Automatic COVID-19 disease diagnosis using 1D convolutional neural network and augmentation with human respiratory sound based on parameters: cough, breath, and voice

Kranthi Kumar Lella; Alphonse Pja; Kranthi Kumar Lella; Alphonse Pja

doi:10.3934/publichealth.2021019

AIMS Public Health

2021, Volume 8, Issue 2: 240-264. doi: 10.3934/publichealth.2021019

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Automatic COVID-19 disease diagnosis using 1D convolutional neural network and augmentation with human respiratory sound based on parameters: cough, breath, and voice

Kranthi Kumar Lella ,
Alphonse Pja ^,

Department of Computer Applications, NIT Tiruchirappalli, Tamil Nadu, India

Received: 25 January 2021 Accepted: 07 March 2021 Published: 10 March 2021

- 1D CNN,
- COVID-19,
- respiratory sounds,
- augmentation,
- data de-noising auto encoder
Citation: Kranthi Kumar Lella, Alphonse Pja. Automatic COVID-19 disease diagnosis using 1D convolutional neural network and augmentation with human respiratory sound based on parameters: cough, breath, and voice[J]. AIMS Public Health, 2021, 8(2): 240-264. doi: 10.3934/publichealth.2021019

Related Papers:

Abbreviations

CNN

Convolutional Neural Network

DDAE

Data De-Noising Auto Encoder

MFCC

Mel-frequency Cepstral Coefficient

Deep Learning

Machine Learning

Artificial Intelligence

SVM

Support Vector Machine

LVQ

Learning Vector Quantization

MLR

Multivariate Linear Regression

MRI

Magnetic Resonance Imaging

SSP

Speech Signal Processing

LSTM

Long Short-Term Memory

TDSN

Tensor Deep Stacking Network

CRD

Compression of Range Dynamically

Background Noise

Stretching Time

Shift Pitch

ReLU

Rectified Linear Unit

MUDA

Musical Data Augmentation

JAMS

JSON Annotated Music Specification

Acknowledgments

We would like to express our sincere gratitude to Prof. Cecilia Mascolo, clinical scientists at Cambridge University, for sharing the dataset. We acknowledge everyone who is trying to stop the COVID-19 pandemic.

Author contributions

All authors have participated in (a) conception and design, or analysis and interpretation of the data; (b) drafting the article or revising it critically for important intellectual content; and (c) approval of the final version. The authors have not been submitted this article too nor under review to any journal or publishing body.

Conflict of interest

The authors have declared no conflict of interest.

