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Automatic adventitious respiratory sound analysis: A systematic review

Renard Xaviero Adhi Pramono, Stuart Bowyer, Esther Rodriguez-Villegas, Thomas Penzel
2017 PLoS ONE  
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

Cátia Pinho, Ana Oliveira, Cristina Jácome, João Manuel Rodrigues, Alda Marques
2016 International Journal of Reliable and Quality E-Healthcare  
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]

Morten Grønnesby, Juan Carlos Aviles Solis, Einar Holsbø, Hasse Melbye, Lars Ailo Bongo
2017 arXiv   pre-print
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

D. Emmanouilidou, K. Patil, J. West, M. Elhilali
2012 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society  
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

Rizwana Zulfiqar, Fiaz Majeed, Rizwana Irfan, Hafiz Tayyab Rauf, Elhadj Benkhelifa, Abdelkader Nasreddine Belkacem
2021 Frontiers in Medicine  
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

Truc Nguyen, Franz Pernkopf
2022 IEEE Transactions on Biomedical Engineering  
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

Cátia Pinho, Ana Oliveira, Cristina Jácome, João Rodrigues, Alda Marques
2015 Procedia Computer Science  
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]

Fu-Shun Hsu, Shang-Ran Huang, Chien-Wen Huang, Chun-Chieh Chen, Yuan-Ren Cheng, Feipei Lai
2021 arXiv   pre-print
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?

Bruno Machado Rocha, Diogo Pessoa, Alda Marques, Paulo Carvalho, Rui Pedro Paiva
2020 Sensors  
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]

Truc Nguyen, Franz Pernkopf
2021 arXiv   pre-print
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

N. Al Jarad, S.W. Davies, R. Logan-Sinclair, R.M. Rudd
1994 Respiratory Medicine  
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

Seyed Amir Hossein Tabatabaei, Gabriela Augustinov, Volker Gross, Keywan Sohrabi, Patrick Fischer, Ulrich Koehler
2020 Current Directions in Biomedical Engineering  
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

Himadri Mukherjee, Priyanka Sreerama, Ankita Dhar, Sk. Md. Obaidullah, Kaushik Roy, Mufti Mahmud, K.C. Santosh
2021 Journal of medical systems  
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

Tinir Mohamed Sadi, Raini Hassan
2020 International Journal on Perceptive and Cognitive Computing  
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

Jesus Antonio Sanchez-Perez, John A. Berkebile, Brandi N. Nevius, Goktug C. Ozmen, Christopher J. Nichols, Venu G. Ganti, Samer A. Mabrouk, Gari D. Clifford, Rishikesan Kamaleswaran, David W. Wright, Omer T. Inan
2022 Sensors  
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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