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 (A...
| Publicado en: | Journal of Medical Systems Vol. 49; no. 1; pp. 1 - 20 |
|---|---|
| Autores principales: | , , , , |
| Formato: | pictorial research tables/charts tracings Journal Article |
| Publicado: |
Springer Nature
3/4/2025
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=183434767&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 183434767 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 3/4/2025 vid: 49 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 183434767 183434767 183434767 10.1007/s10916-025-02154-7 183434767 ppf: 1 ppct: 19 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Edge Computing System for Automatic Detection of Chronic Respiratory Diseases Using Audio Analysis. aug: au: Rivas-Navarrete, José Antonio Pérez-Espinosa, Humberto Padilla-Ortiz, A. L. Rodríguez-González, Ansel Y. García-Cambero, Diana Cristina affil: CICESE-UAT, CICESE, Andador 10 # 109, 63173, Tepic, Nayarit, México sug: subj: Respiratory Tract Diseases Diagnosis Chronic Disease Diagnosis Audiorecording Evaluation Machine Learning Automation Signal Processing, Computer Assisted Human Male Female Middle Age Aged Respiratory Sounds Asthma Pulmonary Disease, Chronic Obstructive Sensitivity and Specificity Smartphone Middle Aged: 45-64 years Aged: 65+ years Male Female ab: 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. pubtype: Academic Journal doctype: pictorial research tables/charts tracings Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|