Publication2020
Cardiac arrhythmia classification using tunable Q-wavelet transform based features and support vector machine classifier
The record
- Type
- Publication
- Date
- 1 May 2020
- Level
- National
- Journal
- Biomedical Signal Processing and Control
- Volume
- 59
- Published
- 1 May 2020
- DOI
- 10.1016/j.bspc.2020.101875
- Scopus ID
- 85079549516
- ISSN
- 17468094
- Authors
- 2
- Open access
- No
- Citations
- 102
- 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 Ensure healthy lives and promote well-being for all at all ages
Cardiac arrhythmia classification using tunable Q-wavelet features achieves 99.27% accuracy in ECG beat classification for cardiac patient monitoring.
- 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
9.5 Enhance scientific research, upgrade technological capabilities, and increase researchers and R&D spending
Development of automated ECG analysis technique contributes to innovation in medical signal processing.
- Moderate confidence
- The alignment is plausible and defensible but rests on an assumption a reasonable reviewer could decline. Published, and surfaced in the review queue for a human to confirm or override.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-1.2 Health and well-being
Cardiac arrhythmia detection supports diagnosis and management of heart diseases.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
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- 1
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Citing this record
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KIIT Deemed to be University. “Cardiac arrhythmia classification using tunable Q-wavelet transform based features and support vector machine classifier”. Kortex.