Chronic Obstructive Pulmonary Disease Severity Classification using lung sound

Khanaghavalle, G R; Rahul, G; Senajith, S R; Vishnuvasan, T S; Keerthana, S

doi:10.1109/iccsp60870.2024.10543344

2024 10th International Conference on Communication and Signal Processing (ICCSP) 2024

DOI: 10.1109/iccsp60870.2024.10543344

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Chronic Obstructive Pulmonary Disease Severity Classification using lung sound

G R Khanaghavalle,

G Rahul,

S R Senajith

et al.

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2024

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Cited by 1 publication

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Fused Audio Instance and Representation for Respiratory Disease Detection

Truong,

Lenga,

Serrurier

et al. 2024

Sensors

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Audio-based classification techniques for body sounds have long been studied to aid in the diagnosis of respiratory diseases. While most research is centered on the use of coughs as the main acoustic biomarker, other body sounds also have the potential to detect respiratory diseases. Recent studies on the coronavirus disease 2019 (COVID-19) have suggested that breath and speech sounds, in addition to cough, correlate with the disease. Our study proposes fused audio instance and representation (FAIR) as a method for respiratory disease detection. FAIR relies on constructing a joint feature vector from various body sounds represented in waveform and spectrogram form. We conduct experiments on the use case of COVID-19 detection by combining waveform and spectrogram representation of body sounds. Our findings show that the use of self-attention to combine extracted features from cough, breath, and speech sounds leads to the best performance with an area under the receiver operating characteristic curve (AUC) score of 0.8658, a sensitivity of 0.8057, and a specificity of 0.7958. Compared to models trained solely on spectrograms or waveforms, the use of both representations results in an improved AUC score, demonstrating that combining spectrogram and waveform representation helps to enrich the extracted features and outperforms the models that use only one representation. While this study focuses on COVID-19, FAIR’s flexibility allows it to combine various multi-modal and multi-instance features in many other diagnostic applications, potentially leading to more accurate diagnoses across a wider range of diseases.

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Fused Audio Instance and Representation for Respiratory Disease Detection

Truong,

Lenga,

Serrurier

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

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Chronic Obstructive Pulmonary Disease Severity Classification using lung sound

Cited by 1 publication

References 12 publications

Fused Audio Instance and Representation for Respiratory Disease Detection

Fused Audio Instance and Representation for Respiratory Disease Detection

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