A Review on Atrial Fibrillation Detection from Ambulatory ECG

Caiyun Ma, Zhijun Xiao, Lina Zhao, Shany Biton, Joachim A. Behar, Xi Long, Rik Vullings, Ronald M. Aarts, Jianqing Li, Chengyu Liu

Research output: Contribution to journalArticlepeer-review


Atrial fibrillation (AF) is a prevalent clinical arrhythmia disease and is an important cause of stroke, heart failure, and sudden death. Due to the insidious onset and no obvious clinical symptoms of AF, the status of AF diagnosis and treatment is not optimal. Early AF screening or detection is essential. Internet of Things (IoT) and artificial intelligence (AI) technologies have driven the development of wearable electrocardiograph (ECG) devices used for health monitoring, which are an effective means of AF detection. The main challenges of AF analysis using ambulatory ECG include ECG signal quality assessment to select available ECG, the robust and accurate detection of QRS complex waves to monitor heart rate, and AF identification under the interference of abnormal ECG rhythm. Through ambulatory ECG measurement and intelligent detection technology, the probability of postoperative recurrence of AF can be reduced, and personalized treatment and management of patients with AF can be realized. This work describes the status of AF monitoring technology in terms of devices, algorithms, clinical applications, and future directions.

Original languageEnglish
Pages (from-to)1-17
Number of pages17
JournalIEEE Transactions on Biomedical Engineering
StateAccepted/In press - 2023


  • Ambulatory ECG
  • Artificial intelligence
  • Atrial fibrillation
  • Atrial fibrillation (AF)
  • Biomedical monitoring
  • Electrocardiogram (ECG)
  • Electrocardiography
  • Monitoring
  • Rhythm
  • Wearable computers

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering


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