Artificial intelligence classification methods of atrial fibrillation with implementation technology

• 出版年: Computer Assisted Surgery
• 第一著者: 2-s2.0-85015419619
• フォーマット: Review
• 言語: English
• 出版事項: Taylor and Francis Ltd. 2016
• オンライン･アクセス: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015419619&doi=10.1080%2f24699322.2016.1240303&partnerID=40&md5=b3dea6b647a8143da59bd24d05c4f25b

Background: Atrial fibrillation (AFIB) is one of the most common types of arrhythmia, which leads to heart failure and stroke to public. As AFIB has the high potential to cause permanent disability in patients, its early detection is extremely important. There are different types of AFIB classification algorithm that have been proposed by researchers in recent years. Methods: This paper reviews the features of AFIB in terms of ECG morphological features and heart rate variability (HRV) analysis on different methods. The existing classification method, particularly focusing on Artificial Intelligence technique, is also comprehensively described. Other than that, the existing implementation technology of arrhythmia detection platforms such as smart phone and System-on-Chip-based embedded device are also elaborated in terms of their design trade-offs. Conclusion: Current existing AFIB detection algorithm cannot compromise for high accuracy and low complexity. Due to the limitation of embedded system, design trade off should be considered to strike the balance between the performance of algorithm and the limitation. © 2016 The Author(s).