Deep Learning for Cardiologist-level Myocardial Infarction Detection in Electrocardiograms
This work addresses a critical healthcare problem for cardiology by providing an automated, high-accuracy detection tool, though it is incremental as it adapts an existing neural network model to a new domain.
The paper tackled myocardial infarction detection in electrocardiograms by designing domain-inspired neural network models, achieving cardiologist-level performance with 99.43% accuracy on a record-wise split and 97.83% on a patient-wise split using only 10 seconds of raw ECG data.
Myocardial infarction is the leading cause of death worldwide. In this paper, we design domain-inspired neural network models to detect myocardial infarction. First, we study the contribution of various leads. This systematic analysis, first of its kind in the literature, indicates that out of 15 ECG leads, data from the v6, vz, and ii leads are critical to correctly identify myocardial infarction. Second, we use this finding and adapt the ConvNetQuake neural network model--originally designed to identify earthquakes--to attain state-of-the-art classification results for myocardial infarction, achieving $99.43\%$ classification accuracy on a record-wise split, and $97.83\%$ classification accuracy on a patient-wise split. These two results represent cardiologist-level performance level for myocardial infarction detection after feeding only 10 seconds of raw ECG data into our model. Third, we show that our multi-ECG-channel neural network achieves cardiologist-level performance without the need of any kind of manual feature extraction or data pre-processing.