Assessment of Heart Rhythm and Arrhythmia Detection from Electromyographic Signals Using a Convolutional Neural Network
M.A. Sazhina, S.A. Lobov, A.S. Pimashkin, I.V. Loskot, V.B. Kazantsev · 2025
Read Assessment of Heart Rhythm and Arrhythmia Detection from Electromyographic Signals Using a Convolutional Neural Network by M.A. Sazhina, S.A. Lobov, A.S. Pimashkin, I.V. Loskot, V.B. Kazantsev (2025) 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-12896126.txt.
Description
Sazhina and co-authors develop a neural method to read heart rhythm and detect arrhythmia from muscle signals, in a 2025 study. The signals come from a wearable electromyography system. Reading the heart from muscle recordings could widen non-invasive monitoring of fatigue, stress, and rhythm problems.
Six healthy young men were recorded at rest and during static and dynamic arm contractions. Muscle signals were taken alongside a standard heart monitor and a chest device. A convolutional autoencoder was trained on masks marking heart wave peaks to extract beat components from the muscle signal. Performance was evaluated against the reference recordings.
Catalog record
| CREATOR | M.A. Sazhina, S.A. Lobov, A.S. Pimashkin, I.V. Loskot, V.B. Kazantsev |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 30 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |