TY - JOUR
T1 - Extracting deep features from short ECG signals for early atrial fibrillation detection
AU - Wu, Xiaodan
AU - Zheng, Yumeng
AU - Chu, Chao Hsien
AU - He, Zhen
N1 - Funding Information:
This work was supported in part by the Natural Science Foundation of Hebei Province, China (No. G2019202488 ) and the 100 Excellent Talents Foundation of Hebei Province of China (No. SLRC2017005 ). Our deepest gratitude goes to the four anonymous reviewers and editor for their careful review and comments and thoughtful suggestions that have helped improve this paper substantially.
Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2020/9
Y1 - 2020/9
N2 - 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 movement. Therefore, it is of great importance to study early AF detection for mobile terminals with noise immunity. Extracting effective features is critical to AF detection, but most existing studies used shallow time, frequency or time-frequency energy (TFE) features with weak representation that need to rely on long ECG signals to capture the variation in information and cannot sensitively capture the subtle variation caused by early AF. In addition, most studies only considered the discrimination of AF from normal sinus rhythm (SR) signals, ignoring the interference of noise and other signals. This study proposes three new deep features that can accurately capture the subtle variation in short ECG segments caused by early AF, examines the interference of noise and other signals generated by the mobile terminal and proposes a new feature set for early AF detection. We use six popular classifiers to evaluate the relative effectiveness of the deep features we developed against the features extracted by two conventional time-frequency methods, and the performance of the proposed feature set for detecting early AF. Our study shows that the best results for classifying AF and SR are obtained by Random Forest (RF), with 0.96 F1 score. The best results for classifying four types of signal are obtained by Extreme Gradient Boosting (XGBoost), with overall F1 score 0.88 and the individual F1 score for classifying SR, AF, Other and Noisy with 0.91, 0.90, 0.73, and 0.96, respectively.
AB - 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 movement. Therefore, it is of great importance to study early AF detection for mobile terminals with noise immunity. Extracting effective features is critical to AF detection, but most existing studies used shallow time, frequency or time-frequency energy (TFE) features with weak representation that need to rely on long ECG signals to capture the variation in information and cannot sensitively capture the subtle variation caused by early AF. In addition, most studies only considered the discrimination of AF from normal sinus rhythm (SR) signals, ignoring the interference of noise and other signals. This study proposes three new deep features that can accurately capture the subtle variation in short ECG segments caused by early AF, examines the interference of noise and other signals generated by the mobile terminal and proposes a new feature set for early AF detection. We use six popular classifiers to evaluate the relative effectiveness of the deep features we developed against the features extracted by two conventional time-frequency methods, and the performance of the proposed feature set for detecting early AF. Our study shows that the best results for classifying AF and SR are obtained by Random Forest (RF), with 0.96 F1 score. The best results for classifying four types of signal are obtained by Extreme Gradient Boosting (XGBoost), with overall F1 score 0.88 and the individual F1 score for classifying SR, AF, Other and Noisy with 0.91, 0.90, 0.73, and 0.96, respectively.
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U2 - 10.1016/j.artmed.2020.101896
DO - 10.1016/j.artmed.2020.101896
M3 - Article
C2 - 34756213
AN - SCOPUS:85091257079
SN - 0933-3657
VL - 109
JO - Artificial Intelligence in Medicine
JF - Artificial Intelligence in Medicine
M1 - 101896
ER -