This project focuses on the early detection of silent heart attacks, one of the most pressing health concerns, through the innovative application of machine learning models to analyze electrocardiogram (ECG) waveforms. Silent heart attacks are a type of myocardial infarction that often go undiagnosed due to their asymptomatic nature, posing a significant health risk due to the delay in intervention and treatment which, consequently, leads to a higher risk of heart failure. The comprehensive Nightingale Open Science dataset, a meticulously curated dataset that aligns ECG waveforms with cardiac ultrasound data, served as the primary data source for this project. The primary objective of the study was the identification of Regional Wall Motion Abnormalities (RWMA), which are key indicators of prior myocardial infarctions. Various machine learning nmethodologies, including the more traditional Logistic Regression and Support Vector Machine (SVM), as well as the state-of-the-art Recurrent Neural Network (RNN), were evaluated in this research. The results demonstrated the immense potential of machine learning models in the detection of silent heart attacks through the analysis of ECG waveforms. Among the models evaluated, the RNN model stood out as it outperformed the other models in detecting RWMA due to its innate ability to capture sequential and time-dependent data. However, it is important to note that challenges such as data imbalance and overfitting were encountered during this study, highlighting the need for further refinement and optimization of the model.
Pramanik, Rajiv, Bhumil Shah, Anna Roth, Honga Wei, Ted Castillo, Katie Lin, Sachin Shah, et al. “Diagnosing ’Silent’ Heart Attack Using ECG Waveforms.” Nightingale Open Science, 2021. https://doi.org/10.48815/N54W2V.