Fetching the paper…
Reading the bibliography…
We present a machine learning based COVID-19 cough classifier which can discriminate COVID-19 positive coughs from both COVID-19 negative and healthy coughs recorded on a smartphone.
P. A. Devijver and J. Kittler, Pattern recognition: A statistical approach . Prentice Hall, 1982
1982
Earlier work this paper cites.
S. Le Cessie and J. C. Van Houwelingen, “Ridge estimators in logistic regression,” Journal of the Royal Statistical Society: Series C (Applied Statistics) , vol. 41, no. 1, pp. 191–201, 1992
1992
Earlier work this paper cites.
J. Korpáš, J. Sadloňová, and M. Vrabec, “Analysis of the cough sound: an overview,” Pulmonary Pharmacology , vol. 9, no. 5-6, pp. 261–268, 1996
1996
Earlier work this paper cites.
L. T. DeCarlo, “On the meaning and use of kurtosis.” Psychological Methods , vol. 2, no. 3, p. 292, 1997
1997
Earlier work this paper cites.
S. Lawrence, C. L. Giles, A. C. Tsoi, and A. D. Back, “Face recognition: A convolutional neural-network approach,” IEEE Transactions on Neural Networks , vol. 8, no. 1, pp. 98–113, 1997
1997
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation , vol. 9, no. 8, pp. 1735–1780, 1997
1997
Earlier work this paper cites.
T. Higenbottam, “Chronic cough and the cough reflex in common lung diseases,” Pulmonary Pharmacology & Therapeutics , vol. 15, no. 3, pp. 241–247, 2002
2002
Earlier work this paper cites.
N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: synthetic minority over-sampling technique,” Journal of Artificial Intelligence Research , vol. 16, pp. 321–357, 2002
2002
Earlier work this paper cites.
WHO et al. , “Summary of probable SARS cases with onset of illness from 1 November 2002 to 31 July 2003,” http://www.who.int/csr/sars/country/table2004_04_21/en/index. html , 2003
2003
Earlier work this paper cites.
H. Yamashita and H. Yabe, “An interior point method with a primal-dual quadratic barrier penalty function for nonlinear optimization,” SIAM Journal on Optimization , vol. 14, no. 2, pp. 479–499, 2003
2003
Earlier work this paper cites.
H. Han, W.-Y. Wang, and B.-H. Mao, “Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning,” in International Conference on Intelligent Computing . Springer, 2005, pp. 878–887
2005
Earlier work this paper cites.
Wei Han, Cheong-Fat Chan, Chiu-Sing Choy, and Kong-Pang Pun, “An efficient MFCC extraction method in speech recognition,” in IEEE International Symposium on Circuits and Systems , 2006
2006
Earlier work this paper cites.
J.-C. Wang, J.-F. Wang, K. W. He, and C.-S. Hsu, “Environmental sound classification using hybrid SVM/KNN classifier and MPEG-7 audio low-level descriptor,” in The 2006 IEEE International Joint Conference on Neural Network Proceedings . IEEE, 2006, pp. 1731–1735
2006
Earlier work this paper cites.
T. Fawcett, “An introduction to ROC analysis,” Pattern Recognition Letters , vol. 27, no. 8, pp. 861–874, 2006
2006
Earlier work this paper cites.
J. Van Hulse, T. M. Khoshgoftaar, and A. Napolitano, “Experimental perspectives on learning from imbalanced data,” in Proceedings of the 24th International Conference on Machine Learning , 2007, pp. 935–942
2007
Earlier work this paper cites.
A. Chang, G. Redding, and M. Everard, “Chronic wet cough: protracted bronchitis, chronic suppurative lung disease and bronchiectasis,” Pediatric Pulmonology , vol. 43, no. 6, pp. 519–531, 2008
2008
Earlier work this paper cites.
K. F. Chung and I. D. Pavord, “Prevalence, pathogenesis, and causes of chronic cough,” The Lancet , vol. 371, no. 9621, pp. 1364–1374, 2008
2008
Earlier work this paper cites.
J. Knocikova, J. Korpas, M. Vrabec, and M. Javorka, “Wavelet analysis of voluntary cough sound in patients with respiratory diseases,” Journal of Physiology and Pharmacology , vol. 59, no. Suppl 6, pp. 331–40, 2008
2008
Earlier work this paper cites.
H. He, Y. Bai, E. A. Garcia, and S. Li, “ADASYN: Adaptive synthetic sampling approach for imbalanced learning,” in 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence) . IEEE, 2008, pp. 1322–1328
2008
Earlier work this paper cites.
S. Aydın, H. M. Saraoğlu, and S. Kara, “Log energy entropy-based EEG classification with multilayer neural networks in seizure,” Annals of Biomedical Engineering , vol. 37, no. 12, p. 2626, 2009
2009
Earlier work this paper cites.
Y. Tsuruoka, J. Tsujii, and S. Ananiadou, “Stochastic gradient descent training for l1-regularized log-linear models with cumulative penalty,” in Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP , 2009, pp. 477–485
2009
Earlier work this paper cites.
