{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/detection-of-inferior-myocardial-infarction","title":"Detection of Inferior Myocardial Infarction using Shallow Convolutional Neural Networks","arxiv_id":"1710.01115","date":"2017-10-03","proceeding":null,"authors":["Tahsin Reasat","Celia Shahnaz"],"abstract":"Myocardial Infarction is one of the leading causes of death worldwide. This\npaper presents a Convolutional Neural Network (CNN) architecture which takes\nraw Electrocardiography (ECG) signal from lead II, III and AVF and\ndifferentiates between inferior myocardial infarction (IMI) and healthy\nsignals. The performance of the model is evaluated on IMI and healthy signals\nobtained from Physikalisch-Technische Bundesanstalt (PTB) database. A\nsubject-oriented approach is taken to comprehend the generalization capability\nof the model and compared with the current state of the art. In a\nsubject-oriented approach, the network is tested on one patient and trained on\nrest of the patients. Our model achieved a superior metrics scores (accuracy=\n84.54%, sensitivity= 85.33% and specificity= 84.09%) when compared to the\nbenchmark. We also analyzed the discriminating strength of the features\nextracted by the convolutional layers by means of geometric separability index\nand euclidean distance and compared it with the benchmark model.","url_abs":"http://arxiv.org/abs/1710.01115v4","url_pdf":"http://arxiv.org/pdf/1710.01115v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"detection-of-inferior-myocardial-infarction","repo_url":"https://github.com/Reasat/cnn-imi","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"electrocardiography-ecg","task_name":"Electrocardiography (ECG)"},{"task_slug":"specificity","task_name":"Specificity"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}