Objective Assessment of Covid-19 Severity Affecting the Vocal and Respiratory System Using a Wearable, Autonomous Sound Collar

被引:0
|
作者
Ishac, D. [1 ]
Matta, S. [1 ]
Bin, S. [2 ]
Aziz, H. [3 ]
Karam, E. [1 ]
Abche, A. [1 ]
Nassar, G. [4 ]
机构
[1] Univ Balamand UOB, Elect Engn Dept, Balamand, El Koura, Lebanon
[2] Univ Qingdao, Coll Phys, Qingdao, Peoples R China
[3] Sahlgrens Univ Hosp, Dept Pulm Pathol, Gothenburg, Sweden
[4] IEMN, CNRS UMR 8520 INSA HdF, Lille Acad, Lille, France
关键词
Autonomous sound systems; Mechanical variables measurement; Electromechanical sensors; Sound signal processing; Covid-19; PIEZOELECTRICITY; ULTRASOUND;
D O I
10.1007/s12195-021-00712-w
中图分类号
Q813 [细胞工程];
学科分类号
摘要
Introduction Since the outbreak began in January 2020, Covid-19 has affected more than 161 million people worldwide and resulted in about 3.3 million deaths. Despite efforts to detect human infection with the virus as early as possible, the confirmatory test still requires the analysis of sputum or blood with estimated results available within approximately 30 minutes; this may potentially be followed by clinical referral if the patient shows signs of aggravated pneumonia. This work aims to implement a soft collar as a sound device dedicated to the objective evaluation of the pathophysiological state resulting from dysphonia of laryngeal origin or respiratory failure of inflammatory origin, in particular caused by Covid-19. Methods In this study, we exploit the vibrations of waves generated by the vocal and respiratory system of 30 people. A biocompatible acoustic sensor embedded in a soft collar around the neck collects these waves. The collar is also equipped with thermal sensors and a cross-data analysis module in both the temporal and frequency domains (STFT). The optimal coupling conditions and the electrical and dimensional characteristics of the sensors were defined based on a mathematical approach using a matrix formalism. Results The characteristics of the signals in the time domain combined with the quantities obtained from the STFT offer multidimensional information and a decision support tool for determining a pathophysiological state representative of the symptoms explored. The device, tested on 30 people, was able to differentiate patients with mild symptoms from those who had developed acute signs of respiratory failure on a severity scale of 1 to 10. Conclusion With the health constraints imposed by the effects of Covid-19, the heavy organization to be implemented resulting from the flow of diagnostics, tests and clinical management, it was urgent to develop innovative and safe biomedical technologies. This passive listening technique will contribute to the non-invasive assessment and dynamic observation of lesions. Moreover, it merits further examination to provide support for medical operators to improve clinical management.
引用
收藏
页码:67 / 86
页数:20
相关论文
共 50 条
  • [41] COVID-19 Respiratory Sound Signal Detection Using HOS-Based Linear Frequency Cepstral Coefficients and Deep Learning
    Sandeep B. Sangle
    Chandrakant J. Gaikwad
    Circuits, Systems, and Signal Processing, 2024, 43 : 331 - 347
  • [42] Fine-Grained Assessment of COVID-19 Severity Based on Clinico-Radiological Data Using Machine Learning
    Liu, Haipeng
    Wang, Jiangtao
    Geng, Yayuan
    Li, Kunwei
    Wu, Han
    Chen, Jian
    Chai, Xiangfei
    Li, Shaolin
    Zheng, Dingchang
    INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2022, 19 (17)
  • [43] Automated Assessment and Tracking of COVID-19 Pulmonary Disease Severity on Chest Radiographs Using Convolutional Siamese Neural Networks
    Li, Matthew D.
    Arun, Nishanth T.
    Gidwani, Mishka
    Chang, Ken
    Deng, Francis
    Little, Brent P.
    Mendoza, Dexter P.
    Lang, Min
    Lee, Susanna, I
    O'Shea, Aileen
    Parakh, Anushri
    Singh, Praveer
    Kalpathy-Cramer, Jayashree
    RADIOLOGY-ARTIFICIAL INTELLIGENCE, 2020, 2 (04) : 1 - 39
  • [44] Automated lung sound analysis using the LungPass platform: a sensitive and specific tool for identifying lower respiratory tract involvement in COVID-19
    Lapteva, Elena A.
    Kharevich, Olga N.
    Khatsko, Victoria V.
    Voronova, Natalia A.
    Chamko, Maksim V.
    Bezruchko, Irina V.
    Katibnikova, Elena I.
    Loban, Elena I.
    Mouawie, Mostafa M.
    Binetskaya, Helena
    Aleshkevich, Sergey
    Karankevich, Aleksey
    Dubinetski, Vitaly
    Vestbo, Jorgen
    Mathioudakis, Alexander G.
    EUROPEAN RESPIRATORY JOURNAL, 2021, 58 (06)
  • [45] Detection of Common Respiratory Infections, Including COVID-19, Using Consumer Wearable Devices in Health Care Workers: Prospective Model Validation Study
    Esmaeilpour, Zeinab
    Natarajan, Aravind
    Su, Hao-Wei
    Faranesh, Anthony
    Friel, Ciaran
    Zanos, Theodoros P.
    D'Angelo, Stefani
    Heneghan, Conor
    JMIR FORMATIVE RESEARCH, 2024, 8
  • [46] Development and Validation of an Early Scoring System for Prediction of Disease Severity in COVID-19 Using Complete Blood Count Parameters
    Rahman, Tawsifur
    Khandakar, Amith
    Hoque, Md Enamul
    Ibtehaz, Nabil
    Bin Kashem, Saad
    Masud, Reehum
    Shampa, Lutfunnahar
    Hasan, Mohammad Mehedi
    Islam, Mohammad Tariqul
    Al-Maadeed, Somaya
    Zughaier, Susu M.
    Badran, Saif
    Doi, Suhail A. R.
    Chowdhury, Muhammad E. H.
    IEEE ACCESS, 2021, 9 : 120422 - 120441
  • [47] Physiological and emotional assessment of college students using wearable and mobile devices during the 2020 COVID-19 lockdown: An intensive, longitudinal dataset
    Labbaf, Sina
    Abbasian, Mahyar
    Nguyen, Brenda
    Lucero, Matthew
    Ahmed, Maryam Sabah
    Yunusova, Asal
    Rivera, Alexander
    Jain, Ramesh
    Borelli, Jessica L.
    Dutt, Nikil
    Rahmani, Amir M.
    DATA IN BRIEF, 2024, 54
  • [48] Exploring factors affecting the unsafe behavior of health care workers' in using respiratory masks during COVID-19 pandemic in Iran: a qualitative study
    Tahernejad, Azadeh
    Sohrabizadeh, Sanaz
    Tahernejad, Somayeh
    BMC HEALTH SERVICES RESEARCH, 2024, 24 (01)
  • [49] RETRACTION: Severity Assessment of COVID-19 Using a CT-Based Radiomics Model (Retraction of Vol 2021, art no 2263469, 2021)
    Xu, Z.
    Zhao, L.
    Yang, G.
    STEM CELLS INTERNATIONAL, 2023, 2023
  • [50] Classification of suspected objects and severity assessment of COVID-19 from chest X-ray images using deep transfer learning
    Verma K.
    Kumar A.
    Swaraj A.
    Sagar A.
    Research on Biomedical Engineering, 2023, 39 (03) : 705 - 718