Wheeze Events Detection Using Convolutional Recurrent Neural Network

Abstract

Chronic respiratory disorders (CRDs) affect the airways and other structures in the lungs. According to WHO, CRDs are a major cause of death globally. Early diagnosis and monitoring of individuals with respiratory disorders are crucial due to the severity and prevalence of these disorders. Auscultation is a common method used to diagnose respiratory patients. However, the classical auscultation procedure has some limitations, such as being subjective, depending on the physician’s expertise, and being inaccurate in noisy environments. To tackle those limitations, this project aims to implement a method for the detection of adventitious respiratory sounds, particularly wheeze sounds, using data derived from ICBHI open data. Short-time Fourier transforms (STFT) of the audio data were applied for the feature extraction. The system was implemented to perform wheeze sound detection using a recurrent neural network (RNN) based deep-learning model.

Keywords

Wheeze Auscultation Respiratory sounds Computer science Feature extraction Short-time Fourier transform Convolutional neural network Recurrent neural network Speech recognition Respiratory system Artificial intelligence Feature (linguistics) Deep learning Artificial neural network Pattern recognition (psychology) Medicine Audiology Fourier transform Cardiology Internal medicine Asthma Fourier analysis Mathematics

Subject Areas

Phonocardiography and Auscultation Techniques ·Pulmonary and Respiratory Medicine ·Health Sciences

Respiratory and Cough-Related Research ·Pulmonary and Respiratory Medicine ·Health Sciences

Noise Effects and Management ·Speech and Hearing ·Health Sciences

Citations by Year

OpenAlex SDG Match

SDGs auto-classified by OpenAlex (score ≥ 0.4 shown).

Good health and well-being 75%

Wheeze Events Detection Using Convolutional Recurrent Neural Network

Abstract

Keywords

Subject Areas

Citations by Year

OpenAlex SDG Match

References (33)

Cited by (2)

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