基于音频分析的慢性呼吸系统疾病自动检测边缘计算系统。

IF 3.5 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
José Antonio Rivas-Navarrete, Humberto Pérez-Espinosa, A L Padilla-Ortiz, Ansel Y Rodríguez-González, Diana Cristina García-Cambero
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引用次数: 0

摘要

慢性呼吸道疾病影响全世界的人们,但在远离人口中心的偏远地区可能无法获得传统的诊断方法。利用人工智能(AI),人类呼吸系统的声音显示出自主检测这些疾病的潜力。本文概述了一种基于音频的边缘计算系统的开发,该系统可以自动检测慢性呼吸道疾病(CRDs)。该系统利用机器学习(ML)技术来分析呼吸声音(咳嗽和呼吸)的录音,并对这些疾病的存在与否进行分类,使用Mel频率倒谱系数(MFCC)和色度属性(色谱图)等特征来捕捉呼吸声音的相关声学特征。该系统使用从86个人收集的呼吸声音数据集进行训练和测试。其中53人患有慢性呼吸系统疾病,包括哮喘和慢性阻塞性肺病(COPD),而其余33人是健康的。该系统的最终评估是在13名患者和22名健康个体中进行的。我们的方法在边缘设备(包括智能手机和树莓派)上的声音分类中表现出高灵敏度和特异性。我们对CRDs的最佳结果达到了90.0%的敏感性,93.55%的特异性,以及91.75%的平衡准确性,可以准确地识别健康和患病个体。这些结果显示了边缘计算和机器学习系统在呼吸系统疾病检测中的潜力。我们相信该系统有助于开发高效且具有成本效益的筛查工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Edge Computing System for Automatic Detection of Chronic Respiratory Diseases Using Audio Analysis.

Chronic respiratory diseases affect people worldwide, but conventional diagnostic methods may not be accessible in remote locations far from population centers. Sounds from the human respiratory system have displayed potential in autonomously detecting these diseases using artificial intelligence (AI). This article outlines the development of an audio-based edge computing system that automatically detects chronic respiratory diseases (CRDs). The system utilizes machine learning (ML) techniques to analyze audio recordings of respiratory sounds (cough and breath) and classify the presence or absence of these diseases, using features such as Mel frequency cepstral coefficients (MFCC) and chromatic attributes (chromagram) to capture the relevant acoustic features of breath sounds. The system was trained and tested using a dataset of respiratory sounds collected from 86 individuals. Among them, 53 had chronic respiratory conditions, including asthma and chronic obstructive pulmonary disease (COPD), while the remaining 33 were healthy. The system's final evaluation was conducted with a group of 13 patients and 22 healthy individuals. Our approach demonstrated high sensitivity and specificity in the classification of sounds on edge devices, including smartphone and Raspberry Pi. Our best results for CRDs reached a sensitivity of 90.0%, a specificity of 93.55%, and a balanced accuracy of 91.75% for accurately identifying individuals with both healthy and diseased. These results showcase the potential of edge computing and machine learning systems in respiratory disease detection. We believe this system can contribute to developing efficient and cost-effective screening tools.

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来源期刊
Journal of Medical Systems
Journal of Medical Systems 医学-卫生保健
CiteScore
11.60
自引率
1.90%
发文量
83
审稿时长
4.8 months
期刊介绍: Journal of Medical Systems provides a forum for the presentation and discussion of the increasingly extensive applications of new systems techniques and methods in hospital clinic and physician''s office administration; pathology radiology and pharmaceutical delivery systems; medical records storage and retrieval; and ancillary patient-support systems. The journal publishes informative articles essays and studies across the entire scale of medical systems from large hospital programs to novel small-scale medical services. Education is an integral part of this amalgamation of sciences and selected articles are published in this area. Since existing medical systems are constantly being modified to fit particular circumstances and to solve specific problems the journal includes a special section devoted to status reports on current installations.
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