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The application of deep learning in electrocardiogram: Where we came from and where we should go?
Sun, Jin-Yu; Shen, Hui; Qu, Qiang; Sun, Wei; Kong, Xiang-Qing.
Afiliação
  • Sun JY; Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210000, China.
  • Shen H; Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210000, China.
  • Qu Q; Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210000, China.
  • Sun W; Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210000, China.. Electronic address: weisun7919@njmu.edu.cn.
  • Kong XQ; Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210000, China.. Electronic address: kongxq@njmu.edu.cn.
Int J Cardiol ; 337: 71-78, 2021 08 15.
Article em En | MEDLINE | ID: mdl-34000355
ABSTRACT
Electrocardiogram (ECG) is a commonly-used, non-invasive examination recording cardiac voltage versus time traces over a period. Deep learning technology, a robust artificial intelligence algorithm, can imitate the data processing patterns of the human brain, and it has experienced remarkable success in disease screening, diagnosis, and prediction. Compared with traditional machine learning, deep learning algorithms possess more powerful learning capabilities and can automatically extract features without extensive data pre-processing or hand-crafted feature extraction, which makes it a suitable tool to analyze complex structures of high-dimensional data. With the advances in computing power and digitized data availability, deep learning provides us an opportunity to improve ECG data interpretation with higher efficacy and accuracy and, more importantly, expand the original functions of ECG. The application of deep learning has led us to stand at the edge of ECG innovation and will potentially change the current clinical monitoring and management strategies. In this review, we introduce deep learning technology and summarize its advantages compared with traditional machine learning algorithms. Moreover, we provide an overview on the current application of deep learning in ECGs, with a focus on arrhythmia (especially atrial fibrillation during normal sinus rhythm), cardiac dysfunction, electrolyte imbalance, and sleep apnea. Last but not least, we discuss the current challenges and prospect directions for the following studies.
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Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Inteligência Artificial / Aprendizado Profundo Limite: Humans Idioma: En Revista: Int J Cardiol Ano de publicação: 2021 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Inteligência Artificial / Aprendizado Profundo Limite: Humans Idioma: En Revista: Int J Cardiol Ano de publicação: 2021 Tipo de documento: Article País de afiliação: China