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Artificial intelligence-based framework to identify the abnormalities in the COVID-19 disease and other common respiratory diseases from digital stethoscope data using deep CNN

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Abstract

The utilization of lung sounds to diagnose lung diseases using respiratory sound features has significantly increased in the past few years. The Digital Stethoscope data has been examined extensively by medical researchers and technical scientists to diagnose the symptoms of respiratory diseases. Artificial intelligence-based approaches are applied in the real universe to distinguish respiratory disease signs from human pulmonary auscultation sounds. The Deep CNN model is implemented with combined multi-feature channels (Modified MFCC, Log Mel, and Soft Mel) to obtain the sound parameters from lung-based Digital Stethoscope data. The model analysis is observed with max-pooling and without max-pool operations using multi-feature channels on respiratory digital stethoscope data. In addition, COVID-19 sound data and enriched data, which are recently acquired data to enhance model performance using a combination of L2 regularization to overcome the risk of overfitting because of less respiratory sound data, are included in the work. The suggested DCNN with Max-Pooling on the improved dataset demonstrates cutting-edge performance employing a multi-feature channels spectrogram. The model has been developed with different convolutional filter sizes (\(1\times 12\), \(1\times 24\), \(1\times 36\), \(1\times 48\), and \(1\times 60\)) that helped to test the proposed neural network. According to the experimental findings, the suggested DCNN architecture with a max-pooling function performs better to identify respiratory disease symptoms than DCNN without max-pooling. In order to demonstrate the model’s effectiveness in categorization, it is trained and tested with the DCNN model that extract several modalities of respiratory sound data.

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Data availability

This work is connected to a repository of data, where you can get the data. We collected COVID-19 large-scale sound (breath, voice, cough) data from reputable repositories and referenced sources in this work instead of using publicly available data [41,42,43,44,45]. Along with this, we have collected ICBHI data (digital stethoscope data) from online sources [40]. The pertinent author may provide the information to scientists and investigators upon proper request.

References

  1. Wang Y, et al. Unobtrusive and automatic classification of multiple people’s abnormal respiratory patterns in real time using deep neural network and depth camera. IEEE Internet Things J. 2020;7(9):8559–71. https://doi.org/10.1109/JIOT.2020.2991456.

    Article  Google Scholar 

  2. Saatci E, Saatci E. Determination of respiratory parameters by means of hurst exponents of the respiratory sounds and stochastic processing methods. IEEE Trans Biomed Eng. 2021;68(12):3582–92. https://doi.org/10.1109/TBME.2021.3079160.

    Article  PubMed  Google Scholar 

  3. World Health Organization. Coronavirus disease 2019 (covid-19). (2021) Available from: https://covid19.who.int/.

  4. Vaishya R, Javaid M, Khan IH, Haleem A. Artificial intelligence (AI) applications for COVID-19 pandemic. Diabetes Metab Syndr Clin Res Rev. 2020;14(4):337–9. https://doi.org/10.1016/j.dsx.2020.04.012.

    Article  Google Scholar 

  5. Unwin HJT, Mishra S, Bradley VC, et al. State-level tracking of COVID-19 in the United States. Nat Commun. 2020;11:6189. https://doi.org/10.1038/s41467-020-19652-6.

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  6. Easwaramoorthy D, Gowrisankar A, Manimaran A, Nandhini S, Rondoni L, Banerjee S. An exploration of fractal-based prognostic model and comparative analysis for second wave of COVID-19 diffusion. Nonlinear Dyn. 2021;8:1–21. https://doi.org/10.1007/s11071-021-06865-7.

    Article  Google Scholar 

  7. Kavitha C, Gowrisankar A, Banerjee S. The second and third waves in India: when will the pandemic be culminated? Eur Phys J Plus. 2021;136(5):596. https://doi.org/10.1140/epjp/s13360-021-01586-7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Gowrisankar A, Rondoni L, Banerjee S. Can India develop herd immunity against COVID-19? Eur Phys J Plus. 2020;135(6):526. https://doi.org/10.1140/epjp/s13360-020-00531-4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Ma Y et al. LungBRN: A Smart digital stethoscope for detecting respiratory disease Using bi-ResNet Deep learning algorithm, 2019 IEEE Biomedical Circuits and Systems Conference (BioCAS), 2019, pp. 1–4, https://doi.org/10.1109/BIOCAS.2019.8919021.

