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Evolution of single-lead ECG for STEMI detection using a deep learning approach
C Michael Gibson1, Sameer Mehta2, Mariana R S Ceschim2
1Cardiovascular Division, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
Insights
A new AI algorithm uses single-lead ECGs for faster ST-Elevation Myocardial Infarction (STEMI) detection. This tool shows promise for early diagnosis and improved patient outcomes in acute myocardial infarction cases.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Diagnostics
Background:
- Symptom-to-door times for ST-Elevation Myocardial Infarction (STEMI) are prolonged due to diagnostic delays.
- Current diagnostic pathways for STEMI can be time-consuming, impacting patient outcomes.
Purpose of the Study:
- To develop and validate a machine learning (ML)-guided algorithm for rapid STEMI detection using single-lead electrocardiograms (ECGs).
- To enhance the speed and accuracy of STEMI diagnosis through an AI-powered approach.
Main Methods:
- Utilized a large dataset of 8,511 ECGs from the Latin America Telemedicine Infarct Network (LATIN) for model training and validation.
- Implemented 1-D convolutional neural networks for STEMI detection (STEMI/Not-STEMI) and localization (anterior, inferior, lateral walls).
- Preprocessed ECG data by detecting QRS complexes and segmenting individual heartbeats for analysis.
Main Results:
- The AI-guided single-lead ECG strategy achieved 90.5% accuracy for STEMI detection using Lead V2.
- The STEMI localization model showed promising results for anterior and inferior wall STEMIs, with areas for improvement in lateral wall detection.
Conclusions:
- AI-enhanced single-lead ECGs represent a viable and accurate screening tool for STEMI.
- This technology can be integrated into wearable devices, offering a potential pathway for earlier patient treatment and improved myocardial infarction outcomes.
Background:
While ST-Elevation Myocardial Infarction (STEMI) door-to-balloon times are often below 90 min, symptom to door times remain long at 2.5-h, due at least in part to a delay in diagnosis.
Objectives:
To develop and validate a machine learning-guided algorithm which uses a single‑lead electrocardiogram (ECG) for STEMI detection to speed diagnosis.
Methods:
Data was extracted from the Latin America Telemedicine Infarct Network (LATIN), a population-based Acute Myocardial Infarction (AMI) program that provides care to patients in Brazil, Colombia, Mexico, and Argentina through telemedicine.
Sample:
the first dataset was comprised of 8511 ECGs that were used for various machine learning experiments to test our Deep Learning approach for STEMI diagnosis. The second dataset of 2542 confirmed STEMI diagnosis EKG records, including specific ischemic heart wall information (anterior, inferior, and lateral), was derived from the previous dataset to test the STEMI localization model. Preprocessing: Detection of QRS complexes by wavelet system, segmentation of each EKG record into individual heartbeats with fixed window of 0.4 s to the left and 0.9 s to the right of main. Training & Testing: 90% and 10% of the total dataset, respectively, were used for both models.
Classification:
two 1-D convolutional neural networks were implemented, two classes were considered for first models (STEMI/Not-STEMI) and three classes for the second model (Anterior/Inferior/Lateral) each corresponding to the heart wall affected. These individual probabilities were aggregated to generate the final label for each model.
Results:
The single‑lead ECG strategy was able to provide an accuracy of 90.5% for STEMI detection with Lead V2, which also yielded the best results overall among individual leads. STEMI Localization model provided promising results for anterior and inferior wall STEMIs but remained suboptimal for Lateral STEMI.
Conclusions:
An Artificial Intelligence-enhanced single‑lead ECG is a promising screening tool. This technology provides an autonomous and accurate STEMI diagnostic alternative that can be incorporated into wearable devices, potentially providing patients reliable means to seek treatment early and offers the potential to thereby improve STEMI outcomes in the long run.
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