Artificial intelligence to detect noise events in remote monitoring data

Autor: Nobuhiro Nishii, Kensuke Baba, Ken'ichi Morooka, Haruto Shirae, Tomofumi Mizuno, Takuro Masuda, Akira Ueoka, Saori Asada, Masakazu Miyamoto, Kentaro Ejiri, Satoshi Kawada, Koji Nakagawa, Kazufumi Nakamura, Hiroshi Morita, Shinsuke Yuasa
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Journal of Arrhythmia, Vol 40, Iss 3, Pp 560-577 (2024)
Druh dokumentu: article
ISSN: 1883-2148
1880-4276
DOI: 10.1002/joa3.13037
Popis: Abstract Background Remote monitoring (RM) of cardiac implantable electrical devices (CIEDs) can detect various events early. However, the diagnostic ability of CIEDs has not been sufficient, especially for lead failure. The first notification of lead failure was almost noise events, which were detected as arrhythmia by the CIED. A human must analyze the intracardiac electrogram to accurately detect lead failure. However, the number of arrhythmic events is too large for human analysis. Artificial intelligence (AI) seems to be helpful in the early and accurate detection of lead failure before human analysis. Objective To test whether a neural network can be trained to precisely identify noise events in the intracardiac electrogram of RM data. Methods We analyzed 21 918 RM data consisting of 12 925 and 1884 Medtronic and Boston Scientific data, respectively. Among these, 153 and 52 Medtronic and Boston Scientific data, respectively, were diagnosed as noise events by human analysis. In Medtronic, 306 events, including 153 noise events and randomly selected 153 out of 12 692 nonnoise events, were analyzed in a five‐fold cross‐validation with a convolutional neural network. The Boston Scientific data were analyzed similarly. Results The precision rate, recall rate, F1 score, accuracy rate, and the area under the curve were 85.8 ± 4.0%, 91.6 ± 6.7%, 88.4 ± 2.0%, 88.0 ± 2.0%, and 0.958 ± 0.021 in Medtronic and 88.4 ± 12.8%, 81.0 ± 9.3%, 84.1 ± 8.3%, 84.2 ± 8.3% and 0.928 ± 0.041 in Boston Scientific. Five‐fold cross‐validation with a weighted loss function could increase the recall rate. Conclusions AI can accurately detect noise events. AI analysis may be helpful for detecting lead failure events early and accurately.
Databáze: Directory of Open Access Journals
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