Abstract
Patient-ventilator asynchronies (PVA) during mechanical ventilation can lead to a prolonged duration of ventilation and increased mortality. Identifying asynchronies still mainly relies on clinical experience due to the lack of effective, accurate automation tools. Machine learning approach promises to offer an automated solution for PVA detection. However, accurately labelling normal or abnormal patterns from massive ventilator data is extremely challenging and manual labor intensive. This leads to the lack of well-annotated training sets for automatic PVA identification. In this work, we designed an annotation software based on the Python programming language. The basic functions of this software consist of 4 functions: (1) loading and reconstructing ventilator data files; (2) extracting breathing cycles from ventilation waveforms; (3) anomaly detection of mechanical ventilation waveforms; (4) generating annotation files. 400 h of ventilator waveform data from 10 patients collected from Zhongda Hospital, Southeast University, Nanjing, China was applied for software validation. The accuracy of automatic extraction of breathing cycle is 99.98%. For each breathing cycle, the abnormal waveform detection was completed with a high accuracy of 95.00%.
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Acknowledgment
This research was financially supported by the Jiangsu Provincial Special Program of Medical Science, China (BE2020786), and the National Natural Science Foundation of China (81971885).
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Chen, C., Wang, Z., Chen, C., Wang, X., Liu, S. (2024). A Software Tool for Anomaly Detection and Labeling of Ventilator Waveforms. In: Wang, G., Yao, D., Gu, Z., Peng, Y., Tong, S., Liu, C. (eds) 12th Asian-Pacific Conference on Medical and Biological Engineering. APCMBE 2023. IFMBE Proceedings, vol 104. Springer, Cham. https://doi.org/10.1007/978-3-031-51485-2_29
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DOI: https://doi.org/10.1007/978-3-031-51485-2_29
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