Web-based Non-contact Edge Computing Solution for Suspected COVID-19 Infection Classification Model
Keywords:Edge computing, COVID-19 classification, Non-Contact Bio-sensor, Artificial Intelligence, Machine Learning
The recent outbreak of the COVID-19 coronavirus pandemic has necessitated the development of web-based, non-contact edge analytics solutions. Non-contact sensors serve as the interface between web servers and edge analytics through web engineering technology. The need for an edge device classification model that can identify COVID-19 patients based on early symptoms has become evident. In particular a non-contact implementation of such a classification model is required to efficiently prevent viral infection and minimize cross-infection. In this work, we investigate the use of diverse non-contact biosensors (e.g., remote photoplethysmography, radar, and infrared sensors) for reducing effective physical contact with patients and for measuring their biometric data and vital signs. We further explain a classification method for suspected COVID-19 infection based on the measured vital signs and symptoms. The results of this study can be applied in patient classification by mobile-based edge computing applications. The correlation between symptoms comprising cough, sore throat, fever, headache, myalgia, and arthralgia are analyzed in the model. We implement a machine learning classification model using vital signs for performance evaluation, and propose an ensemble model realized by fine-tuning the high-performing classification models. The proposed ensemble model successfully distinguishes suspected patients with an accuracy, area under curve, and F1 scores of 94.4%, 98.4%, and 94.4%, respectively.
Struyf T., Deeks J. J., Dinnes J., Takwoingi Y., Davenport C., Leeflang M. M. G., Spijker R., Hooft L., Emperador D., Domen J., Horn S. R. A., Van den Bruel A., “Signs and symptoms to determine if a patient presenting in primary care or hospital outpatient settings has COVID-19,” Cochrane Database of Systematic Reviews 2021, Issue 2, 2021.
Wynants L., Van Calster B., Collins G. S., Riley R D., Heinze G., Schuit E, et al. “Prediction models for diagnosis and prognosis of covid-19,” systematic review and critical appraisal BMJ 2020, 2020
BUYYA, Rajkumar; SRIRAMA, Satish Narayana (ed.). Fog and edge computing: principles and paradigms. John Wiley & Sons, 2019.
W. Shi, J. Cao, Q. Zhang, Y. Li and L. Xu, “Edge Computing: Vision and Challenges,” in IEEE Internet of Things Journal, vol. 3, no. 5, pp. 637–646, Oct. 2016, doi: 10.1109/JIOT.2016.2579198.
A. B. Hertzman, “Photoelectric plethysmography of the fingers and toes in man,” Exp. Biol. Med., vol. 37, no. 3, pp. 529–534, 1937.
G. de Haan and V. Jeanne, “Robust pulse rate from chrominance-based rPPG.” IEEE Transactions on Biomedical Engineering, vol. 60, no. 10, pp. 2878–2886, 2013.
Yue, Zijie, et al. “Deep Super-Resolution Network for rPPG Information Recovery and Noncontact Heart Rate Estimation.” IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1–11, 2021.
Jingda Du, Si-Qi Liu, Bochao Zhang, Pong C. Yuen, “Weakly Supervised rPPG Estimation for Respiratory Rate Estimation.” Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2391–2397, 2021.
F. Schrumpf, P. Frenzel, “Assessment of Non-Invasive Blood Pressure Prediction from PPG and rPPG Signals Using Deep Learning.” Sensors vol. 21, no. 18, pp. 3820–3830, 2021.
O. Schlesinger, N. Vigderhouse, D. Eytan and Y. Moshe, “Blood pressure estimation from ppg signals using convolutional neural networks and siamese network.” ICASSP 2020–2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1135–1139, 2020.
Hsu, Y. C., Li, Y. H., Chang, C. C., Harfiya, L. N, “Generalized deep neural network model for cuffless blood pressure estimation with photoplethysmogram signal only.” Sensors, vol. 20, no. 19, 2020.
Wang, W., den Brinker, A. C., De Haan, G., “Discriminative signatures for remote-PPG.” IEEE Transactions on Biomedical Engineering, vol. 67, no. 5, pp. 1462–1473, 2019.
Sugita, N., Yoshizawa, M., Abe, M., Tanaka, A., Homma, N., Yambe, T., “Contactless technique for measuring blood-pressure variability from one region in video plethysmography.” Journal of Medical and Biological Engineering, vol. 39, no. 1, pp. 76–85, 2019.
Takahashi, R., Ogawa-Ochiai, K., Tsumura, N., “Non-contact method of blood pressure estimation using only facial video.” Artificial Life and Robotics, vol. 25, no. 3, pp. 343–350, 2020.
Lefloch, JM Wang, A Deep Convolutional Neural Network to Limit Virus Spread Using Facial Mask Segmentation, Journal of Web Engineering, vol. 20. Iss 4, 1177–1188, 2021. https://doi.org/10.13052/jwe1540-9589.20414.
SR Pandey, D Hicks, A Goyal, D Gaurav, SM Tiwari, Mobile Notification System for Blood Pressure and Heartbeat Anomaly Detection, Journal of Web Engineering, Vol. 19. Iss 5–6, 2020. https://doi.org/10.13052/jwe1540-9589.19568.
K. F. Wu and Y. T. Zhang, “Contactless and continuous monitoring of heart electric activities through clothes on a sleeping bed,” in 2008 International Conference on Technology and Applications in Biomedicine, pp. 282–285, 2008.
