: A test-set, including 2239 ECGs from three independent sources—public 12-lead (PTB, CSE) and proprietary 16-lead (Basel University Hospital) databases—is used for algorithm validation, reporting specificity (Sp) and sensitivity (Se) as true negative and true positive detection of simulated lead swaps. Reversals of limb leads are detected with Se = 95.5–96.9% and 100% when right leg is involved in the reversal. Among all 15 possible pairwise reversals in standard precordial leads, adjacent lead reversals are detected with Se = 93.8% (V5–V6), 95.6% (V2–V3), 95.9% (V3–V4), 97.1% (V1–V2), and 97.8% (V4–V5), increasing to 97.8–99.8% for reversals of anatomically more distant electrodes. The pairwise reversals in the four extra precordial leads are detected with Se = 74.7% (right-sided V4R–V3R), 91.4% (posterior V8–V9), 93.7% (V4R–V9), and 97.7% (V4R–V8, V3R–V9, V3R–V8). Higher true negative rate is achieved with Sp > 99% (standard 12-lead ECG), 81.9% (V4R–V3R), 91.4% (V8–V9), and 100% (V4R–V9, V4R–V8, V3R–V9, V3R–V8), which is reasonable considering the low prevalence of lead swaps in clinical environment. Conclusions: Inter-lead correlation analysis is able to provide robust detection of cable reversals in standard 12-lead ECG, effectively extended to 16-lead ECG applications that have not previously been addressed.