References

[1]	World Health Organization. Coronavirus disease 2019 (covid-19) (2020) .Available from: https://www.who.int/.
[2]	Wang Y, Hu M, Li Q, et al. (2020) Abnormal respiratory patterns classifier may contribute to large-scale screening of people infected with COVID-19 in an accurate and unobtrusive manner. arXiv:2002.05534 [cs.LG] .
[3]	Jiang Z, Hu M, Lei F, et al. (2020) Combining Visible Light and Infrared Imaging for Efficient Detection of Respiratory Infections Such as Covid-19 on Portable Device. arXiv:2004.06912 [cs.CV] .
[4]	Imran A, Posokhova I, Qureshi HN, et al. (2020) AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app. Inform Med Unlocked 20: 100378. doi: 10.1016/j.imu.2020.100378
[5]	Shuja J, Alanazi E, Alasmary W, et al. (2020) COVID-19 open source data sets: a comprehensive survey. Appl Intell 21: 1-30.
[6]	Rasheed J, Jamil A, Hameed AA, et al. (2020) A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic. Chaos Solitons Fractals 141: 110337. doi: 10.1016/j.chaos.2020.110337
[7]	Alafif T, Tehame AM, Bajaba S, et al. (2021) Machine and Deep Learning towards COVID-19 Diagnosis and Treatment: Survey, Challenges, and Future Directions. Int J Environ Res Public Health 18: 1117. doi: 10.3390/ijerph18031117
[8]	Ritwik KVS, Shareef BK, Deepu V (2020) Covid-19 Patient Detection from Telephone Quality Speech Data. arXiv:2011.04299v1 [cs.SD] .
[9]	Kranthi KL, Alphonse PJA (2021) A literature review on COVID-19 disease diagnosis from respiratory sound data. AIMS Bioeng 8: 140-153. doi: 10.3934/bioeng.2021013
[10]	Huang Y, Meng S, Zhang Y, et al. (2020) The respiratory sound features of COVID-19 patients fill gaps between clinical data and screening methods. medRxiv 2020.04.07.20051060 .
[11]	Shi J, Zheng X, Li Y, et al. (2018) Multimodal Neuroimaging Feature Learning With Multimodal Stacked Deep Polynomial Networks for Diagnosis of Alzheimer's Disease. IEEE J Biomed Health Inform 22: 173-183. doi: 10.1109/JBHI.2017.2655720
[12]	Brabenec L, Mekyska J, Galaz Z, et al. (2017) Speech disorders in Parkinson's disease: early diagnostics and effects of medication and brain stimulation. J Neural Transm (Vienna) 124: 303-334. doi: 10.1007/s00702-017-1676-0
[13]	Erdogdu SB, Serbes G, Sakar CO (2017) Analyzing the effectiveness of vocal features in early telediagnosis of Parkinson's disease. PLoS ONE 12: e0182428. doi: 10.1371/journal.pone.0182428
[14]	Li F, Liu M, Zhao Y, et al. (2019) Feature extraction and classification of heart sound using 1D convolutional neural networks. EURASIP J Adv Signal Process 59.
[15]	Klára V, Viktor I, Krisztina M (2011) Voice Disorder Detection on the Basis of Continuous Speech. 5th European Conference of the International Federation for Medical and Biological Engineering Berlin, Heidelberg: IFMBE Proceedings, Springer.
[16]	Verde L, De Pietro D, Sannino G (2018) Voice Disorder Identification by Using Machine Learning Techniques. IEEE Access 6: 16246-16255. doi: 10.1109/ACCESS.2018.2816338
[17]	Bader M, Shahin I, Hassan A (2020) Studying the Similarity of COVID-19 Sounds based on Correlation Analysis of MFCC. arXiv:2010.08770 [cs.SD] .
[18]	Sahidullah Md, Saha G (2012) Design, analysis and experimental evaluation of block-based transformation in MFCC computation for speaker recognition. Speech Commun 54: 543-565. doi: 10.1016/j.specom.2011.11.004
[19]	Srinivasamurthy RS (2018) Understanding 1D Convolutional Neural Networks Using Multiclass Time-Varying Signals. All Thesis. Available from: https://tigerprints.clemson.edu/cgi/viewcontent.cgi?article=3918&context=all_theses.
[20]	Zhao W, Singh R (2020) Speech-Based Parameter Estimation of an Asymmetric Vocal Fold Oscillation Model and its Application in Discriminating Vocal Fold Pathologies. ICASSP 2020–2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Barcelona, Spain.
[21]	Kumar A, Gupta PK, Srivastava A (2020) A review of modern technologies for tackling COVID-19 pandemic. Diabetes Metab Syndr 14: 569-573. doi: 10.1016/j.dsx.2020.05.008
[22]	Deshpande G, Schuller B (2020) An Overview on Audio, Signal, Speech, & Language Processing for COVID-19. arXiv:2005.08579 [cs.CY] .
[23]	Brown C, Chauhan J, Grammenos A, et al. (2020) Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining .
[24]	Han J, Qian K, Song M, et al. (2020) An Early Study on Intelligent Analysis of Speech under COVID-19: Severity, Sleep Quality, Fatigue, and Anxiety. arXiv:2005.00096v2 [eess.AS] .
