Publication2021
Detection of ventricular arrhythmia using hybrid time–frequency-based features and deep neural network
The record
- Type
- Publication
- Date
- 1 March 2021
- Level
- National
- Journal
- Physical and Engineering Sciences in Medicine
- Volume
- 44
- Issue
- 1
- Pages
- 135-145
- Published
- 1 March 2021
- DOI
- 10.1007/s13246-020-00964-2
- Scopus ID
- 85099089362
- ISSN
- 26624729
- Authors
- 4
- Open access
- No
- Citations
- 46
- 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
Deep neural network approach for ventricular tachyarrhythmia classification achieves 99.2% accuracy, enabling timely detection of sudden cardiac death precursors.
- 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
Novel hybrid feature extraction combining wavelet, empirical mode, and variable mode decomposition with deep learning advances biomedical signal processing research.
- 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
Automated cardiac arrhythmia detection system directly improves health outcomes through rapid diagnosis and automated external defibrillator intervention.
- 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. “Detection of ventricular arrhythmia using hybrid time–frequency-based features and deep neural network”. Kortex.