An electrocardiographic algorithm for the prediction of the culprit lesion site in acute anterior myocardial infarction

Detection and classification of atrial and ventricular cardiovascular diseases to improve the cardiac health literacy for resource constrained regions

ECG is a non-invasive way of determining cardiac health by measuring the electrical activity of the heart. A novel detection technique for feature points P, QRS and T is investigated to diagnose various atrial and ventricular cardiovascular anomalies with ECG signals for ambulatory monitoring. Before the system is worthy of field trials, it is validated with several databases and recorded their response. The QRS complex detection is based on the Pan Tompkins algorithm and difference operation method that provides positive predictivity, sensitivity and false detection rate of 99.29%, 99.49% and 1.29%, respectively. Proposed novel T wave detection provides sensitivity of 97.78%. Also, proposed P wave detection provides positive predictivity, sensitivity and false detection rate of 99.43%, 99.4% and 1.15% for the control study (normal subjects) and 82.68%, 94.3% and 25.4% for the case (patients with cardiac anomalies) study, respectively. Disease detection such as arrhythmia is based on standard R-R intervals while myocardial infarction is based on the ST-T deviations where the positive predictivity, sensitivity and accuracy are observed to be 94.6%, 84.2% and 85%, respectively. It should be noted that, since the frontal leads are only used, the anterior myocardial infarction cases are detected with the injury pattern in lead avl and ST depression in reciprocal leads. Detection of atrial fibrillation is done for both short and long duration signals using statistical methods using interquartile range and standard deviations, giving very high accuracy, 100% in most cases. The system hardware for obtaining the 2 lead ECG signal is designed using commercially available off the shelf components. Small field validation of the designed system is performed at a Public Health Centre in Gujarat, India with 42 patients (both cases and controls). 78.5% accuracy was achieved during the field validation. It is thus concluded that the proposed method is ideal for improvisation in cardiac health monitoring outreach in resource constrained regions.

A comprehensive and easy-to-use ECG algorithm to predict the coronary occlusion site in ST-segment elevation myocardial infarction

BACKGROUND

Several electrocardiogram (ECG) criteria have been proposed to predict the location of the culprit occlusion in specific subsets of patients presenting with ST-segment elevation myocardial infarction (STEMI). The aim of this study was to develop, through an independent validation of currently available criteria, a comprehensive and easy-to-use ECG algorithm, and to test its diagnostic performance in real-world clinical practice.

METHODS

We analyzed ECG and angiographic data from 419 consecutive STEMI patients submitted to primary percutaneous coronary intervention over a one-year period, dividing the overall population into derivation (314 patients) and validation (105 patients) cohorts. In the derivation cohort, we tested >60 previously published ECG criteria, using the decision-tree analysis to develop the algorithm that would best predict the infarct-related artery (IRA) and its occlusion level. We further assessed the new algorithm diagnostic performance in the validation cohort.

RESULTS

In the derivation cohort, the algorithm correctly predicted the IRA in 88% of cases and both the IRA and its occlusion level (proximal vs mid-distal) in 71% of cases. When applied to the validation cohort, the algorithm resulted in 88% and 67% diagnostic accuracies, respectively. In a real-world comparative test, the algorithm performed significantly better than expert physicians in identifying the site of the culprit occlusion (P = .026 vs best cardiologist and P < .001 vs best emergency medicine doctor).

CONCLUSIONS

Derived from an extensive literature review, this comprehensive and easy-to-use ECG algorithm can accurately predict the IRA and its occlusion level in all-comers STEMI patients.

Identification of Coronary Culprit Lesion in ST Elevation Myocardial Infarction by Using Deep Learning

OBJECTIVE

Early revascularization of the occluded coronary artery in patients with ST elevation myocardial infarction (STEMI) has been demonstrated to decrease mortality and morbidity. Currently, physicians rely on features of electrocardiograms (ECGs) to identify the most likely location of coronary arteries related to an infarct. We sought to predict these culprit arteries more accurately by using deep learning.

METHODS

A deep learning model with a convolutional neural network (CNN) that incorporated ECG signals was trained on 384 patients with STEMI who underwent primary percutaneous coronary intervention (PCI) at a medical center. The performances of various signal preprocessing methods (short-time Fourier transform [STFT] and continuous wavelet transform [CWT]) with different lengths of input ECG signals were compared. The sensitivity and specificity for predicting each infarct-related artery and the overall accuracy were evaluated.

RESULTS

ECG signal preprocessing with STFT achieved fair overall prediction accuracy (79.3%). The sensitivity and specificity for predicting the left anterior descending artery (LAD) as the culprit vessel were 85.7% and 88.4%, respectively. The sensitivity and specificity for predicting the left circumflex artery (LCX) were 37% and 99%, respectively, and the sensitivity and specificity for predicting the right coronary artery (RCA) were 88.4% and 82.4%, respectively. Using CWT (Morlet wavelet) for signal preprocessing resulted in better overall accuracy (83.7%) compared with STFT preprocessing. The sensitivity and specificity were 93.46% and 80.39% for LAD, 56% and 99.7% for LCX, and 85.9% and 92.9% for RCA, respectively.