[25]	Orlandic L, Teijeiro T, Atienza D (2020) The COUGHVID crowdsourcing dataset: A corpus for the study of large scale cough analysis algorithms. arXiv:2009.11644v1 [cs.SD] .
[26]	Singh R (2019) Production and Perception of Voice. Profiling Humans from their Voice Singapore: Springer. doi: 10.1007/978-981-13-8403-5
[27]	Hassan A, Shahin I, Alsabek MB (2020) COVID-19 Detection System using Recurrent Neural Networks. 2020 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI) Sharjah, United Arab Emirates.
[28]	Chaudhari G, Jiang X, Fakhry A, et al. (2021) Virufy: Global Applicability of Crowdsourced and Clinical Datasets for AI Detection of COVID-19 from Cough. arXiv. PPR: PPR272849 .
[29]	Ismail MA, Deshmukh S, Rita S (2020) Detection of COVID-19 through the Analysis of Vocal Fold Oscillations. arXiv:2010.10707v1 [eess.AS] .
[30]	Laguarta J, Hueto F, Subirana B (2020) COVID-19 Artificial Intelligence Diagnosis Using Only Cough Recordings. IEEE Open J Eng Med Biol 1: 275-281. doi: 10.1109/OJEMB.2020.3026928
[31]	Wang L, Lin ZQ, Wong A (2020) COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. Sci Rep 10: 19549. doi: 10.1038/s41598-020-76550-z
[32]	Quartieri TF, Talker T, Palmer JS (2020) A Framework for Biomarkers of COVID-19 Based on Coordination of Speech-Production Subsystems. IEEE Open J Eng Med Biol 1: 203-206. doi: 10.1109/OJEMB.2020.2998051
[33]	Sajjad A, Patrick C, Alessandro LK (2019) End-to-end environmental sound classification using a 1D convolutional neural network. Expert Sys Appl 136: 252-263. doi: 10.1016/j.eswa.2019.06.040
[34]	Li Y, Baidoo C, Cai T, et al. (2019) Speech Emotion Recognition Using 1D CNN with No Attention. 2019 23rd International Computer Science and Engineering Conference (ICSEC) Phuket, Thailand.
[35]	Serkan K, Onur A, Osama A, et al. (2019) 1D Convolutional Neural Networks and Applications: A Survey. Mech Sys Signal Proc 151: 107398.
[36]	Kiranyaz S, Ince T, Abdeljaber O, et al. (2019) 1-D Convolutional Neural Networks for Signal Processing Applications. ICASSP 2019–2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Brighton, UK.
[37]	Salamon J, Bello JP (2017) Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification. IEEE Signal Proc Lett 24: 279-283. doi: 10.1109/LSP.2017.2657381
[38]	Aditya K, Deepak G, Nguyen NG, et al. (2019) Sound Classification Using Convolutional Neural Network and Tensor Deep Stacking Network. IEEE Access 7: 7717-7727. doi: 10.1109/ACCESS.2018.2888882
[39]	Chen X, Kopsaftopoulos F, Wu Q, et al. (2019) A Self-Adaptive 1D Convolutional Neural Network for Flight-State Identification. Sensors 19: 275. doi: 10.3390/s19020275
[40]	Pons J, Serra X (2019) Randomly Weighted CNNs for (Music) Audio Classification. ICASSP 2019–2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Brighton, UK.
[41]	Aykanat M, Kılıç O, Kurt B, et al. (2017) Classification of lung sounds using convolutional neural networks. J Image Video Proc 65. doi: 10.1186/s13640-017-0213-2
[42]	Ismael AM, Abdulkadir S (2021) Deep learning approaches for COVID-19 detection based on chest X-ray images. Expert Sys Appl 164: 114054. doi: 10.1016/j.eswa.2020.114054
[43]	Minaee S, Abdolrashidi A, Su H, et al. (2021) Biometrics Recognition Using Deep Learning: A Survey. arXiv:1912.00271 [cs.CV] .
[44]	Yazdani S, Minaee S, Kafieh R, et al. (2020) COVID CT-Net: Predicting Covid-19 from Chest CT Images Using Attentional Convolutional Network. arXiv:2009.05096 [eess.IV] .
[45]	Jain R, Gupta M, Taneja S, et al. (2021) Deep learning-based detection and analysis of COVID-19 on chest X-ray images. Appl Intell 51: 1690-1700. doi: 10.1007/s10489-020-01902-1
[46]	Khan AI, Shah JL, Bhat MM (2020) CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest X-ray images. Comput Methods Programs Biomed 196: 105581. doi: 10.1016/j.cmpb.2020.105581
[47]	Wu Y, Yang F, Liu Y, et al. (2018) A Comparison of 1-D and 2-D Deep Convolutional Neural Networks in ECG Classification. arXiv:1810.07088v1 [cs.CV] .
[48]	Ioffe S, Szegedy C (2015) Batch Normalization: Accelerating the deep network training by reducing internal covariate shift, Proceedings of the 32^nd International Conference on Machine Learning. Proceed Mach Learn Res 37: 448-456.

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