Research on Classification of COPD Respiratory Sounds Based on RBM Feature Extraction and Improved Random Forest

FU Dan; CHEN Guang-xin; GUO Jin-xing; CAI Ying; LI Ji

doi:10.12263/newIT.2025.02.004

您当前的位置：

首页 >

文章列表页 >

Research on Classification of COPD Respiratory Sounds Based on RBM Feature Extraction and Improved Random Forest

RESEARCH PAPER | 更新时间：2025-12-01

- Research on Classification of COPD Respiratory Sounds Based on RBM Feature Extraction and Improved Random Forest
- New Generation of Information Technology Vol. 8, Issue 2, Pages: 19-23(2025)
- 作者机构：
  
  1.牡丹江医科大学，黑龙江牡丹江 157011
  2.牡丹江医科大学附属红旗医院，黑龙江牡丹江 157011
  3.牡丹江医科大学附属第二医院，黑龙江牡丹江 157011
- 作者简介：
- 基金信息：
- DOI：10.12263/newIT.2025.02.004
  CLC： TP181
- Published：15 February 2025
- 稿件说明：
移动端阅览
富丹, 陈广新, 郭金兴, 等. 基于RBM特征提取与改进随机森林的慢阻肺呼吸音分类研究[J]. 新一代信息技术, 2025, 8(2): 19-23

FU Dan, CHEN Guang-xin,GUO Jin-xing, et al. Research on Classification of COPD Respiratory Sounds Based on RBM Feature Extraction and Improved Random Forest[J]. New Generation of Information Technology, 2025, 8(2): 19-23
富丹, 陈广新, 郭金兴, 等. 基于RBM特征提取与改进随机森林的慢阻肺呼吸音分类研究[J]. 新一代信息技术, 2025, 8(2): 19-23 DOI： 10.12263/newIT.2025.02.004.

FU Dan, CHEN Guang-xin,GUO Jin-xing, et al. Research on Classification of COPD Respiratory Sounds Based on RBM Feature Extraction and Improved Random Forest[J]. New Generation of Information Technology, 2025, 8(2): 19-23 DOI： 10.12263/newIT.2025.02.004.

摘要

慢性阻塞性肺病的早期诊断对于改善患者预后具有重要意义。本研究提出了一种基于快速傅里叶变换和受限玻尔兹曼机特征提取，结合粒子群优化算法优化的随机森林分类器的新型慢性阻塞性肺病呼吸音分类模型。实验结果表明，该模型在准确率、精确率、召回率和

分数等多个评估指标上均表现出色，显示出其在慢性阻塞性肺病早期诊断中的潜在应用价值。

Abstract

The early diagnosis of chronic obstructive pulmonary disease is of great significance to improve the prognosis of patients. In this study

a COPD respiratory sound classification model based on fast Fourier transform and Constrained Boltzmann machine feature extraction combined with particle swarm optimization algorithm optimized random forest classifier was proposed. The experimental results show that the model performs well on several evaluation indexes such as accuracy rate

accuracy rate

recall rate and

score

indicating its potential application value in the early diagnosis of COPD.

关键词

Keywords

references

SORIANO J B , POLVERINO F , COSIO B G . What is early COPD and why is it important? [J ] . European Respiratory Journal , 2018 , 52 ( 6 ): 1801448 .

AARON S D , VANDEMHEEN K L , ALEX WHITMORE G , et al . Early diagnosis and treatment of COPD and asthma - A randomized, controlled trial [J ] . New England Journal of Medicine , 2024 , 390 ( 22 ): 2061 - 2073 .

房有丽 , 王红 , 狄瑞彤 , 等 . COPD多维特征提取与集成诊断方法 [J ] . 计算机应用研究 , 2019 , 36 ( 10 ): 2925 - 2929 .

常峥 , 罗萍 , 杨波 , 等 . 基于HHT-MFCC和短时能量的慢性阻塞性肺病患者呼吸声识别 [J ] . 计算机应用 , 2021 , 41 ( 2 ): 598 - 603 .

胡明昕 . 基于LSTM的慢性阻塞性肺疾病氧减状态预测研究 [D ] . 南京 : 南京理工大学 , 2020 .

PIRMORADI S , HOSSEINIYAN KHATIBI S M , ZUNUNI VAHED S , et al . Unraveling the link between PTBP1 and severe asthma through machine learning and association rule mining method [J ] . Scientific Reports , 2023 , 13 ( 1 ): 15399 .

HAN T T , LE TRUNG K , NGUYEN ANH P , et al . High performance method for COPD features extraction using complex network [J ] . Biomedical Physics and Engineering Express , 2024 , 10 ( 6 ): 065045 .

余辉 , 赵婧 , 仇兆禹 , 等 . 基于深度学习的慢性阻塞性肺病的诊断模型研究 [J ] . 中国生物医学工程学报 , 2022 , 41 ( 5 ): 558 - 566 .

黄斌 . 基于FFT算法的铁路机车信号故障检测研究 [J ] . 电子技术与软件工程 , 2022 ( 12 ): 144 - 147 .

毛旭东 . 基于限制性玻尔兹曼机的叶片识别算法研究 [J ] . 电子技术与软件工程 , 2017 ( 8 ): 107 - 109 .

BAI J W , LI Y M , LI J W , et al . Multinomial random forest [J ] . Pattern Recognition , 2022 , 122 : 108331 .

JAIN M , SAIHJPAL V , SINGH N , et al . An overview of variants and advancements of PSO algorithm [J ] . Applied Sciences , 2022 , 12 ( 17 ): 8392 .

MENG Z Y , ZHONG Y X , MAO G J , et al . PSO-sono: A novel PSO variant for single-objective numerical optimization [J ] . Information Sciences , 2022 , 586 : 176 - 191 .

PRADHAN A , BISOY S K , DAS A . A survey on PSO based meta-heuristic scheduling mechanism in cloud computing environment [J ] . Journal of King Saud University - Computer and Information Sciences , 2022 , 34 ( 8 ): 4888 - 4901 .

HU J C , SZYMCZAK S . A review on longitudinal data analysis with random forest [J ] . Briefings in Bioinformatics , 2023 , 24 ( 2 ): bbad002 .

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Research on Stator Inter-Turn Short-Circuit Fault Diagnosis of Three-Phase Asynchronous Motors Driven by Model-Data Fusion

Research on Bone Marrow Transplantation Survival Prediction Based on Particle Swarm Optimized BP Neural Network

Related Author

XUE Wei

NIU Zuo-gang

SHEN Xiang

SUN Yue

GUO Jin-xing

WANG Zhen

CHEN Guang-xin

HAN Xue-shan

Related Institution

Oil and Gas Production Engineering Service Center, East China Oil and Gas Branch

First Clinical Medical College of Mudanjiang Medical University

Mudanjiang Medical University Affiliated Hongqi Hospital

Medical Imaging College of Mudanjiang Medical University

⁰