Publication2026
ECG Arrhythmia Detection Using Lightweight 1DCNN-Bi-LSTM Technique for Cardiovascular Disease
The record
- Type
- Publication
- Date
- 1 January 2026
- Level
- National
- Journal
- Lecture Notes in Networks and Systems
- Volume
- 1522 LNNS
- Pages
- 393-407
- Published
- 1 January 2026
- DOI
- 10.1007/978-981-96-9932-2_27
- Scopus ID
- 105028156486
- ISSN
- 23673370
- Authors
- 5
- Open access
- No
- Citations
- 0
- 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
Lightweight 1DCNN-Bi-LSTM deep learning model for ECG arrhythmia detection achieves 99.4% accuracy on 45,152 samples, directly supporting cardiovascular disease diagnosis and patient health outcomes.
- 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
AI innovation for cardiac disease detection advances research capacity in medical artificial intelligence and embedded healthcare systems.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-1.2 Health and well-being
Cardiovascular disease detection technology enables early intervention and improved health outcomes for cardiac patients.
- 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. “ECG Arrhythmia Detection Using Lightweight 1DCNN-Bi-LSTM Technique for Cardiovascular Disease”. Kortex.