SM Tiwari, S Jain, A Abraham, S Shandilay, Secure Semantic Smart HealthCare (S3HC), Journal of Web Engineering, vol 17. Iss 8, 617–646, 2018. https://doi.org/10.13052/jwe1540-9589.1782.
Cho, Hui-Sup, and Young-Jin Park. “Detection of heart rate through a wall using UWB impulse radar.” Journal of Healthcare Engineering, 2018.
Al-Masri, Eyhab, Misba Momin., “Detecting heart rate variability using millimeter-wave radar technology.” 2018 IEEE International Conference on Big Data (Big Data), pp. 5282–5284, 2018.
Will, Christoph, et al. “Local pulse wave detection using continuous wave radar systems.” IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology, vol. 1, no. 2, pp. 81–89, 2017.
J. Liu, K. Zhang, W. He, J. Ma, L. Peng and T. Zheng, “Non-contact Human Fatigue Assessment System Based on Millimeter Wave Radar,” 2021 IEEE 4th International Conference on Electronics Technology (ICET), pp. 173–177, 2021.
M. Jung, M. Caris and S. Stanko, “Non-contact Blood Pressure Estimation Using a 300 GHz Continuous Wave Radar and Machine Learning Models,” 2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA), pp. 1–6, 2021.
Min Jeong Lee, Yoo Mi Kim, “Masked Face Temperature Measurement System Using Deep Learnin”, Journal of Korea Multimedia Society, Vol. 24, No. 2, pp. 208–214, 2021.
S. S. Farfade, M. J. Saberian and L. J. Li, “Multi-View Face Detection Using Deep Convolutional Neural Networks,” ACM International Conference on Multimedia Retrieval (ICMR), pp. 643–650, 2015.
Diagnosis Guideline of Severe COVID-19 Patients, Korean Society of Critical Care Medicine (KSCCM), (v.1.1), March, 2020.
Gary B. Smith, David R. Prytherch, Paul Meredith, Paul E. Schmidt, Peter I. Featherstone, “The ability of the National Early Warning Score (NEWS) to discriminate patients at risk of early cardiac arrest, unanticipated intensive care unit admission, and death” Resuscitation, Vol. 84, Issue 4, pp. 465–470, 2013.
Huang, Chaolin, et al. “Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China.” The lancet, vol. 39, no. 10223, pp. 497–596, 2020.
Grassin-Delyle, Stanislas, et al. “Metabolomics of exhaled breath in critically ill COVID-19 patients: A pilot study.” EBioMedicine, vol. 63, 2021.
Carruthers S, McCall B, Cordell B, Wu R., “Relationships between HR and PR interval during physiological and pharmacological interventions.”, Br J Clin Pharmacol, vol. 23, no. 3, pp. 259–265, 1987.
Pavri, B. B., Kloo, J., Farzad, D., Riley, J. M., “Behavior of the PR interval with increasing heart rate in patients with COVID-19. Heart rhythm”, vol. 17, no. 9, pp. 1434–1438, 2020.
Karjalainen, Jouko; Viitasalo, Matti. Fever and cardiac rhythm. Archives of internal medicine, 146.6: 1169–1171, 1986.
Géron, Aurélien. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. O’Reilly Media, Inc.,” 2022.
Rish, Irina, et al. An empirical study of the naive Bayes classifier. In: IJCAI 2001 workshop on empirical methods in artificial intelligence. 2001. p. 41–46.
Cover, Thomas; Hart, Peter. Nearest neighbor pattern classification. IEEE transactions on information theory, 1967, 13.1: 21–27.
Friedman, Jerome H. Greedy function approximation: a gradient boosting machine. Annals of statistics, 2001, 1189–1232.
Iwendi, C., Bashir, A.K., Peshkar, A., Sujatha, R., Chatterjee, J.M., Pasupuleti, S., Mishra, R., Pillai, S., Jo, O, “COVID-19 patient health prediction using boosted random forest algorithm”, Front. Public Health 8, 2020.
Yanamala, N., Krishna, N.H., Hathaway, Q.A. et al., “A vital sign-based prediction algorithm for differentiating COVID-19 versus seasonal influenza in hospitalized patients,” NPJ digital medicine, vol. 4, no. 1, pp. 1–10, 2021.
Dalianis, Hercules. “Evaluation metrics and evaluation.” Clinical Text Mining. Springer, Cham, pp. 45–53, 2018.
S. M. Vieira, U. Kaymak and J. M. C. Sousa, “Cohen’s kappa coefficient as a performance measure for feature selection,” International Conference on Fuzzy Systems, Barcelona, Spain, pp. 1–8, 2010. doi: 10.1109/FUZZY.2010.5584447.
Matthews, B. W. “Comparison of the predicted and observed secondary structure of T4 phage lysozyme”. Biochimica et Biophysica Acta (BBA) – Protein Structure. 405(2): 442–451. 1975. doi: 10.1016/0005-2795(75)90109-9. PMID 1180967.
Jingxiu Yao and Martin Shepperd. Assessing software defection prediction performance: why using the Matthews correlation coefficient matters. In Proceedings of the Evaluation and Assessment in Software Engineering (EASE ’20). Association for Computing Machinery, New York, NY, USA, 120–129. 2020. https://doi.org/10.1145/3383219.3383232.
Soui, M., Mansouri, N., Alhamad, R. et al. NSGA-II as feature selection technique and AdaBoost classifier for COVID-19 prediction using patient’s symptoms. Nonlinear Dyn 106, 1453–1475 (2021). https://doi.org/10.1007/s11071-021-06504-1.