Publication2020
Machine Learning Approach to Detect Cardiac Arrhythmias in ECG Signals: A Survey
- Good Health and Well-being
- Empowered Indians
The record
- Type
- Publication
- Date
- 1 August 2020
- Level
- National
- Journal
- Irbm
- Volume
- 41
- Issue
- 4
- Pages
- 185-194
- Published
- 1 August 2020
- DOI
- 10.1016/j.irbm.2019.12.001
- Scopus ID
- 85077376130
- ISSN
- 19590318
- Authors
- 4
- Open access
- No
- Citations
- 200
- International co-authorship
- No
- Co-author countries
- IN
Classification
Every label below carries the reasoning it was assigned on, the confidence band it was assigned in, and what produced it. The rubric is published.
UN Sustainable Development Goals
3.4 Reduce premature mortality from non-communicable diseases and promote mental health and well-being
Machine learning approaches for automatic electrocardiogram beat classification and cardiac arrhythmia detection support computer-aided diagnosis systems.
- High confidence
- The subject matter is a strong and direct inference from the record type, the funding or organising body, or the programme context, even where the title alone is thin. Example: an ICMR-funded project against SDG 3, on the strength of the agency.
- Judged
- Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-1 Empowered Indians
Derived through the seeded SDG-to-Viksit-Bharat crosswalk from the primary SDG 3 label. Machine learning approaches for automatic electrocardiogram beat classification and cardiac arrhythmia detection support computer-aided diagnosis systems.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Each label above carries the instrument and the version that produced it. How each classification is reached.
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- 1
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Citing this record
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KIIT Deemed to be University. “Machine Learning Approach to Detect Cardiac Arrhythmias in ECG Signals: A Survey”. Kortex.