Improving myocardial infarction diagnosis with Siamese network-based ECG analysis.

{"sci_version": "1.0", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39883662", "kind": "science-record", "revision": 1, "content_hash": "sha256:1742cc4cda3a6771d340d64440fdf0067dc0f1e5d4f4298e86cc0cf84f377e5f", "created_at": "2026-11-01T20:30:10Z", "updated_at": "2026-11-01T20:30:10Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Improving myocardial infarction diagnosis with Siamese network-based ECG analysis.", "doi": "10.1371/journal.pone.0313390", "pmid": "39883662", "authors": ["Vaibhav Gadag", "Simrat Singh", "Anshul Harish Khatri", "Shruti Mishra", "Sandeep Kumar Satapathy", "Sung-Bae Cho", "Abishi Chowdhury", "Amrit Pal", "Sachi Nandan Mohanty"], "journal": "PloS one", "year": 2025, "abstract": "BACKGROUND: Heart muscle damage from myocardial infarction (MI) is brought on by insufficient blood flow. The leading cause of death for middle-aged and older people worldwide is myocardial infarction (MI), which is difficult to diagnose because it has no symptoms. Clinicians must evaluate electrocardiography (ECG) signals to diagnose MI, which is difficult and prone to observer bias. To be effective in actual practice, an automated, and computerized detection system for Myocardial Infarction using ECG images, must meet a number of criteria.\nOBJECTIVE: In an actual clinical situation, these requirements-such as dependability, simplicity, and superior decision-making abilities-remain crucial. In the current work, we have developed a model using a dataset that consists of a combination of 928 ECG images taken from publicly available Mendeley Data. It was converted into three classes Myocardial Infarction, Abnormal heartbeat, and Normal.\nMETHODS: The dataset is then imported, pre-processed, and split into a 70:20:10 ratio of training, validation, and testing. It is then trained using the Siamese Network Model.\nRESULTS: The classification accuracy comes out to be 98%. The algorithm works excellently with datasets having class imbalance by taking pair of images as input. The validation and testing classification matrix is then generated and the evaluation metrics for both of them come out to be a near-perfect value.\nCONCLUSION: In this study, we developed the ECG signals based early detection of cardiovascular diseases with Siamese network model.", "claims": [{"id": "claim-data-access", "statement": "The dataset is available in the public repository https://physionet.org/content/ptbdb/1.0.0/, DOI: https://doi.org/10.13026/C28C71.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39883662", "locator": "sections[40] \"Data Availability\" paragraph 2", "quote": "The dataset is available in the public repository https://physionet.org/content/ptbdb/1.0.0/, DOI: https://doi.org/10.13026/C28C71."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The classification accuracy comes out to be 98%.", "kind": "quantitative", "target": {"value": 98.0, "unit": "per cent", "tolerance": null, "source": "The classification accuracy comes out to be 98%."}, "evidence": [{"asset": "source:pmid:39883662", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "The classification accuracy comes out to be 98%."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The classification accuracy comes out to be 98%.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:39883662"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R4", "reachable": false, "accessions": [], "resolved_accessions": [], "statements": ["The dataset is available in the public repository https://physionet.org/content/ptbdb/1.0.0/, DOI: https://doi.org/10.13026/C28C71."], "basis": "The paper names no accession to its source data, so the data has to come from the authors."}}, "can_run": {"dsl_operations": [], "assets": ["source:pmid:39883662"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:None"]}, "gaps": ["data:accession_absent"]}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:45:31Z", "independence": "single_reader"}, "evidence_check": {"method": null, "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 12989, "completion_tokens": 1516, "cached_tokens": 256}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:39883662", "type": "paper", "label": "pmid:39883662"}, {"id": "source:pmid:39883662", "type": "manuscript", "label": "PMID 39883662 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:39883662", "type": "paper", "label": "pmid:39883662"}, {"id": "source:pmid:39883662", "type": "manuscript", "label": "PMID 39883662 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-39883662", "type": "extraction", "used": ["source:pmid:39883662"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-None", "type": "accession_resolution", "used": ["data:geo:None"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": null, "parent_hash": null, "fork_of": null, "generation": 0}, "gaps": ["execution.not_run", "method", "operations", "review.human_critique", "review.independent_reader", "review.machine_critique", "step:obtain_source_data blocked on data:accession_absent"]}