RespireNet: A Deep Neural Network for Accurately Detecting Abnormal Lung Sounds in Limited Data Setting

Gairola, Siddhartha; Tom, Francis; Kwatra, Nipun; Jain, Mohit

doi:10.48550/arxiv.2011.00196

Cited by 3 publications

(9 citation statements)

References 22 publications

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“…The most common approach is placing the device on the chest area, like a typical auscultation device. This type of data acquisition can be conducted by digital stethoscopes, and the authors in [ 47 , 52 , 53 , 56 , 58 , 59 , 60 , 61 , 62 , 63 , 66 ] utilized data which were derived from this kind of data retrieval methodology. Most of the studies mention that they did not execute the data acquisition phase themselves, but they exploited open-source or proprietary datasets.…”

Section: Lung and Breath Sounds Analysis Of Lower Respiratory Symptomsmentioning

confidence: 99%

“…This results in an increase in the models’ generalization capability, while also expanding the field of application where the trained algorithm can be used. Data constructed by this methodology are employed in [ 44 , 51 , 54 , 55 , 57 , 61 , 62 , 67 , 68 ]. On the other hand, only a limited number of studies have employed smartphone recordings as a means of data collection, with one notable example being [ 63 ], which utilized various smartphones to record lung sounds.…”

Section: Lung and Breath Sounds Analysis Of Lower Respiratory Symptomsmentioning

confidence: 99%

See 1 more Smart Citation

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: Lung and Breath Sounds Analysis Of Lower Respiratory Symptomsmentioning

confidence: 99%

Section: Lung and Breath Sounds Analysis Of Lower Respiratory Symptomsmentioning

confidence: 99%

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

Kapetanidis,

Kalioras,

Tsakonas

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…This binary classification task is to detect whether a breathing sound segment contains abnormalities, including crackle and wheeze. We use the backbone of a deep convolutional model (ResNet) proposed in (Gairola et al 2020). Dropout (p = 0.5) and batch normalisation are leveraged to reduce over-fitting.…”

Section: Appendix Task Implementationmentioning

confidence: 99%

Benchmarking Uncertainty Quantification on Biosignal Classification Tasks under Dataset Shift

Tong¹,

Han²,

Mascolo³

2021

Preprint

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A biosignal is a signal that can be continuously measured from human bodies, such as respiratory sounds, heart activity (ECG), brain waves (EEG), etc, based on which, machine learning models have been developed with very promising performance for automatic disease detection and health status monitoring. However, dataset shift, i.e., data distribution of inference varies from the distribution of the training, is not uncommon for real biosignal-based applications. To improve the robustness, probabilistic models with uncertainty qualification are adapted to capture how reliable a prediction is. Yet, assessing the quality of the estimated uncertainty remains a challenge. In this work, we propose a framework to evaluate the capability of the estimated uncertainty in capturing different types of biosignal dataset shifts with various degrees. In particular, we use three classification tasks based on respiratory sounds and electrocardiography signals to benchmark five representative uncertainty qualification methods. Extensive experiments show that, although Ensemble and Bayesian models could provide relatively better uncertainty estimations under dataset shifts, all tested models fail to meet the promise in trustworthy prediction and model calibration. Our work paves the way for a comprehensive evaluation for any newly developed biosignal classifiers.

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“…We observe a different frequency response across devices which results in a performance degradation for underrepresented devices. Hence, we calibrate the features of the audio segments by applying spectrum correction instead of training or fine-tuning the model for a specific device [31], [32]. The spectrum correction or calibration proposed in [33], which was first applied for acoustic scene classification, scales the frequency response of the recording devices.…”

Section: B Spectrum Correctionmentioning

confidence: 99%

“…al. [32] proposed a RespireNet model based on ResNet34 and fully connected layers with a set of techniques i.e. device specific fine-tuning, concatenationbased augmentation, blank region clipping and smart padding to improve the accuracy.…”

Section: A Lung Sound Classification On Icbhi Datasetmentioning

confidence: 99%

Lung Sound Classification Using Co-tuning and Stochastic Normalization

Nguyen¹,

Pernkopf²

2021

Preprint

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In this paper, we use pre-trained ResNet models as backbone architectures for classification of adventitious lung sounds and respiratory diseases. The knowledge of the pretrained model is transferred by using vanilla fine-tuning, cotuning, stochastic normalization and the combination of the cotuning and stochastic normalization techniques. Furthermore, data augmentation in both time domain and time-frequency domain is used to account for the class imbalance of the ICBHI and our multi-channel lung sound dataset. Additionally, we apply spectrum correction to consider the variations of the recording device properties on the ICBHI dataset. Empirically, our proposed systems mostly outperform all state-of-the-art lung sound classification systems for the adventitious lung sounds and respiratory diseases of both datasets.

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RespireNet: A Deep Neural Network for Accurately Detecting Abnormal Lung Sounds in Limited Data Setting

Cited by 3 publications

References 22 publications

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

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

Benchmarking Uncertainty Quantification on Biosignal Classification Tasks under Dataset Shift

Lung Sound Classification Using Co-tuning and Stochastic Normalization

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