Importance of Electrode Selection and Number in Reconstructing Standard Twelve Lead Electrocardiograms.

Many clinical and consumer electrocardiogram (ECG) devices collect fewer electrodes than the standard twelve-lead ECG and either report less information or employ algorithms to reconstruct a full twelve-lead signal. We assessed the optimal electrode selection and number that minimizes redundant information collection while maximizing reconstruction accuracy. We employed a validated deep learning model to reconstruct ECG signals from 250 different patients in the PTB database. Different numbers and combinations of electrodes were removed from the ECG before reconstruction to measure the effect of electrode inclusion on reconstruction accuracy. The Left Leg (LL) electrode registered the largest drop in average reconstruction accuracy, from an R2 of 0.836 when the LL was included to 0.737 when excluded. Additionally, we conducted a correlation analysis to identify leads that behave similarly. We demonstrate that there exists a high correlation between leads I, II, aVL, aVF, V4, V5, and V6, which all occupy the bottom right quadrant in an ECG axis interpretation, and likely contain redundant information. Based on our analysis, we recommend the prioritization of electrodes RA, LA, LL, and V3 in any future lead collection devices, as they appear most important for full ECG reconstruction.

Deep Learning Algorithm Predicts Angiographic Coronary Artery Disease in Stable Patients Using Only a Standard 12-Lead Electrocardiogram.

BACKGROUND: Current electrocardiogram analysis algorithms cannot predict the presence of coronary artery disease (CAD), especially in stable patients. This study assessed the ability of an artificial intelligence algorithm (ECGio; HEARTio Inc, Pittsburgh, PA) to predict the presence, location, and severity of coronary artery lesions in an unselected stable patient population. METHODS: A cohort of 1659 stable outpatients was randomly divided into training (86%) and validation (14%) subsets, maintaining population characteristics. ECGio was trained and validated using electrocardiograms paired with retrospectively collected angiograms. Coronary artery lesions were classified in 2 analyses. The primary classification was no to mild (< 30% diameter stenosis [DS]) vs moderate (30%-70% DS) vs severe (≥ 70% DS) CAD. The secondary classification was yes/no based on ≥ 50% DS in any vessel. RESULTS: In the primary analysis, 22 patients had no angiographic CAD and were grouped mild CAD (56 patients, DS < 30%), 31 had moderate CAD (DS 30%-70%), and 113 had severe CAD (DS ≥ 70%). Weighted average sensitivity was 93.2%, and weighted average specificity was 96.4%. In the secondary analysis, 93 had significant CAD, and 128 did not. There was sensitivity of 93.1% and specificity of 85.6% in determining the presence of clinically significant disease (≥ 50% DS) in any vessel. ECGio was able to predict stenosis with average vessel error in the left anterior descending coronary artery of 18%, the left circumflex coronary artery of 19%, the right coronary artery of 18%, and the left main coronary artery of 8%. CONCLUSIONS: This study strongly suggests that it is possible to use an artificial intelligence algorithm to determine the presence and severity of CAD in stable patients, using data from a 12-lead electrocardiogram.