TY - JOUR
T1 - A Multistep Paroxysmal Atrial Fibrillation Scanning Strategy in Long-Term ECGs
AU - Ma, Caiyun
AU - Liu, Chengyu
AU - Wang, Xingyao
AU - Li, Yuwen
AU - Wei, Shoushui
AU - Lin, Bor Shyh
AU - Li, Jianqing
N1 - Publisher Copyright:
© 1963-2012 IEEE.
PY - 2022
Y1 - 2022
N2 - Atrial fibrillation (AF) is a progressive disease often initially manifested by intermittent episodes spontaneously terminating and is an insidious disease. The previous work trained the support vector machine (SVM) classifier on multiple RR interval features. The trained AF detector was tested on the fourth China Physiological Signal Challenge (CPSC 2021) database, achieving 97.59% and 89.83% for sensitivity and specificity on dataset 1, respectively. The test results were 96.46% and 78.64% on dataset 2, respectively. The results show that the AF detector based on rhythm is insufficient for the recognition of non-AF. Therefore, this work developed AF scanning algorithm integrating rhythm and P-wave information for long-term ECGs. The proposed algorithm is divided into three steps. First, utilize a priori knowledge to locate suspected AF, and then, employ a trained rhythm-based AF detector to detect AF. Finally, adopt the dynamic time warping (DTW) and an autoencoding (AE) network to quantize the P-wave information to identify the non-AF signal from AF. The results on dataset 1 were 97.44% and 98.50% and on dataset 2 were 96.13% and 87.42%. This work scanned 11 patients with 24-h paroxysmal AF (PAF). The best result is that the detection accuracy is 99.33%, and the false detection is 0.01%, and the worst outcome is that the detection accuracy is 88.75%, and the false detection is 14.06%. The results proved that the proposed method could provide reliable scanning for PAF events.
AB - Atrial fibrillation (AF) is a progressive disease often initially manifested by intermittent episodes spontaneously terminating and is an insidious disease. The previous work trained the support vector machine (SVM) classifier on multiple RR interval features. The trained AF detector was tested on the fourth China Physiological Signal Challenge (CPSC 2021) database, achieving 97.59% and 89.83% for sensitivity and specificity on dataset 1, respectively. The test results were 96.46% and 78.64% on dataset 2, respectively. The results show that the AF detector based on rhythm is insufficient for the recognition of non-AF. Therefore, this work developed AF scanning algorithm integrating rhythm and P-wave information for long-term ECGs. The proposed algorithm is divided into three steps. First, utilize a priori knowledge to locate suspected AF, and then, employ a trained rhythm-based AF detector to detect AF. Finally, adopt the dynamic time warping (DTW) and an autoencoding (AE) network to quantize the P-wave information to identify the non-AF signal from AF. The results on dataset 1 were 97.44% and 98.50% and on dataset 2 were 96.13% and 87.42%. This work scanned 11 patients with 24-h paroxysmal AF (PAF). The best result is that the detection accuracy is 99.33%, and the false detection is 0.01%, and the worst outcome is that the detection accuracy is 88.75%, and the false detection is 14.06%. The results proved that the proposed method could provide reliable scanning for PAF events.
KW - Atrial fibrillation (AF)
KW - autoencoding (AE)
KW - dynamic time warping (DTW)
KW - electrocardiogram (ECG)
KW - wearable ECG
UR - http://www.scopus.com/inward/record.url?scp=85127515871&partnerID=8YFLogxK
U2 - 10.1109/TIM.2022.3164138
DO - 10.1109/TIM.2022.3164138
M3 - Article
AN - SCOPUS:85127515871
SN - 0018-9456
VL - 71
JO - IEEE Transactions on Instrumentation and Measurement
JF - IEEE Transactions on Instrumentation and Measurement
M1 - 4004010
ER -