End-to-end sensor fusion and classification of atrial fibrillation using deep neural networks and smartphone mechanocardiography
Mehrang, Saeed; Jafaritadi, Mojtaba; Knuutila, Timo; Jaakkola, Jussi; Jaakkola, Samuli; Kiviniemi, Tuomas; Vasankari, Tuija; Airaksinen, Juhani; Koivisto, Tero; Pänkäälä, Mikko (2022)
Mehrang, Saeed
Jafaritadi, Mojtaba
Knuutila, Timo
Jaakkola, Jussi
Jaakkola, Samuli
Kiviniemi, Tuomas
Vasankari, Tuija
Airaksinen, Juhani
Koivisto, Tero
Pänkäälä, Mikko
Institute of physics publishing
2022
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2022123174144
https://urn.fi/URN:NBN:fi-fe2022123174144
Tiivistelmä
The purpose of this research is to develop a new deep learning framework for detecting atrial fibrillation (AFib), one of the most common heart arrhythmias, by analyzing the heart’s mechanical functioning as reflected in seismocardiography (SCG) and gyrocardiography (GCG) signals. Jointly, SCG and GCG constitute the concept of mechanocardiography (MCG), a method used to measure precordial vibrations with the built-in inertial sensors of smartphones.