Deep Transfer Learning for Chronic Obstructive Pulmonary Disease Detection Utilizing Electrocardiogram Signals
importance 6/10
ai assisted
ECG Monitoring and Analysis respiratory diagnostics stage: research
Problem
Diagnosing COPD normally requires spirometer tests and a lengthy hospital workup, so the disease often goes undetected until its final stage.
Why it matters
COPD progresses slowly and concealed, and late detection can make it incurable.
What the AI did
A deep transfer learning system classifies COPD from the patient's ECG heart signal alone, which can be captured by modern smart devices.
Results
not stated in source
Impact
The evidence supports only that a first ECG-based COPD classification system and annotated dataset were proposed.
Record
| Venue | IEEE Access |
|---|---|
| Published | 2023 |
| Work type | article |
| Authors | Inanc Moran, D. Turgay Altılar, Muhammed Kürşad Uçar, Cahit Bílgín, Mehmet Recep Bozkurt |
| Citations | 38 (OpenAlex, at import time) |
| References | 50 |
| Model or method | deep transfer learning (no specific architecture named) |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1109/access.2023.3269397 |
| Open access | gold |
| Keywords | COPD, ECG, deep transfer learning, smart devices, diagnosis |
Importance rating
6
significant — thousands to millions affected
Abstract states COPD progresses concealed until a final, incurable stage; ECG-based screening from smart devices would be a genuinely new early-detection capability.