A Smartphone-Based System for Automated Bedside Detection of Crackle Sounds in Diffuse Interstitial Pneumonia Patients

• Published in: Sensors (Basel, Switzerland) Vol. 18; no. 11; p. 3813
• Main Authors: Reyes, Bersain A, Olvera-Montes, Nemecio, Charleston-Villalobos, Sonia, González-Camarena, Ramón, Mejía-Ávila, Mayra, Aljama-Corrales, Tomas
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI 07.11.2018; MDPI AG
• Subjects: automatic detection; crackles; Humans; Lung Diseases, Interstitial - diagnosis; Lung Diseases, Interstitial - physiopathology; Mobile Applications; respiratory sounds; Respiratory Sounds - diagnosis; Respiratory Sounds - physiopathology; Signal Processing, Computer-Assisted; smartphone; Smartphone - instrumentation; Sound; Stethoscopes; time-varying autoregressive model; respiratory sounds; smartphone; automatic detection; crackles; time-varying autoregressive model
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s18113813

In this work, we present a mobile health system for the automated detection of crackle sounds comprised by an acoustical sensor, a smartphone device, and a mobile application (app) implemented in Android. Although pulmonary auscultation with traditional stethoscopes had been used for decades, it has limitations for detecting discontinuous adventitious respiratory sounds (crackles) that commonly occur in respiratory diseases. The proposed app allows the physician to record, store, reproduce, and analyze respiratory sounds directly on the smartphone. Furthermore, the algorithm for crackle detection was based on a time-varying autoregressive modeling. The performance of the automated detector was analyzed using: (1) synthetic fine and coarse crackle sounds randomly inserted to the basal respiratory sounds acquired from healthy subjects with different signal to noise ratios, and (2) real bedside acquired respiratory sounds from patients with interstitial diffuse pneumonia. In simulated scenarios, for fine crackles, an accuracy ranging from 84.86% to 89.16%, a sensitivity ranging from 93.45% to 97.65%, and a specificity ranging from 99.82% to 99.84% were found. The detection of coarse crackles was found to be a more challenging task in the simulated scenarios. In the case of real data, the results show the feasibility of using the developed mobile health system in clinical no controlled environment to help the expert in evaluating the pulmonary state of a subject.