Identifying the Respiratory Sound Based on Single-Channel Separation and Hyperdimensional Computing

IF 4.3 2区综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Sensors Journal Pub Date : 2025-07-03 DOI:10.1109/JSEN.2025.3557909

Jie Zheng;Yixuan Wang;Jinglong Niu;Yan Shi;Fei Xie

引用次数: 0

Abstract

In intensive care units (ICUs), efficient respiratory management, particularly sputum suction in weakened patients, is critical. Traditional stethoscope-based methods for respiratory sound analysis in tracheal sputum assessment are time-consuming and often struggle to differentiate between cardiac and respiratory sounds, affecting sputum detection accuracy. To address these issues, we propose identifying respiratory sound based on single-channel separation and hyperdimensional computing (IRS-SSHC). Specifically, the proposed method first employs an encoder-decoder framework to effectively separate heart and respiratory sounds in the time domain. Then, it segments respiratory sounds using short-duration energy, where each segment is represented by a 1024-D vector space. Next, it utilizes light gradient boosting machine (LightGBM) based on the vector space for classification. Experimental results show that the classification ACC of IRS-SSHC is 97.9%, which outperforms existing methods.

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基于单通道分离和超维计算的呼吸声识别

在重症监护病房（icu），有效的呼吸管理，特别是虚弱患者的吸痰是至关重要的。传统的基于听诊器的气管痰液呼吸音分析方法耗时长，而且往往难以区分心音和呼吸音，影响了痰液检测的准确性。为了解决这些问题，我们提出了基于单通道分离和超维计算（IRS-SSHC）的呼吸声识别方法。具体而言，该方法首先采用编码器-解码器框架在时域内有效分离心音和呼吸音。然后，它使用短时间能量分割呼吸声音，其中每个片段由1024-D向量空间表示。其次，利用基于向量空间的光梯度增强机（LightGBM）进行分类。实验结果表明，IRS-SSHC的分类ACC为97.9%，优于现有方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Sensors Journal 工程技术-工程：电子与电气

CiteScore

7.70

自引率

14.00%

发文量

2058

审稿时长

5.2 months

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