Detection of COVID-19 in smartphone-based breathing recordings: A pre-screening deep learning tool

  1. Mohanad Alkhodari
  2. Ahsan H. Khandoker

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Abstract

This study was sought to investigate the feasibility of using smartphone-based breathing sounds within a deep learning framework to discriminate between COVID-19, including asymptomatic, and healthy subjects. A total of 480 breathing sounds (240 shallow and 240 deep) were obtained from a publicly available database named Coswara. These sounds were recorded by 120 COVID-19 and 120 healthy subjects via a smartphone microphone through a website application. A deep learning framework was proposed herein that relies on hand-crafted features extracted from the original recordings and from the mel-frequency cepstral coefficients (MFCC) as well as deep-activated features learned by a combination of convolutional neural network and bi-directional long short-term memory units (CNN-BiLSTM). The statistical analysis of patient profiles has shown a significant difference (p-value: 0.041) for ischemic heart disease between COVID-19 and healthy subjects. The Analysis of the normal distribution of the combined MFCC values showed that COVID-19 subjects tended to have a distribution that is skewed more towards the right side of the zero mean (shallow: 0.59±1.74, deep: 0.65±4.35, p-value: <0.001). In addition, the proposed deep learning approach had an overall discrimination accuracy of 94.58% and 92.08% using shallow and deep recordings, respectively. Furthermore, it detected COVID-19 subjects successfully with a maximum sensitivity of 94.21%, specificity of 94.96%, and area under the receiver operating characteristic (AUROC) curves of 0.90. Among the 120 COVID-19 participants, asymptomatic subjects (18 subjects) were successfully detected with 100.00% accuracy using shallow recordings and 88.89% using deep recordings. This study paves the way towards utilizing smartphone-based breathing sounds for the purpose of COVID-19 detection. The observations found in this study were promising to suggest deep learning and smartphone-based breathing sounds as an effective pre-screening tool for COVID-19 alongside the current reverse-transcription polymerase chain reaction (RT-PCR) assay. It can be considered as an early, rapid, easily distributed, time-efficient, and almost no-cost diagnosis technique complying with social distancing restrictions during COVID-19 pandemic.

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  1. Version published to 10.1371/journal.pone.0262448
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    Therefore, the integration of biological sounds, as in breathing recordings, within a deep learning framework overcomes the aforementioned limitations, while at the same time provides acceptable levels of performance. The proposed deep learning framework had high levels of accuracy (94.58%) in discriminating between COVID-19 and healthy subjects. The structure of the framework was built to ensure a simple architecture, while at the same time to provide advanced features extraction and learning mechanisms. The combination between hand-crafted features and deep-activated features allowed for maximized performance capabilities within the model, as it learns through hidden and internal attributes as well as deep structural and temporal characteristics of recordings. The high sensitivity and specificity measures (94.21% and 94.96%, respectively) obtained in this study prove the efficiency of deep learning in distinguishing COVID-19 subjects (AUROC: 0.90). Additionally, it supports the field of deep learning research on the use of respiratory signals for COVID-19 diagnostics [21, 56]. Alongside the high performance levels, it was interesting to observe a 100.00% accuracy in predicting asymptomatic COVID-19 subjects. This could enhance the detection of this viral infection at a very early stage and thus, preventing it from developing to mild and moderate conditions or spreading to other people. Furthermore, this high performance levels were achieved through 1D signals instead of 2D ...

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  3. Version published to 10.1101/2021.09.18.21263775 on medRxiv