Department of Biomedical Engineering, School of Fundamental Sciences, China Medical University, Shenyang, Liaoning, China.
Department of Nuclear Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China.
Biomed Res Int. 2020 May 21;2020:7429345. doi: 10.1155/2020/7429345. eCollection 2020.
Tracheal sounds were used to detect apnea on various occasions. However, ambient noises can contaminate tracheal sounds which result in poor performance of apnea detection. The objective of this paper was to apply the adaptive filtering (AF) algorithm to improve the quality of tracheal sounds and examine the accuracy of the apnea detection algorithm using tracheal sounds after AF.
Tracheal sounds were acquired using a primary microphone encased in a plastic bell, and the ambient noises were collected using a reference microphone resting outside the plastic bell in quiet and noisy environments, respectively. Simultaneously, the flow pressure signals and thoracic and abdominal movement were obtained as the standard signals to determine apnea events. Then, the normalized least mean square (NLMS) AF algorithm was applied to the tracheal sounds mixed with noises. Finally, the algorithm of apnea detection was used to the tracheal sounds with AF and the tracheal sounds without AF. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and Cohen's kappa coefficient of apnea detection were calculated.
Forty-six healthy subjects, aged 18-35 years and with BMI < 21.4, were included in the study. The apnea detection performance using tracheal sounds was as follows: in the quiet environment, the tracheal sounds without AF detected apnea with 97.2% sensitivity, 99.9% specificity, 99.8% PPV, 99.4% NPV, 99.5% accuracy, and 0.982 kappa coefficient. The tracheal sounds with AF detected apnea with 98.2% sensitivity, 99.9% specificity, 99.4% PPV, 99.6% NPV, 99.6% accuracy, and 0.985 kappa coefficient. While in the noisy environment, the tracheal sounds without AF detected apnea with 81.1% sensitivity, 96.9% specificity, 85.1% PPV, 96% NPV, 94.2% accuracy, and 0.795 kappa coefficient and the tracheal sounds with AF detected apnea with 91.5% sensitivity, 97.4% specificity, 88.4% PPV, 98.2% NPV, 96.4% accuracy, and 0.877 kappa coefficient.
The performance of apnea detection using tracheal sounds with the NLMS AF algorithm in the noisy environment proved to be accurate and reliable. The AF technology could be applied to the respiratory monitoring using tracheal sounds.
气管音曾被用于检测各种情况下的呼吸暂停。然而，环境噪声会污染气管音，从而导致呼吸暂停检测的性能下降。本文旨在应用自适应滤波（AF）算法来改善气管音的质量，并利用 AF 后的气管音检查呼吸暂停检测算法的准确性。
使用包裹在塑料钟形罩内的主麦克风采集气管音，分别在安静和嘈杂环境下，使用放置在塑料钟形罩外的参考麦克风采集环境噪声。同时，还采集流量压力信号和胸腹部运动作为确定呼吸暂停事件的标准信号。然后，将归一化最小均方（NLMS）AF 算法应用于混合噪声的气管音。最后，将呼吸暂停检测算法应用于经 AF 和未经 AF 处理的气管音。计算呼吸暂停检测的灵敏度、特异性、阳性预测值（PPV）、阴性预测值（NPV）、准确性和 Cohen's kappa 系数。
本研究纳入了 46 名年龄在 18-35 岁、BMI<21.4 的健康受试者。在安静环境下，未经 AF 处理的气管音检测呼吸暂停的灵敏度为 97.2%，特异性为 99.9%，PPV 为 99.8%，NPV 为 99.4%，准确性为 99.5%，kappa 系数为 0.982。经 AF 处理的气管音检测呼吸暂停的灵敏度为 98.2%，特异性为 99.9%，PPV 为 99.4%，NPV 为 99.6%，准确性为 99.6%，kappa 系数为 0.985。而在嘈杂环境下，未经 AF 处理的气管音检测呼吸暂停的灵敏度为 81.1%，特异性为 96.9%，PPV 为 85.1%，NPV 为 96%，准确性为 94.2%，kappa 系数为 0.795。经 AF 处理的气管音检测呼吸暂停的灵敏度为 91.5%，特异性为 97.4%，PPV 为 88.4%，NPV 为 98.2%，准确性为 96.4%，kappa 系数为 0.877。
NLMS AF 算法在嘈杂环境下使用气管音进行呼吸暂停检测的性能准确可靠。AF 技术可应用于气管音呼吸监测。
