Emerging ECG methods for acute coronary syndrome detection: Recommendations & future opportunities
Salah Al-Zaiti1, Robert Macleod2, Peter Van Dam3
1Department of Acute & Tertiary Care, University of Pittsburgh, Pittsburgh, PA, USA.
Insights
The 12-lead electrocardiogram (ECG) is suboptimal for detecting myocardial ischemia. Novel computational ECG methods analyzing waveform features, body surface potentials, inverse solutions, and artificial intelligence show promise for improving diagnostic accuracy.
Area of Science:
- Cardiology
- Biomedical Engineering
- Medical Diagnostics
Background:
- The 12-lead electrocardiogram (ECG) is a primary tool for assessing symptomatic coronary artery disease.
- Current ECG interpretation for myocardial ischemia primarily relies on ST segment amplitude, which has limited sensitivity and specificity.
- This focus misses opportunities to utilize other ECG waveform characteristics for improved ischemia detection.
Purpose of the Study:
- To explore advanced computational ECG methods for enhancing the detection of myocardial ischemia.
- To review emerging techniques that improve upon traditional ECG analysis for ischemia diagnosis.
- To highlight opportunities for increasing the diagnostic accuracy of ECG in identifying myocardial ischemia.
Main Methods:
- Analyzing novel ECG waveform features beyond ST-T amplitude.
- Utilizing body surface potential mapping (BSPM) for enhanced spatial ECG coverage.
- Developing inverse ECG solutions to reconstruct activation and recovery pathways.
- Applying artificial intelligence (AI) techniques to ECG data for ischemia signature detection.
Main Results:
- Emerging computational ECG approaches demonstrate potential to significantly increase sensitivity for myocardial ischemia detection.
- Novel methods leverage diverse ECG signal characteristics and advanced analytical techniques.
- These approaches offer a more comprehensive analysis of the ECG for diagnosing ischemic events.
Conclusions:
- Advanced computational ECG methods, including AI and BSPM, offer significant potential to improve myocardial ischemia detection.
- Further research and prospective clinical validation are crucial for translating these techniques into routine clinical practice.
- Enhancing ECG analysis beyond traditional ST-T amplitude measures is a key opportunity for improving cardiovascular diagnostics.
Abstract:
Despite being the mainstay for the initial noninvasive assessment of patients with symptomatic coronary artery disease, the 12‑lead ECG remains a suboptimal diagnostic tool for myocardial ischemia detection with only acceptable sensitivity and specificity scores. Although myocardial ischemia affects the configuration of the QRS complex and the STT waveform, current guidelines primarily focus on ST segment amplitude, which constitutes a missed opportunity and may explain the suboptimal diagnostic performance of the ECG. This possible opportunity and the low cost and ease of use of the ECG provide compelling motivation to enhance the diagnostic accuracy of the ECG to ischemia detection. This paper describes numerous computational ECG methods and approaches that have been shown to dramatically increase ECG sensitivity to ischemia detection. Briefly, these emerging approaches can be conceptually grouped into one of the following four approaches: (1) leveraging novel ECG waveform features and signatures indicative of ischemic injury other than the classical ST-T amplitude measures; (2) applying body surface potentials mapping (BSPM)-based approaches to enhance the spatial coverage of the surface ECG to detecting ischemia; (3) developing an inverse ECG solution to reconstruct anatomical models of activation and recovery pathways to detect and localize injury currents; and (4) exploring artificial intelligence (AI)-based techniques to harvest ECG waveform signatures of ischemia. We present recent advances, shortcomings, and future opportunities for each of these emerging ECG methods. Future research should focus on the prospective clinical testing of these approaches to establish clinical utility and to expedite potential translation into clinical practice.
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