Download PDFOpen PDF in browserDeep Learning for Cardiologist-level Myocardial Infarction Detection in ElectrocardiogramsEasyChair Preprint 15432 pages•Date: September 22, 2019AbstractHeart disease is the leading cause of death worldwide. Amongst patients with cardiovascular diseases, myocardial infarction is the main cause of death. Thus, detection of myocardial infarction in a timely manner is a serious challenge with a significant potential for impact. Here, we study the impact of multiple channels of observation to correctly classify heart conditions, finding that lead I and lead II are critical to obtain correct classifications using data from the Physikalisch-Technische Bundesanstalt (PTB) database. Based on these findings, we develop a convolutional neural network to detect myocardial infarction using lead I and lead II electrocardiogram (ECG) signals. Our approach differs from others in the community in that it does not require any kind of manual feature extraction or pre-processing of any kind. Rather, the raw ECG signal is fed into the neural network. When evaluated, the model achieves a 99.15% accuracy, reaching cardiologist-level performance level for myocardial infarction detection. Preliminary experiments indicate that coupling this neural network model with a denoising deep learning model increases classification accuracy even further. Keyphrases: Applications of AI, computational biology, machine learning, statistical learning
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