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Automatic adventitious respiratory sound analysis: A systematic review
2017
PLoS ONE
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Conclusion A review of the literature was performed to summarise different analysis approaches, features, and methods used for the analysis. ...
While computerised respiratory sound analysis, specifically for the detection or classification of adventitious sounds, has recently been the focus of an increasing number of studies, a standardised approach ...
Another approach is to combine the detected segments, for example by taking a few consecutive detected segments as a positive event or by taking the mean values of extracted features. ...
doi:10.1371/journal.pone.0177926
pmid:28552969
pmcid:PMC5446130
fatcat:xptqevtq2reh5dtwriei4tgdtm
Integrated Approach for Automatic Crackle Detection Based on Fractal Dimension and Box Filtering
2016
International Journal of Reliable and Quality E-Healthcare
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The algorithm is based on 4 main procedures: i) recognition of a potential crackle; ii) verification of its validity; iii) characterisation of crackles parameters; and iv) optimisation of the algorithm ...
The algorithm performance was assessed by comparing its results with a multi-annotator gold standard agreement. High level of overall performance (F-score=92%) was achieved. ...
Therefore, the acquisition of respiratory sounds in clinical settings and the validation of automatic detection algorithms against a multi-annotator gold standard, are key features to address in the development ...
doi:10.4018/ijrqeh.2016100103
fatcat:euukajtahzbi3f3umdhsg3it6e
Feature Extraction for Machine Learning Based Crackle Detection in Lung Sounds from a Health Survey
[article]
2017
arXiv
pre-print
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We propose a machine learning based approach for detecting crackles in lung sounds recorded using a stethoscope in a large health survey. ...
Our approach had a precision of 86% and recall of 84% for classifying a crackle in a window, which is more accurate than found in studies of health personnel. ...
We believe it can be useful as a feature for detecting possible crackle candidates inside a larger audio file, but it is less useful as a feature in crackle classification. ...
arXiv:1706.00005v2
fatcat:qronnrtjangupok4wufwo42zve
A multiresolution analysis for detection of abnormal lung sounds
2012
2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
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The proposed model is based on a biomimetic multi-resolution analysis of the spectro-temporal modulation details in lung sounds. ...
The methodology provides a detailed description of joint spectral and temporal variations in the signal and proves to be more robust than frequency-based techniques in distinguishing crackles and wheezes ...
Existing approaches in the literature use techniques to capture the spectral and temporal details of sounds like wheezes and crackles, ranging from frequency analysis using Fourier transform [8] , [9 ...
doi:10.1109/embc.2012.6346630
pmid:23366591
pmcid:PMC4087194
fatcat:2qnortjjojeqtho73xvw4momea
Abnormal Respiratory Sounds Classification Using Deep CNN Through Artificial Noise Addition
2021
Frontiers in Medicine
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The proposed framework contains an adaptive mechanism of adding a similar type of noise to unhealthy respiratory sounds. ...
Respiratory sound (RS) attributes and their analyses structure a fundamental piece of pneumonic pathology, and it gives symptomatic data regarding a patient's lung. ...
All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication. ...
doi:10.3389/fmed.2021.714811
pmid:34869413
pmcid:PMC8635523
fatcat:iai4ivawqvgwzjlsz3v2hfaava
Lung Sound Classification Using Co-tuning and Stochastic Normalization
2022
IEEE Transactions on Biomedical Engineering
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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. ...
In this paper, we use pre-trained ResNet models as backbone architectures for classification of adventitious lung sounds and respiratory diseases. ...
Lung Sound Classification on our multi-channel dataset In [21] , Messner et al. introduced an event detection approach with bidirectional gated recurrent neural networks (Bi-GRNNs) using MFCCs to identify ...
doi:10.1109/tbme.2022.3156293
pmid:35254969
fatcat:ww5tlhlrlzcd3kdqnrxwsxcxgi
Automatic Crackle Detection Algorithm Based on Fractal Dimension and Box Filtering
2015
Procedia Computer Science
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This study aimed to develop an algorithm for automatic crackle detection and characterisation and to evaluate its performance and accuracy against a multi-annotator gold standard. ...
Crackles automatic detection in a respiratory sound file is challenging, and thus different signal processing methodologies have been proposed. ...
Acknowledgements This work was funded by Fundação para a Ciência e Tecnologia (PTDC/SAU-BEB/101943/2008). ...
doi:10.1016/j.procs.2015.08.592
fatcat:f2nt4qf5avdidgq3ow2ohg67vi
Multi-path Convolutional Neural Networks Efficiently Improve Feature Extraction in Continuous Adventitious Lung Sound Detection
[article]
2021
arXiv
pre-print
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Conclusively, the Multi-path CNN layers can efficiently improve the effectiveness of feature extraction and subsequently result in better CAS detection. ...
CNN layers were investigated: (1) making the CNN layers a bit deeper by using the residual blocks, (2) making the CNN layers a bit wider by increasing the number of CNN kernels, and (3) separating the ...
