Robust wave-feature adaptive heartbeat classification based on self-attention mechanism using a transformer model
Objective . Electrocardiography is a common method for screening cardiovascular diseases. Accurate
heartbeat classification assists in diagnosis and has attracted great attention. In this paper, we
proposed an automatic heartbeat classification method based on a transformer neural network using a
self-attention mechanism. Approach. An adaptive heartbeat segmentation method was designed to
selectively focus on the time-dependent representation of heartbeats. A one-dimensional convolution
layer was used to embed wave characteristics into symbolic representations, and then, a transformer
block using multi-head attention was applied to deal with the dependence of wave-embedding. The
model was trained and evaluated using the MIT-BIH arrhythmia database (MIT-DB). To improve the model
performance, the model pre-trained on MIT-BIH supraventricular arrhythmia database (MIT-SVDB) was
used and fine-tuned on MIT-DB. Main results. The proposed method was verified using t...
Source: Physiological Measurement - Category: Physiology Authors: Shuaicong Hu, Wenjie Cai, Tijie Gao, Jiajun Zhou and Mingjie Wang Source Type: research
More News: Arrhythmia | Cardiology | Cardiovascular | Databases & Libraries | Electrocardiogram | Heart | Physiology