Automated serial ECG comparison improves computerized interpretation of 12-lead ECG

• Published in: Journal of electrocardiology 2012-11, Vol.45 (6), p.561-565
• Main Authors: Gregg, Richard E., MS, Deluca, Daniel C., BSEE, Chien, Cheng-hao Simon, PhD, Helfenbein, Eric D., MSEE, Ariet, Mario, PhD
• Format: Article
• Language: English
• Subjects: 12-lead ECG analysis; Algorithms; Arrhythmias, Cardiac - diagnosis; Artificial Intelligence; Automated serial ECG comparison; Cardiovascular; Diagnosis, Computer-Assisted - methods; Electrocardiography - methods; Humans; Pattern Recognition, Automated - methods; Reproducibility of Results; Sensitivity and Specificity
• ISSN: 0022-0736; 1532-8430
• DOI: 10.1016/j.jelectrocard.2012.07.021

Abstract Background Interpretation of a patient's 12-lead ECG frequently involves comparison to a previously recorded ECG. Automated serial ECG comparison can be helpful not only to note significant ECG changes but also to improve the single-ECG interpretation. Corrections from the previous ECG are carried forward by the serial comparison algorithm when measurements do not change significantly. Methods A sample of patients from three hospitals was collected with two or more 12-lead ECGs from each patient. There were 233 serial comparisons from 143 patients. 41% of patients had two ECGs and 59% of patients had more than two ECGs. ECGs were taken from a difficult population as measured by ECG abnormalities, 197/233 abnormal, 11/233 borderline, 14/233 otherwise-normal and 11/233 normal. ECGs were processed with the Philips DXL algorithm and then in time order for each patient with the Philips serial comparison algorithm. To measure accuracy of interpretation and serial change, an expert cardiologist corrected the ECGs in stages. The first ECG was corrected and used as the reference for the second ECG. The second ECG was then corrected and used as the reference for the third ECG and so on. At each stage, the serial comparison algorithm compared an unedited ECG to an earlier edited ECG. Interpretation accuracy was measured by comparing the algorithm to the cardiologist on a statement by statement basis. The effect of serial comparison was measured by the sum of interpretive statement mismatches between the algorithm and cardiologist. Statement mismatches were measured in two ways, (1) exact match and (2) match within the same diagnostic category. Results The cardiologist used 910 statements over 233 ECGs for an average number of 3.9 statements per ECG and a mode of 4 statements. When automated serial comparison was used, the total number of exact statement mismatches decreased by 29% and the total same-category statement mismatches decreased by 47%. Conclusion Automated serial comparison improves interpretation accuracy in addition to its main role of noting differences between ECGs.