Karhunen-Loève representation distinguishes ST-T wave morphology differences in emergency department chest pain patients with non-ST-elevation myocardial infarction versus nonacute coronary syndrome

Abstract

Patients presenting to the emergency department with chest pain are triaged to early reperfusion therapies based on their initial 12-lead electrocardiogram (ECG). The standard 12-lead ECG lacks sensitivity to detect acute myocardial infarction (AMI). Electrocardiographic diagnosis of non-ST-elevation myocardial infarction (non-STEMI) is especially difficult and is delayed until cardiac biomarkers turn positive, indicating onset of myocardial necrosis.

Study aims: The purpose of this analysis was to extract global ST-T waveform features from patients with chest pain, compare these features in patients with and without AMI, and then identify features that distinguish diagnostic categories.

Methods: This is a secondary analysis of data from the Ischemia Monitoring and Mapping in the Emergency Department in Appropriate Triage and Evaluation of Acute Ischemic Myocardium study, a prospective clinical trial in which patients were attached to Holter monitor devices to obtain 24 hours of continuous ECG data. Digital recordings from 176 patients were analyzed: 88 with AMI (STEMI and non-STEMI) and 88 without AMI or unstable angina. The non-acute coronary syndrome (ACS) group was further subdivided into those with non-ACS cardiac conditions such as heart failure and those without cardiac disease who had noncardiac chest pain. For each patient, 10 consecutive waveforms were obtained within the first 120 minutes of emergency department presentation. The waveforms were time-aligned to the QRS, signal-averaged, baseline-adjusted. ST-T waveforms were complied according to diagnostic category and pooled for further analysis. Eigenvector-lead feature coefficients (Karhunen-Loève [K-L] coefficients) were obtained for each patient by taking the dot product of the ST-T wave (ST segment or entire waveform) and the first 3 common eigenvectors, producing 24 K-L coefficients. Cumulative probability distribution function curves were plotted for each diagnostic category. Statistical significance of category coefficient distribution differences was determined. Multinomial regression was used to assess accuracy of feature coefficients to predict diagnostic category.

Results: Non-STEMI and non-ACS cardiac category K-L coefficient curves were statistically different in 11 of 24 feature curves (P < .001-.047). ST-segment (50 samples) coefficients predicted non-ACS cardiac patients 11.5% more often (P = .02) than those derived from the entire ST-T wave.

Conclusion: Patients diagnosed with non-STEMI have distinct distribution of K-L coefficients compared with non-ACS cardiac patients. Coefficients from the first 50 samples of the ST-T wave (ST segment) better predict diagnostic category than do coefficients derived from the entire ST-T wave. Karhunen-Loève coefficient feature analysis may provide early diagnostic information to distinguish patients with non-STEMI vs non-ACS cardiac patients.

Figure 1

Sample tree showing diagnostic category constituent numbers.

Figure 2

Example of cumulative probability density function curves, comparing K-L coefficient distributions from non-STEMI and non-ACS Cardiac patients in eigenvector-lead EV1-CH1. Note separation of distributions over majority of coefficient range.