Acknowledgment The authors thank the employees of Heroic Faith Medical Science Co. Ltd. who have ever contributed to this study. ...
arXiv:2107.04226v1
fatcat:huz6pg2rujctjbfjuewphha5ne
Automatic Classification of Adventitious Respiratory Sounds: A (Un)Solved Problem?
2020
Sensors
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We conducted a set of experiments where we varied the durations of the other events on three tasks: crackle vs. wheeze vs. other (3 Class); crackle vs. other (2 Class Crackles); and wheeze vs. other ( ...
(1) Background: Patients with respiratory conditions typically exhibit adventitious respiratory sounds (ARS), such as wheezes and crackles. ARS events have variable duration. ...
MFCC Features The most common features used to describe the spectral shape of a sound are the MFCCs [49] . ...
doi:10.3390/s21010057
pmid:33374363
fatcat:wpqbarnpcjdsfcb3ocphbbuxm4
Lung Sound Classification Using Co-tuning and Stochastic Normalization
[article]
2021
arXiv
pre-print
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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. ...
In this paper, we use pre-trained ResNet models as backbone architectures for classification of adventitious lung sounds and respiratory diseases. ...
Lung Sound Classification on our multi-channel dataset In [19] , Messner et al. introduced an event detection approach with bidirectional gated recurrent neural networks (Bi-GRNNs) using MFCCs to identify ...
arXiv:2108.01991v1
fatcat:2o4nswj4i5bhpkvxdtlhr6vz3u
Lung crackle characteristics in patients with asbestosis, asbestos-related pleural disease and left ventricular failure using a time-expanded waveform analysis — a comparative study
1994
Respiratory Medicine
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Crackles on TEW were counted during inspiration and expiration, and the timing of clusters of crackles with respect to inspiration and expiration was noted. A total of 1117 crackles were identified. ...
Crackles in ARPD generally took the configuration of fine crackles but another type of crackle preceded by a sharp deflection followed by an M-shape oscillation then by the largest oscillation was also ...
A double buffered DMA approach was used to provide 33 s of gap-free samples. Flow at the mouth is recorded using a pneumotachograph and low pressure transducer (type EMT 32~). ...
doi:10.1016/0954-6111(94)90172-4
pmid:8029512
fatcat:xbx5rudlofeejk6jui4px7au6a
Automatic Detection and Classification of Cough Events Based on Deep Learning
2020
Current Directions in Biomedical Engineering
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AbstractIn this paper, a deep learning approach for classification of cough sound segments is presented. ...
The architecture of the network is based on a pre-trained network and the spectrogram images of three recording channels have been extracted for the sake of training the network. ...
Ethical approval: The research related to human use complies with all the relevant national regulations, institutional policies and was performed in accordance with the tenets of the Helsinki Declaration ...
doi:10.1515/cdbme-2020-3083
fatcat:ssyrfymy2nabbo2lrv2sjumlf4
Automatic Lung Health Screening Using Respiratory Sounds
2021
Journal of medical systems
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Linear Predictive Cepstral Coefficient (LPCC)-based features were used to characterize such audio clips. ...
One of such avenues is health screening based on respiratory sounds. In this paper, we developed a tool to detect respiratory sounds that come from respiratory infection carrying patients. ...
[2] developed a deep learning-based approach to detect chronic obstructive pulmonary disease. Their tool used Hilbert-Huang transform on multi-channel lung sounds. ...
doi:10.1007/s10916-020-01681-9
pmid:33426615
fatcat:xgqrpz2aijaofjeozec42525gy
Development of Classification Methods for Wheeze and Crackle Using Mel Frequency Cepstral Coefficient (MFCC): A Deep Learning Approach
2020
International Journal on Perceptive and Cognitive Computing
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There have been efforts to address this problem using a myriad of machine learning algorithms, but little has been done using deep learning. ...
The most common method used by physicians and pulmonologists to evaluate the state of the lung is by listening to the acoustics of the patient's breathing by a stethoscope. ...
The authors are deeply grateful to the Department of Computer Science. ...
doi:10.31436/ijpcc.v6i2.166
fatcat:zt37ijiysjeyfbos6axdfct44e
A Wearable Multimodal Sensing System for Tracking Changes in Pulmonary Fluid Status, Lung Sounds, and Respiratory Markers
2022
Sensors
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We present a multimodal sensing system that captures bioimpedance spectroscopy (BIS), multi-channel lung sounds from four contact microphones, multi-frequency impedance pneumography (IP), temperature, ...
The IP-derived respiratory signals and lung sounds were sensitive enough to detect abnormal respiratory patterns (Cheyne–Stokes) and inspiratory crackles from patient recordings, respectively. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/s22031130
pmid:35161876
pmcid:PMC8838360
fatcat:2uvisuik6rd43etzg2ml7ynflm
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