Extracting deep features from short ECG signals for early atrial fibrillation detection

• Main Authors: WU, Xiaodan, ZHENG, Yumeng, CHU, Chao-Hsien, HE, Zhen
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Medical knowledge engineering; Deep features extraction; Early atrial fibrillation detection; Data mining; Artificial Intelligence and Robotics; Cybersecurity
• Online Access: https://ink.library.smu.edu.sg/sis_research/10127; https://ink.library.smu.edu.sg/context/sis_research/article/11127/viewcontent/Extracting_deep_features_from_short_ECG_signals_for_early_atrial_fibrillation_detection.pdf
• Institution: Singapore Management University

Atrial Fibrillation (AF) at an early stage has a short duration and is sometimes asymptomatic, making it difficult to detect. Although the use of mobile sensing devices has provided the possibility of real-time cardiac detection, it is highly susceptible to the noise signals generated by body moveme...