Grand Challenge on Respiratory Sound Classification for SPRSound Dataset

Zhang, Qing; Zhang, Jing; Yuan, Jiang; Huang, Huajie; Zhang, Yuhang; Zhang, Baoqin; Lv, Gaomei; Lin, Shuzhu; Wang, Na; Liu, Xin; Tang, Mingyu; Wang, Yahua; Ma, Hui; Liu, Lu; Yuan, Shuo; Zhou, Hongyuan; Zhao, Jian; Li, Yongfu; Yin, Yong; Zhao, Liebin; Wang, Guoxing; Lian, Yong

doi:10.1109/biocas54905.2022.9948551

Cited by 10 publications

(1 citation statement)

References 25 publications

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“…Notably, each respiratory cycle within the recordings is also annotated based on the existence of crackles, wheezes, both crackles and wheezes, or the absence of these adventitious sounds, making it ideal for disease or lower respiratory disease symptom sound classification tasks. Another dataset for respiratory sound classification is the SPRSound dataset [ 95 ], which was created within the context of IEEE BioCAS 2022 Respiratory challenge [ 99 ]. It consists of 2683 records and 9089 respiratory sound events from 292 participants.…”

Section: Publicly Available Datasetsmentioning

confidence: 99%

Respiratory Diseases Diagnosis Using Audio Analysis and Artificial Intelligence: A Systematic Review

Kapetanidis,

Kalioras,

Tsakonas

et al. 2024

Sensors

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Respiratory diseases represent a significant global burden, necessitating efficient diagnostic methods for timely intervention. Digital biomarkers based on audio, acoustics, and sound from the upper and lower respiratory system, as well as the voice, have emerged as valuable indicators of respiratory functionality. Recent advancements in machine learning (ML) algorithms offer promising avenues for the identification and diagnosis of respiratory diseases through the analysis and processing of such audio-based biomarkers. An ever-increasing number of studies employ ML techniques to extract meaningful information from audio biomarkers. Beyond disease identification, these studies explore diverse aspects such as the recognition of cough sounds amidst environmental noise, the analysis of respiratory sounds to detect respiratory symptoms like wheezes and crackles, as well as the analysis of the voice/speech for the evaluation of human voice abnormalities. To provide a more in-depth analysis, this review examines 75 relevant audio analysis studies across three distinct areas of concern based on respiratory diseases’ symptoms: (a) cough detection, (b) lower respiratory symptoms identification, and (c) diagnostics from the voice and speech. Furthermore, publicly available datasets commonly utilized in this domain are presented. It is observed that research trends are influenced by the pandemic, with a surge in studies on COVID-19 diagnosis, mobile data acquisition, and remote diagnosis systems.

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Section: Publicly Available Datasetsmentioning

confidence: 99%