ECGformer: Leveraging transformer for ECG heartbeat arrhythmia
  classification
Taymaz Akan Sait Alp Mohammad Alfrad Nobel Bhuiyan
An arrhythmia, also known as a dysrhythmia, refers to an irregular heartbeat.
There are various types of arrhythmias that can originate from different areas
of the heart, resulting in either a rapid, slow, or irregular heartbeat. An
electrocardiogram (ECG) is a vital diagnostic tool used to detect heart
irregularities and abnormalities, allowing experts to analyze the heart's
electrical signals to identify intricate patterns and deviations from the norm.
Over the past few decades, numerous studies have been conducted to develop
automated methods for classifying heartbeats based on ECG data. In recent
years, deep learning has demonstrated exceptional capabilities in tackling
various medical challenges, particularly with transformers as a model
architecture for sequence processing. By leveraging the transformers, we
developed the ECGformer model for the classification of various arrhythmias
present in electrocardiogram data. We assessed the suggested approach using the
MIT-BIH and PTB datasets. ECG heartbeat arrhythmia classification results show
that the proposed method is highly effective.