  10. Tong, Xia et al. COVID-19 Sounds: a large-scale audio dataset for digital respiratory screening, 35th Conference on Neural Information Processing Systems (NeurIPS 2021) Track on Datasets and Benchmarks, 2021. https://openreview.net/forum?id=9KArJb4r5ZQ.

  11. Brabenec L, Mekyska J, Galaz Z, et al. Speech disorders in Parkinson’s disease: early diagnostics and effects of medication and brain stimulation. J Neural Transm. 2017;124:303–34. https://doi.org/10.1007/s00702-017-1676-0.

    Article  CAS  PubMed  Google Scholar 

  12. Shi J, Zheng X, Li Y, Zhang Q, Ying S. Multimodal neuroimaging feature learning with multimodal stacked deep polynomial Networks for diagnosis of Alzheimer’s disease. IEEE J Biomed Health Inf. 2018;22(1):173–83. https://doi.org/10.1109/JBHI.2017.2655720.

    Article  Google Scholar 

  13. Robert RD, Pipe AL, Quinlan B, Oda J. Interactive voice response telephony to promote smoking cessation in patients with heart disease: a pilot study. Patient Educ Couns. 2007;66(3):319–26. https://doi.org/10.1016/j.pec.2007.01.005.

    Article  Google Scholar 

  14. Liu Y, Whitfield C, Zhang T, et al. Monitoring COVID-19 pandemic through the lens of social media using natural language processing and machine learning. Health Inf Sci Syst. 2021;9:25. https://doi.org/10.1007/s13755-021-00158-4.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Campagner A, Carobene A, Cabitza F. External validation of machine learning models for COVID-19 detection based on complete blood count. Health Inf Sci Syst. 2021;9:37. https://doi.org/10.1007/s13755-021-00167-3.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Bezzan VP, Rocco CD. Predicting special care during the COVID-19 pandemic: a machine learning approach. Health Inf Sci Syst. 2021;9:34. https://doi.org/10.1007/s13755-021-00164-6.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Malla SJ, Alphonse PJA. COVID-19 outbreak: an ensemble pre-trained deep learning model for detecting informative tweets. Appl Soft Comput. 2021;107:1568–4946. https://doi.org/10.1016/j.asoc.2021.107495.

    Article  Google Scholar 

  18. Pham TD. Classification of COVID-19 chest X-rays with deep learning: new models or fine tuning? Health Inf Sci Syst. 2021;9:2. https://doi.org/10.1007/s13755-020-00135-3.

    Article  PubMed  Google Scholar 

  19. Jagadeesh MS, Alphonse PJA. NIT_COVID-19 at WNUT-2020 Task 2: Deep learning model RoBERTa for identify informative COVID-19 english tweets, Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020), pages 450–454, Online, 2020. https://doi.org/10.18653/v1/2020.wnut-1.66.

  20. Andreu-Perez J, 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 Trans Serv Comput. 2021. https://doi.org/10.1109/TSC.2021.3061402.

    Article  PubMed  Google Scholar 

  21. Islam R, Tarique M, Abdel-Raheem E. A survey on signal processing based pathological voice detection techniques. IEEE Access. 2020;8:66749–76.

    Article  Google Scholar 

  22. Al Ismail M, Deshmukh S, Singh R. Detection of Covid-19 Through the analysis of vocal fold oscillations, ICASSP. 2021–2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021, pp. 1035–1039, https://doi.org/10.1109/ICASSP39728.2021.9414201.

  23. Lella KK, Alphonse PJA. A literature review on COVID-19 disease diagnosis from respiratory sound data. AIMS Bioeng. 2021;8(2):140–53. https://doi.org/10.3934/bioeng.2021013. https://www.aimspress.com/article/doi/.