R. Bachu, S. Kopparthi, B. Adapa, and B. D. Barkana, “Voiced/unvoiced decision for speech signals based on zero-crossing rate and energy,” in Advanced Techniques in Computing Sciences and Software Engineering . Springer, 2010, pp. 279–282
2010
Earlier work this paper cites.
H. M. Nguyen, E. W. Cooper, and K. Kamei, “Borderline over-sampling for imbalanced data classification,” International Journal of Knowledge Engineering and Soft Data Paradigms , vol. 3, no. 1, pp. 4–21, 2011
2011
Earlier work this paper cites.
H. Chatrzarrin, A. Arcelus, R. Goubran, and F. Knoefel, “Feature extraction for the differentiation of dry and wet cough sounds,” in IEEE International Symposium on Medical Measurements and Applications . IEEE, 2011
2011
Earlier work this paper cites.
B. H. Tracey, G. Comina, S. Larson, M. Bravard, J. W. López, and R. H. Gilman, “Cough detection algorithm for monitoring patient recovery from pulmonary tuberculosis,” in 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society . IEEE, 2011, pp. 6017–6020
2011
Earlier work this paper cites.
R. Miyata, N. Tanuma, M. Hayashi, T. Imamura, J.-i. Takanashi, R. Nagata, A. Okumura, H. Kashii, S. Tomita, S. Kumada et al. , “Oxidative stress in patients with clinically mild encephalitis/encephalopathy with a reversible splenial lesion (MERS),” Brain and Development , vol. 34, no. 2, pp. 124–127, 2012
2012
Earlier work this paper cites.
M. Al-khassaweneh and R. Bani Abdelrahman, “A signal processing approach for the diagnosis of asthma from cough sounds,” Journal of Medical Engineering & Technology , vol. 37, no. 3, pp. 165–171, 2013
2013
Earlier work this paper cites.
L. L. Blagus, R., “SMOTE for high-dimensional class-imbalanced data,” BMC Bioinformatics , vol. 14, p. 106, 2013
2013
Cited alongside, same era.
J.-M. Liu, M. You, Z. Wang, G.-Z. Li, X. Xu, and Z. Qiu, “Cough detection using deep neural networks,” in 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) . IEEE, 2014, pp. 560–563
2014
Cited alongside, same era.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in European Conference on Computer Vision . Springer, 2014, pp. 740–755
2014
Cited alongside, same era.
J. Amoh and K. Odame, “DeepCough: A deep convolutional neural network in a wearable cough detection system,” in 2015 IEEE Biomedical Circuits and Systems Conference (BioCAS) . IEEE, 2015, pp. 1–4
2015
Cited alongside, same era.
D. Wang, B. Hu, C. Hu, F. Zhu, X. Liu, J. Zhang, B. Wang, H. Xiang, Z. Cheng, Y. Xiong et al. , “Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus–infected pneumonia in Wuhan, China,” JAMA , vol. 323, no. 11, pp. 1061–1069, 2020
2020
Closest in time.
A. Carfì, R. Bernabei, F. Landi et al. , “Persistent symptoms in patients after acute COVID-19,” JAMA , vol. 324, no. 6, pp. 603–605, 2020
2020
Closest in time.
John Hopkins University. (2020, Nov.) COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE). John Hopkins University. [Online]. Available: https://coronavirus.jhu.edu
2020
Closest in time.
S. Walvekar, D. Shinde et al. , “Detection of COVID-19 from CT images using Resnet50,” in 2nd International Conference on Communication & Information Processing (ICCIP) 2020 , May 2020, available at SSRN: https://ssrn.com/abstract=3648863 or http://dx.doi.org/10.2139/ssrn.3648863
2020
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
E. Marchi, F. Vesperini, F. Weninger, F. Eyben, S. Squartini, and B. Schuller, “Non-linear prediction with LSTM recurrent neural networks for acoustic novelty detection,” in 2015 International Joint Conference on Neural Networks (IJCNN) . IEEE, 2015, pp. 1–7
2015
Cited alongside, same era.
R. X. A. Pramono, S. A. Imtiaz, and E. Rodriguez-Villegas, “A cough-based algorithm for automatic diagnosis of pertussis,” PloS one , vol. 11, no. 9, p. e0162128, 2016
2016
Cited alongside, same era.
B. Krawczyk, “Learning from imbalanced data: open challenges and future directions,” Progress in Artificial Intelligence , vol. 5, no. 4, pp. 221–232, 2016
2016
Cited alongside, same era.
L. Sarangi, M. N. Mohanty, and S. Pattanayak, “Design of MLP Based Model for Analysis of Patient Suffering from Influenza,” Procedia Computer Science , vol. 92, pp. 396–403, 2016
2016
Cited alongside, same era.
