Cascaded redundant convolutional encoder-decoder network improved apnea detection performance using tracheal sounds in post anesthesia care unit patients

Methods of detecting apnea based on acoustic features can be prone to misdiagnosed and missed diagnoses due to the influence of noise. The aim of this paper is to improve the performance of apnea detection algorithms in the Post Anesthesia Care Unit (PACU) using a denoising method that processes tra...

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Bibliographic Details
Published in:Biomedical physics & engineering express Vol. 10; no. 6
Main Authors: Zhang, Erpeng, Jia, Xiuzhu, Wu, Yanan, Liu, Jing, Yu, Lu
Format: Journal Article
Language:English
Published: England 01.11.2024
Subjects:
Apnea Detection
Anesthesia
CR-CED
Tracheal Sounds
Denoising
ISSN:2057-1976, 2057-1976
Online Access:Get more information
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Summary:Methods of detecting apnea based on acoustic features can be prone to misdiagnosed and missed diagnoses due to the influence of noise. The aim of this paper is to improve the performance of apnea detection algorithms in the Post Anesthesia Care Unit (PACU) using a denoising method that processes tracheal sounds without the need for separate background noise. Approach: Tracheal sound data from laboratory subjects was collected using a microphone. Record a segment of clinical background noise and clean tracheal sound data to synthesize the noisy tracheal sound data according to a specified signal-to-noise ratio. Extract the frequency-domain features of the tracheal sounds using the Short Time Fourier Transform (STFT) and input the Cascaded Redundant Convolutional Encoder-Decoder network (CR-CED) network for training. Patients' tracheal sound data collected in the PACU were then fed into the CR-CED network as test data and inversely transformed by STFT to obtain denoised tracheal sounds. The apnea detection algorithm was used to detect the tracheal sound after denoising. Results: Apnea events were correctly detected 207 times and normal respiratory events 11,305 times using tracheal sounds denoised by the CR-CED network. The sensitivity and specificity of apnea detection were 88% and 98.6%, respectively. Significance: The apnea detection results of tracheal sounds after CR-CED network denoising in the PACU are accurate and reliable. Tracheal sound can be denoised using this approach without separate background noise. It effectively improves the applicability of the tracheal sound denoising method in the medical environment while ensuring its correctness.&#xD.
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ISSN:2057-1976
2057-1976
DOI:10.1088/2057-1976/ad89c6