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一种用于高噪声环境下听诊的自适应降噪听诊器。

An adaptive noise reduction stethoscope for auscultation in high noise environments.

作者信息

Patel S B, Callahan T F, Callahan M G, Jones J T, Graber G P, Foster K S, Glifort K, Wodicka G R

机构信息

School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana 47907-1285, USA.

出版信息

J Acoust Soc Am. 1998 May;103(5 Pt 1):2483-91. doi: 10.1121/1.422769.

Abstract

Auscultation of lung sounds in patient transport vehicles such as an ambulance or aircraft is unachievable because of high ambient noise levels. Aircraft noise levels of 90-100 dB SPL are common, while lung sounds have been measured in the 22-30 dB SPL range in free space and 65-70 dB SPL within a stethoscope coupler. Also, the bandwidth of lung sounds and vehicle noise typically has significant overlap, limiting the utility of traditional band-pass filtering. In this study, a passively shielded stethoscope coupler that contains one microphone to measure the (noise-corrupted) lung sound and another to measure the ambient noise was constructed. Lung sound measurements were made on a healthy subject in a simulated USAF C-130 aircraft environment within an acoustic chamber at noise levels ranging from 80 to 100 dB SPL. Adaptive filtering schemes using a least-mean-squares (LMS) and a normalized least-mean-squares (NLMS) approach were employed to extract the lung sounds from the noise-corrupted signal. Approximately 15 dB of noise reduction over the 100-600 Hz frequency range was achieved with the LMS algorithm, with the more complex NLMS algorithm providing faster convergence and up to 5 dB of additional noise reduction. These findings indicate that a combination of active and passive noise reduction can be used to measure lung sounds in high noise environments.

摘要

在诸如救护车或飞机等患者运输工具中,由于环境噪音水平过高,无法进行肺部声音听诊。飞机的噪音水平通常在90 - 100 dB SPL,而肺部声音在自由空间中测量为22 - 30 dB SPL,在听诊器耦合器内为65 - 70 dB SPL。此外,肺部声音的带宽和车辆噪音通常有很大重叠,限制了传统带通滤波的效用。在本研究中,构建了一种被动屏蔽的听诊器耦合器,它包含一个用于测量(被噪音干扰的)肺部声音的麦克风和另一个用于测量环境噪音的麦克风。在声学室内模拟的美国空军C - 130飞机环境中,对一名健康受试者进行了肺部声音测量,噪音水平范围为80至100 dB SPL。采用了使用最小均方(LMS)和归一化最小均方(NLMS)方法的自适应滤波方案,从被噪音干扰的信号中提取肺部声音。使用LMS算法在100 - 600 Hz频率范围内实现了约15 dB的降噪,更复杂的NLMS算法收敛更快,额外降噪高达5 dB。这些发现表明,主动降噪和被动降噪相结合可用于在高噪音环境中测量肺部声音。

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