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A novel infrasound and audible machine-learning approach to the diagnosis of COVID-19

Guy Dori, Noa Bachner-Hinenzon, Nour Kasim, Haitem Zaidani, Sivan Haia Perl, Shlomo Maayan, Amin Shneifi, Yousef Kian, Tuvia Tiosano, Doron Adler, Yochai Adir
ERJ Open Research 2022 8: 00152-2022; DOI: 10.1183/23120541.00152-2022
Guy Dori
1HaEmek Medical Center, Afula, Israel
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Noa Bachner-Hinenzon
2Sanolla, Nesher, Israel
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  • For correspondence: noa@sanolla.com
Nour Kasim
1HaEmek Medical Center, Afula, Israel
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Haitem Zaidani
3Rambam Medical Center, Haifa, Israel
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Sivan Haia Perl
4Shamir Medical Center, Zerifin, Israel
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Shlomo Maayan
5Barzilai Medical Center, Ashkelon, Israel
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Amin Shneifi
6Clalit Health Services, Tel Aviv, Israel
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Yousef Kian
5Barzilai Medical Center, Ashkelon, Israel
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Tuvia Tiosano
1HaEmek Medical Center, Afula, Israel
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Doron Adler
2Sanolla, Nesher, Israel
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  • ORCID record for Doron Adler
Yochai Adir
7Carmel Medical Center, Haifa, Israel
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  • Article
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Figures

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  • FIGURE 1
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    FIGURE 1

    A map of the 14 sites (A–N) used for acquiring the acoustic data.

  • FIGURE 2
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    FIGURE 2

    Optional cloud connectivity for the VoqX. BT: Bluetooth; EMR: electronic medical record.

  • FIGURE 3
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    FIGURE 3

    Flowchart of the pre-processing and machine-learning algorithm. SVM: support vector machine.

  • FIGURE 4
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    FIGURE 4

    Two features (out of 164) that strongly depend on infrasound: Mel-Frequency Cepstrum Coefficient 1 (MFCC1) as a function of magnitude of breathing frequency for COVID-19, normal and non-COVID-19: a) with infrasound and b) no infrasound.

  • FIGURE 5
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    FIGURE 5

    Visual sound signatures of the VoqX recorded from: a) a healthy subject, b) a COVID-19 pneumonia patient and c) a patient with non-COVID-19 pneumonia. The colours represent the intensity of breathing sounds (dB full scale).

  • FIGURE 6
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    FIGURE 6

    Receiver operating characteristic curves after classification with and without infrasound: a) detection of “silent” COVID-19 pneumonia and b) detection of non-COVID-19 pneumonia.

Tables

  • Figures
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  • TABLE 1

    Selected patient characteristics and physical findings by lung auscultation

    SubjectsAge (years)Height (cm)Weight (kg)SmokerGender (F/M)Lung auscultation
    Crackles (%)Wheezes (%)Crackles and wheezes (%)Reduced intensity breathing sound (%)Vesicular breathing sounds (%)
    Healthy14142±19168±1173±215258/831600876
    Non-COVID-19 pneumonia6162±17*166±9#,*81±2038#,*33/2859#,*7#,*3#,*328#,*
    COVID-19 pneumonia16458±12*170±1178±283277/8719*4*0275

    Data are presented as n or mean±sd, unless otherwise stated. F: female; M: male. *: p<0.05 versus healthy; #: p<0.05 versus COVID-19 pneumonia.

    • TABLE 2

      Performance measures from 12 runs of random sets

      Specificity (%)SensitivityNPV (%)PPVAccuracy
      Non-COVID-19 pneumonia (%)COVID-19 pneumonia (%)Non-COVID-19 pneumonia (%)COVID-19 pneumonia (%)
      Test group#
       With IFS93±4**70±797±2**90±4**90±4*94±3**92±1**
       No IFS80±769±1193±485±584±685±385±2
      Cross-validation¶
       With IFS87±2**71±7*93±2**88±3*78±6**89±1**87±2**
       No IFS75±465±885±379±268±580±278±2
      Auscultation by acoustic stethoscope (95% CI)+76 (68–83)72 (59–82)25 (17–32)43 (37–50)71 (62–79)52 (44–66)

      VoqX data are presented as mean±sd. IFS: infrasound; NPV: negative predictive value; PPV: positive predictive value. #: not in the learning process; ¶: optimisation of the learning process; +: all data. *: p<0.05; **: p<0.01 versus no IFS.

      Supplementary Materials

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      • Supplementary Material

        Please note: supplementary material is not edited by the Editorial Office, and is uploaded as it has been supplied by the author.

        Features for machine learning and their p-values for separating the three groups 00152-2022.supplement

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      A novel infrasound and audible machine-learning approach to the diagnosis of COVID-19
      Guy Dori, Noa Bachner-Hinenzon, Nour Kasim, Haitem Zaidani, Sivan Haia Perl, Shlomo Maayan, Amin Shneifi, Yousef Kian, Tuvia Tiosano, Doron Adler, Yochai Adir
      ERJ Open Research Oct 2022, 8 (4) 00152-2022; DOI: 10.1183/23120541.00152-2022

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      A novel infrasound and audible machine-learning approach to the diagnosis of COVID-19
      Guy Dori, Noa Bachner-Hinenzon, Nour Kasim, Haitem Zaidani, Sivan Haia Perl, Shlomo Maayan, Amin Shneifi, Yousef Kian, Tuvia Tiosano, Doron Adler, Yochai Adir
      ERJ Open Research Oct 2022, 8 (4) 00152-2022; DOI: 10.1183/23120541.00152-2022
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