J. Amoh and K. Odame, “Deep neural networks for identifying cough sounds,” IEEE transactions on Biomedical Circuits and Systems , vol. 10, no. 5, pp. 1003–1011, 2016
2016
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 770–778
2016
Cited alongside, same era.
G. Lemaître, F. Nogueira, and C. K. Aridas, “Imbalanced-learn: A Python toolbox to tackle the curse of imbalanced datasets in machine learning,” The Journal of Machine Learning Research , vol. 18, no. 1, pp. 559–563, 2017
2017
Cited alongside, same era.
M. M. Azmy, “Feature extraction of heart sounds using velocity and acceleration of MFCCs based on support vector machines,” in 2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT) , 2017, pp. 1–4
2017
Cited alongside, same era.
H. Sotoudeh, M. Tabatabaei, B. Tasorian, K. Tavakol, E. Sotoudeh, and A. L. Moini, “Artificial Intelligence Empowers Radiologists to Differentiate Pneumonia Induced by COVID-19 versus Influenza Viruses,” Acta Informatica Medica , vol. 28, no. 3, p. 190, 2020
2020
Closest in time.
M. Yildirim and A. Cinar, “A Deep Learning Based Hybrid Approach for COVID-19 Disease Detections,” Traitement du Signal , vol. 37, no. 3, pp. 461–468, 2020
2020
Closest in time.
G. Rudraraju, S. Palreddy, B. Mamidgi, N. R. Sripada, Y. P. Sai, N. K. Vodnala, and S. P. Haranath, “Cough sound analysis and objective correlation with spirometry and clinical diagnosis,” Informatics in Medicine Unlocked , p. 100319, 2020
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.
C. Brown, J. Chauhan, A. Grammenos, J. Han, A. Hasthanasombat, D. Spathis, T. Xia, P. Cicuta, and C. Mascolo, “Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data,” in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 2020, pp. 3474–3484
2020
Closest in time.
A. Imran, I. Posokhova, H. N. Qureshi, U. Masood, S. Riaz, K. Ali, C. N. John, and M. Nabeel, “AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app,” Informatics in Medicine Unlocked , vol. 20, p. 100378, 2020
2020
Closest in time.
2020
Closest in time.
J. Laguarta, F. Hueto, and B. Subirana, “COVID-19 Artificial Intelligence Diagnosis using only Cough Recordings,” IEEE Open Journal of Engineering in Medicine and Biology , 2020
2020
Closest in time.
M. Cohen-McFarlane, R. Goubran, and F. Knoefel, “Novel Coronavirus Cough Database: NoCoCoDa,” IEEE Access , vol. 8, pp. 154 087–154 094, 2020
2020
Closest in time.
2020
Closest in time.
M. Pahar and L. S. Smith, “Coding and Decoding Speech using a Biologically Inspired Coding System,” in 2020 IEEE Symposium Series on Computational Intelligence (SSCI) . IEEE, 2020, pp. 3025–3032
2020
Closest in time.
A. Sherstinsky, “Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network,” Physica D: Nonlinear Phenomena , vol. 404, p. 132306, 2020
2020
Closest in time.
J. Laguarta, F. Hueto, P. Rajasekaran, S. Sarma, and B. Subirana, “Longitudinal Speech Biomarkers for Automated Alzheimer’s Detection,” 2020
2020
Closest in time.
M. Pahar, I. Miranda, A. Diacon, and T. Niesler, “Deep Neural Network based Cough Detection using Bed-mounted Accelerometer Measurements,” in ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2021, pp. 8002–8006
2021
Closest in time.
A. N. Belkacem, S. Ouhbi, A. Lakas, E. Benkhelifa, and C. Chen, “End-to-End AI-based Point-of-Care Diagnosis System for Classifying Respiratory Illnesses and Early Detection of COVID-19: A Theoretical Framework,” Frontiers in Medicine , vol. 8, p. 372, 2021
2021
Closest in time.
A. Pal and M. Sankarasubbu, “Pay attention to the cough: Early diagnosis of COVID-19 using interpretable symptoms embeddings with cough sound signal processing,” in Proceedings of the 36th Annual ACM Symposium on Applied Computing , 2021, pp. 620–628
2021
Closest in time.
J. Andreu-Perez, H. Pérez-Espinosa, E. Timonet, M. Kiani, M. I. Giron-Perez, A. B. Benitez-Trinidad, D. Jarchi, A. Rosales, N. Gkatzoulis, O. F. Reyes-Galaviz et al. , “A Generic Deep Learning Based Cough Analysis System from Clinically Validated Samples for Point-of-Need Covid-19 Test and Severity Levels,” IEEE Transactions on Services Computing , no. 01, pp. 1–1, 2021
2021
Closest in time.
2021
Closest in time.
2021
Closest in time.
2021
Closest in time.
A. Rácz, D. Bajusz, and K. Héberger, “Effect of Dataset Size and Train/Test Split Ratios in QSAR/QSPR Multiclass Classification,” Molecules , vol. 26, no. 4, p. 1111, 2021
2021
Closest in time.