    Article  CAS  Google Scholar 

  24. Nikolaou V, Massaro S, Fakhimi M, et al. COVID-19 diagnosis from chest x-rays: developing a simple, fast, and accurate neural network. Health Inf Sci Syst. 2021;9:36. https://doi.org/10.1007/s13755-021-00166-4.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Rahman T, Akinbi A, Chowdhury MEH, et al. COV-ECGNET: COVID-19 detection using ECG trace images with deep convolutional neural network. Health Inf Sci Syst. 2022;10:1. https://doi.org/10.1007/s13755-021-00169-1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Chowdhury NK, Rahman MM, Kabir MA. PDCOVIDNet: a parallel-dilated convolutional neural network architecture for detecting COVID-19 from chest X-ray images. Health Inf Sci Syst. 2020;8:27. https://doi.org/10.1007/s13755-020-00119-3.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Lella KK, Pja A, Automatic. COVID-19 disease diagnosis using 1D convolutional neural network and augmentation with human respiratory sound based on parameters: cough, breath, and voice. AIMS Public Health. 2021;8(2):240–64. https://doi.org/10.3934/publichealth.2021019.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Zohaib Mushtaq S-F, Su. Environmental sound classification using a regularized deep convolutional neural network with data augmentation. Appl Acoustics. 2020;167:0003–682. https://doi.org/10.1016/j.apacoust.2020.107389.

    Article  Google Scholar 

  29. Malla SJ, Lella KK, Alphonse PJA. Novel fuzzy deep learning approach for automated detection of useful COVID-19 tweets. Artifntell Med. 2023. https://doi.org/10.1016/j.artmed.2023.102627.

    Article  Google Scholar 

  30. Lella KK, Pja A. Automatic diagnosis of COVID-19 disease using deep convolutional neural network with multi-feature channel from respiratory sound data: cough, voice, and breath. Alexandria Eng J. 2022 https://doi.org/10.1016/j.aej.2021.06.024.

    Article  Google Scholar 

  31. Tan W, Liu P, Li X, et al. Classification of COVID-19 pneumonia from chest CT images based on reconstructed super-resolution images and VGG neural network. Health Inf Sci Syst. 2021;9:10. https://doi.org/10.1007/s13755-021-00140-0.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Verónica Abreu A, JoséA Oliveira, Duarte. Alda Marques,. Computerized respiratory sounds in paediatrics: a systematic review. Respiratory Med. 2021. https://doi.org/10.1016/j.yrmex.2021.100027.

    Article  Google Scholar 

  33. Kranthi Kumar L, Alphonse P. COVID-19 disease diagnosis with light-weight CNN using modified MFCC and enhanced GFCC from human respiratory sounds. Eur Phys J Spec Top. 2022. https://doi.org/10.1140/epjs/s11734-022-00432-w.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Khan SM, Qaiser N, Shaikh SF, Hussain MM. Design analysis and human tests of foil-based wheezing monitoring system for asthma detection. IEEE Trans Electron Devices. 2020;67(1):249–57. https://doi.org/10.1109/TED.2019.2951580.

    Article  ADS  CAS  Google Scholar 

  35. Guo C, Lin S, Huang Z, et al. Analysis of sentiment changes in online messages of depression patients before and during the COVID-19 epidemic based on BERT + BiLSTM. Health Inf Sci Syst. 2022;10:15. https://doi.org/10.1007/s13755-022-00184-w.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Chemaitelly H, Yassine HM, Benslimane FM, et al. mRNA-1273 COVID-19 vaccine effectiveness against the B.1.1.7 and B.1.351 variants and severe COVID-19 disease in Qatar. Nat Med. 2021;27:1614–21. https://doi.org/10.1038/s41591-021-01446-y.

    Article  CAS  PubMed  Google Scholar 

  37. Mardani R, et al. Laboratory parameters in detection of COVID-19 patients with poSITIVe RT-PCR; a diagnostic accuracy study. Archives Acad Emergency Med. 2020;8(1):e43.

