Contributions of myocardial ischemia and heart rate to ST segment changes in patients with or without coronary artery disease

Reliable Detection of Myocardial Ischemia Using Machine Learning Based on Temporal-Spatial Characteristics of Electrocardiogram and Vectorcardiogram

Background: Myocardial ischemia is a common early symptom of cardiovascular disease (CVD). Reliable detection of myocardial ischemia using computer-aided analysis of electrocardiograms (ECG) provides an important reference for early diagnosis of CVD. The vectorcardiogram (VCG) could improve the performance of ECG-based myocardial ischemia detection by affording temporal-spatial characteristics related to myocardial ischemia and capturing subtle changes in ST-T segment in continuous cardiac cycles. We aim to investigate if the combination of ECG and VCG could improve the performance of machine learning algorithms in automatic myocardial ischemia detection. Methods: The ST-T segments of 20-second, 12-lead ECGs, and VCGs were extracted from 377 patients with myocardial ischemia and 52 healthy controls. Then, sample entropy (SampEn, of 12 ECG leads and of three VCG leads), spatial heterogeneity index (SHI, of VCG) and temporal heterogeneity index (THI, of VCG) are calculated. Using a grid search, four SampEn and two features are selected as input signal features for ECG-only and VCG-only models based on support vector machine (SVM), respectively. Similarly, three features (S I , THI, and SHI, where S I is the SampEn of lead I) are further selected for the ECG + VCG model. 5-fold cross validation was used to assess the performance of ECG-only, VCG-only, and ECG + VCG models. To fully evaluate the algorithmic generalization ability, the model with the best performance was selected and tested on a third independent dataset of 148 patients with myocardial ischemia and 52 healthy controls. Results: The ECG + VCG model with three features (S I ,THI, and SHI) yields better classifying results than ECG-only and VCG-only models with the average accuracy of 0.903, sensitivity of 0.903, specificity of 0.905, F1 score of 0.942, and AUC of 0.904, which shows better performance with fewer features compared with existing works. On the third independent dataset, the testing showed an AUC of 0.814. Conclusion: The SVM algorithm based on the ECG + VCG model could reliably detect myocardial ischemia, providing a potential tool to assist cardiologists in the early diagnosis of CVD in routine screening during primary care services.

Evaluation of non-ST segment elevation acute chest pain syndromes with a novel low-profile continuous imaging ultrasound transducer

BACKGROUND

This investigation was designed to test the hypothesis that continuous cardiac imaging using an ultrasound transducer developed in our laboratory (ContiScan) is superior to electrocardiogram (ECG) monitoring in the diagnosis of coronary artery disease (CAD) in patients with acute non-ST segment elevation chest pain syndromes.

METHODS

Seventy patients with intermediate to high probability of CAD who presented with typical anginal chest pain and no evidence of ST segment elevation on the ECG were studied. The 2.5-MHz transducer is spherical in its distal part mounted in an external housing to permit steering in 360 degrees. The transducer was placed at the left sternal border to image the left ventricular short-axis view and recorded on video tape at baseline, during and after episodes of chest pain. Two ECG leads were continuously monitored. The presence of CAD was confirmed by coronary arteriography or nuclear or echocardiographic stress testing.

RESULTS

Twenty-four patients had regional wall motion abnormalities (RWMA) on their initial echo which were unchanged during the period of monitoring. All had evidence of CAD. Twenty-eight patients had transient RWMA. All had evidence of CAD. Eighteen patients had normal wall motion throughout the monitoring period, 14 of these had no evidence of CAD, and four had evidence of CAD. These four patients did not have chest pain during monitoring. The sensitivity, specificity, and accuracy of echocardiographic monitoring for diagnosing non-ST elevation myocardial infarction was 88%, 100%, and 91% respectively. The sensitivity, specificity, and accuracy of the ECG for diagnosis of CAD were 31%, 100%, and 52%, respectively. Echocardiography was superior to ECG (P < 0.001).

CONCLUSIONS

The data indicate that continuous cardiac imaging is superior to ECG monitoring for the diagnosis of CAD in patients presenting with acute non-ST segment elevation chest pain syndromes. This technique could be a useful adjunct to ECG monitoring for myocardial ischemia in the acute care setting.

ST changes and temporal relation to the J point during heart rate increase and myocardial ischemia

There is no concensus concerning where in the ST segment to measure. We studied the relation between different J point intervals to ST results during tachycardia and ischemia. Symptomatic (anesthetized) patients with coronary artery disease were paced at ascending incremental levels until they became ischemic. ST vector magnitude and ST vector change from baseline (STC-VM) as well as the sum of ST changes from all 12 electrocardiogram (ECG) leads (ECG ST sum) were measured at J point 0 millisecond, J + 20, J + 60, and J + 80 milliseconds for 34 patients. ST segments increased in similar fashion during pacing and ischemia. There was no difference in ST results when measurement was performed at different time intervals for both STC-VM and ECG ST sum. We conclude that ST assessment by ST change from baseline is not affected by different J point intervals during increased heart rate and ischemia in this clinical model of pacing-induced ischemia and vectorcardiographic ST analysis.