CONCLUSION

Our study demonstrated that deep learning with a CNN could facilitate the identification of the culprit coronary artery in patients with STEMI. Preprocessing ECG signals with CWT was demonstrated to be superior to doing so with STFT.

A new electrocardiographic algorithm to locate the occlusion in left anterior descending coronary artery

BACKGROUND

Early prediction of proximal left anterior descending coronary artery (LAD) occlusion is essential from a clinical point of view

HYPOTHESIS

To develop an electrocardiogram (ECG) algorithm based on ST-segment deviations to predict the location of occlusion of LAD as a culprit artery.

METHODS

ECG and angiographic findings were correlated in 100 patients with an ST-segment elevation myocardial infarction (MI) in precordial leads V(1), V(2), and V(4) through V(6).

RESULTS

ST-depression > or = 2.5 mm in leads III + ventricular fibrillation (VF) presents sensitivity (SE) of 77% and specificity (SP) of 84% for LAD occlusion proximal to the first diagonal artery (D1). ST-segment in III + VF isoelectric or elevated, presents SE of 44% and SP of 100% for LAD occlusion distal to D1. Subsequent analysis of the equation summation operator of ST-deviation in VR + V(1) - V(6) < 0, allows us to predict occlusion distal to first septal artery (S1) with 100% SP. On the other hand, any ST-depression in III + VF > 0.5 mm + summation operator of ST-deviation in VR + V(1) - V(6) > or = 0 identifies a high-risk group (lower ejection fraction, worse Killip findings, higher peak of CPK and CK-MB, and major adverse cardiac events [MACE]: death, reinfarction, recurrent angina, persistent left ventricular failure, or sustained ventricular arrhythmia during hospitalization).

CONCLUSIONS

This sequential ECG algorithm based on ST-segment deviations in different leads allowed us to predict the location of occlusion in LAD with good accuracy. Cases with proximal LAD occlusion present the most markers of poor prognosis. We recommend the use of the algorithm in everyday clinical practice.

Electrocardiographic predictors of proximal left anterior descending coronary artery occlusion

Proximal occlusion of the left anterior descending coronary artery (LAD) results in a less favorable prognosis in coronary angiography. Therefore, it is important to determine whether there are significant lesions in LAD by electrocardiography (ECG) before coronary angiography. Twelve-lead ECG was compared in 130 patients with significant lesions (≥70% stenosis) confined to proximal part of the LAD (P LAD group) and 492 patients with normal coronary angiography (control group). Fifty-nine patients in the P LAD group and 18 patients in the control group had signs of anterior myocardial infarction as shown by ST elevation (≥1.0 mV) in two consecutive pericardial leads or the presence of a pathological Q wave. An inverted U wave (biphasic T wave) in leads V1 to V4 had a sensitivity of 49.3% (35/71) in P LAD patients without signs of anterior myocardial infarction (MI) and 96.6 % (57/59; specificity, 66.6%; positive predictive value, 90.9 %) in the P LAD patients with signs of anterior MI. In the P LAD patients with signs of anterior MI, T inversion in V4–V5 had a lower sensitivity (67.0% [40/59]) than an inverted U wave. ST depression in inferior leads and ST depression in V5 were not useful markers of proximal LAD occlusion. In conclusions, an inverted U wave in V1 to V4 (or in each of these leads) and T inversion in V4–V5 are the best predictors of significant proximal LAD lesion, especially in patients with ECG findings of anterior MI.

Electrocardiographic diagnosis of acute myocardial infarction: Current concepts for the clinician

BACKGROUND

Over the past 2 decades, the 12-lead electrocardiogram has attained special significance for the diagnosis and triage of patients with chest pain because timely detection of myocardial injury and a rapid assessment of myocardium at risk proved pivotal to implementing effective reperfusion therapies during acute myocardial infarction. However, this wealth of information could still be underutilized by clinicians who may restrict their diagnostic quest in patients with chest pain to the more classic electrocardiographic signs.

METHODS

The medical literature on electrocardiographic manifestations of acute myocardial infarction was extensively reviewed.

RESULTS

The widespread utilization of both coronary angiography and methods to determine myocardial function and metabolism in patients with acute myocardial infarction over the last 10 years has provided the means for rigorous comparisons with electrocardiographic information. We summarize these electrocardiographic signs and patterns in terms of their relevance to the clinician to help reduce the incidence of "nondiagnostic electrocardiograms" and improve timely decision-making.

CONCLUSIONS

The electrocardiogram continues to be an invaluable tool in the initial evaluation of patients with chest pain. The plethora of data currently available on electrocardiographic changes correlating with myocardial injury allows clinicians to make faster and better decisions than ever before.

Right ventricular infarction mimicking anterior infarction: a case report

Right ventricular infarction usually occurs in association with inferior infarction, with no remarkable electrocardiographic signs in conventional leads. This report describes a patient with a previous inferior acute myocardial infarction who developed right ventricular infarction with significant anterior lead ST segment elevation (V1-V4) caused by the loss of two large right ventricular branches during a coronary angioplasty of the right coronary artery. The case is discussed and the literature is reviewed.