ECG Prediction with Convolutional Neural Networks (CNN)

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@article{1708843,
      author = {Akshay Bhatia, Kamal Jyoti, Ayushi Upreti, Aniket Tripathi, Simran Sharma},
      title = {ECG Prediction with Convolutional Neural Networks (CNN)},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {7},
      number = {6},
      pages = {517-526},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1708843.pdf},
      abstract = {Doctors use electrocardiogram (ECG) signals to diagnose various cardiovascular diseases, which are a major cause of death all over the world. Interpreting an ECG manually takes a lot of time, can be based on the doctor?s opinions, and might result in inconsistent diagnoses. As a result, scientists commonly use CNNs, which are designed to understand changes in data at different levels, to predict and classify ECG signals. This paper aims to show how CNNs are useful for forecasting cardiac events and for ECG signal classification with accuracy and using a few hand-crafted features. We discuss several CNN models that are fitted for ECG, including those built for segmented data and those that add recurrent steps for studying sequence dependency. We additionally explore how to filter noise, normalize the ECG data, and segment them before they are fed into the model. CNN-based models are evaluated against common machine learning techniques and are found to be more accurate, sensitive, and specific in picking out arrhythmias, myocardial infarctions, and other illnesses of the heart. To address the problem of a few labelled ECG datasets, we apply transfer learning and data augmentation for our models. Using saliency maps and CAMs, it is possible to interpret the results of CNN models, which contributes to the acceptance and trust of AI-based diagnoses among users. In summary, CNN-based systems make cardiology much more effective by providing doctors with real-time, easy-to-scale, and non-invasive support for ECG analysis. In summary, we look ahead by discussing federated learning, the use of the technology on mobile devices, and the application of models in different populations to widen the impact of ECG-based AI in the real world.},
      keywords = {Convolutional Neural Networks (CNN); ECG Signal Processing; Deep Learning; Cardiac Disease Prediction; Biomedical AI},
      month = {December},
  }