    MathSciNet  Google Scholar 

  38. Tahamtan A, Ardebili. A. Real-time RT-PCR in COVID-19 detection: issues affecting the results. Expert Rev Mol Diagn. 2020;20(5):453–4. https://doi.org/10.1080/14737159.2020.1757437.

    Article  CAS  PubMed  Google Scholar 

  39. Puck B, van Kasteren B, van der Veer R, Molenkamp, Chantal BEM, Reusken, Meijer A. Comparison of seven commercial RT-PCR diagnostic kits for COVID-19. J Clin Virol. 2020;128:1386–6532. https://doi.org/10.1016/j.jcv.2020.104412.

    Article  CAS  Google Scholar 

  40. Rocha BM, Filos D, Mendes L, Serbes G, Ulukaya S, Kahya YP, Jakovljevic N, Turukalo TL, Vogiatzis IM, Perantoni E, Kaimakamis E, Natsiavas P, Oliveira A, Jácome C, Marques A, Maglaveras N, Pedro Paiva R, Chouvarda I, de Carvalho P. An open access database for the evaluation of respiratory sound classification algorithms. Physiol Meas. 2019;40(3):035001.

    Article  PubMed  Google Scholar 

  41. Brown C, Chauhan J, Grammenos A et al.  Exploring automatic diagnosis of COVID-19 from Crowdsourced respiratory sound data. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2020).

  42. Mesaros A et al. DCASE 2017 challenge setup: tasks datasets and baseline system, Procceedings Detection Classification Acoust. Scenes Events Workshop, 2017. https://hal.inria.fr/hal-01627981/.

  43. Chaudhari G, Jiang X, Fakhry A et al. Virufy: global applicability of Crowdsourced and clinical datasets for AI detection of COVID-19 from Cough Audio Samples. https://doi.org/10.48550/arXiv.2011.13320.

  44. Sharma N, Krishnan P, Kumar R, Ramoji S, Chetupalli SR, Ghosh RN, Ganapathy PK. S. (2020) Coswara–a database of breathing, cough, and Voice Sounds for COVID-19 Diagnosis. Proceedings Interspeech 2020, pp. 4811–4815, https://doi.org/10.21437/Interspeech.2020-2768.

  45. Orlandic L, Teijeiro T, Atienza D. The COUGHVID crowdsourcing dataset, a corpus for the study of large-scale cough analysis algorithms. Sci Data. 2021;8(1):156. https://doi.org/10.1038/s41597-021-00937-4.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Jayalakshmy S, Sudha GF. Conditional GAN based augmentation for predictive modeling of respiratory signals. Comput Biol Med. 2021. https://doi.org/10.1016/j.compbiomed.2021.104930.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Van Rossum G, Drake FL. Python 3 reference Manual. Scotts Valley: CreateSpace; 2009.

    Google Scholar 

  48. Anon. Anaconda Software Distribution, Anaconda Inc. 2020 Available from https://docs.anaconda.com/.

  49. McFee B et al. Librosa: audio and music signal analysis in python. In Proceedings of the 14th python in science conference. 2015

  50. Chollet F. & others, 2015. Keras. Available from https://github.com/fchollet/keras.

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Acknowledgements

We want to express our gratitude to everyone who is working to control the SARS-CoV-2 outbreak.

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Authors and Affiliations

  1. School of Computer Science and Engineering, VIT-AP University, Vijayawada, Guntur, Andhra Pradesh, 522237, India

    Kranthi Kumar Lella & M. S. Jagadeesh

  2. Department of Computer Applications, NIT Tiruchirappalli, Tiruchirappalli, Guntur, Tamil Nadu, 620015, India

    P. J. A. Alphonse

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Lella, K.K., Jagadeesh, M.S. & Alphonse, P.J.A. Artificial intelligence-based framework to identify the abnormalities in the COVID-19 disease and other common respiratory diseases from digital stethoscope data using deep CNN. Health Inf Sci Syst 12, 22 (2024). https://doi.org/10.1007/s13755-024-00283-w

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