Automated Myocardial Infarction Screening Using Morphology-Based Electrocardiogram Biomarkers

• Main Authors: Jahnavi, Dhaladhuli, Dash, Ashutosh, Ghosh, Nirmalya, Patra, Amit
• Format: Conference Proceeding
• Language: English
• Subjects: Algorithms; Electrocardiography; Feature extraction; Heart; Humans; Myocardial Infarction - diagnosis; Myocardial Ischemia; Myocardium; Recording; Signal Processing, Computer-Assisted; Training; Visualization; Wearable computers
• ISSN: 2694-0604
• DOI: 10.1109/EMBC40787.2023.10340935

Ischemic heart disease (IHD), a critical and dreadful cardiovascular disease, is a leading cause of death globally. The steady progress of IHD leads to an irreversible condition called myocardial infarction (MI). The detection of MI can be done by observing the altered electrocardiogram (ECG) characteristics. Often, automated ECG analysis is preferred in place of visual inspection to reduce time and ensure reliable detection even when the recording quality is not very good. This paper presents an automated approach to classify recent MI, past MI, and normal sinus rhythm (NSR) classes based on the morphological features of the ECG. In clinical practice, a standard 12-lead ECG setup is typically employed to identify MI. However, acquiring a 12-lead ECG is not always convenient. Hence, in this study, we have explored the possibility of using a minimal number of ECG leads by deriving the augmented limb leads using leads I and II. A well-known and widely used ensemble machine learning tool, the random forest (RF) classifier is trained using features extracted from the derived augmented limb leads and their combinations. An RF classifier built using features extracted from all limb leads has outperformed classifiers built on combinations of them with five-fold cross-validation training accuracy of 97.9 (±0.008) % and testing accuracy of 98 %.Clinical relevance- As high sensitivity is reported in identifying recent MI and past MI classes, the proposed approach is suitable for preventative healthcare applications since it is less likely that subjects with recent or past MI will be misclassified. Due to its low computational complexity, better interpretability, and comparable performance to the state-of-the-art results, the proposed approach can be employed in clinical and cardiac health screening applications. It also has the potential to be employed in remote monitoring with mobile and wearable devices because it is built on features extracted from only lead I and II ECG recordings.