Time-frequency transformation integrated with a lightweight convolutional neural network for detection of myocardial infarction
Kashvi Ankitbhai Sheth, Charvi Upreti, Manas Ranjan Prusty, Sandeep Kumar Satapathy, Shruti Mishra, Sung-Bae Cho · 2024
Read Time-frequency transformation integrated with a lightweight convolutional neural network for detection of myocardial infarction by Kashvi Ankitbhai Sheth, Charvi Upreti, Manas Ranjan Prusty, Sandeep Kumar Satapathy, Shruti Mishra, Sung-Bae Cho (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11613720.txt.
Description
Kashvi Sheth and co-authors pair wavelet cleaning with a featherweight neural network to spot heart attacks on ECG traces, in a 2024 paper. Myocardial infarction demands fast, accurate calls, and automated reading could help. Their pipeline decomposes the heartbeat signal into frequency bands, thresholds away high-frequency noise, then classifies the cleaned trace as infarction or normal.
Trained with five-fold cross-checks on the large PTB-XL dataset, the model reached 96 percent accuracy, 97 percent precision, and a 95 percent F1 score. On a second public ECG database it held 91.18 percent accuracy. Variants with resampling tackled class imbalance. The authors pitch the small model for settings where heavyweight systems cannot run.
Catalog record
| CREATOR | Kashvi Ankitbhai Sheth, Charvi Upreti, Manas Ranjan Prusty, Sandeep Kumar Satapathy, Shruti Mishra, Sung-Bae Cho |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 45 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |