Robust Detection of COVID-19 in Cough Sounds

Abstract

, otherwise known as the coronavirus, has precipitated the world into a pandemic that has infected, as of the time of writing, more than 10 million persons worldwide and caused the death of more than 500,000 persons. Early symptoms of the virus include trouble breathing, fever and fatigue and over 60% of people experience a dry cough. Due to the devastating impact of COVID-19 and the tragic loss of lives, it is of the utmost urgency to develop methods for the early detection of the disease that may help limit its spread as well as aid in the development of targeted solutions. Coughs and other vocal sounds contain pulmonary health information that can be used for diagnostic purposes, and recent studies in chaotic dynamics have shown that nonlinear phenomena exist in vocal signals. The present work investigates the use of symbolic recurrence quantification measures with MFCC features for the automatic detection of COVID-19 in cough sounds of healthy and sick individuals. Our performance evaluation reveals that our symbolic dynamics measures capture the complex dynamics in the vocal sounds and are highly effective at discriminating sick and healthy coughs. We apply our method to sustained vowel 'ah' recordings, and show that our model is robust for the detection of the disease in sustained vowel utterances as well. Furthermore, we introduce a robust novel method of informative undersampling using information rate to deal with the imbalance in our dataset, due to the unavailability of an equal number of sick and healthy recordings. The proposed model achieves a mean classification performance of 97% and 99%, and a mean F 1 -score of 91% and 89% after optimization, for coughs and sustained vowels, respectively.

Detection of COVID-19 from voice, cough and breathing patterns: Dataset and preliminary results

“…Several studies have already explored the usability of voice, cough and breathing for detection and screening of COVID-19 [ [25] , [26] , [27] , [28] , [29] , [30] , [31] ]. Crowdsourced dataset of cough and breathing samples is collected and used for distinguishing between individuals tested positive and negative to COVID-19, as well as participants diagnosed with asthma [ 25 ].…”

COVID-19 detection with traditional and deep features on cough acoustic signals

“…(CV, cross validation, Acc, accuracy, Spe, specificity, Rec, recall, F1, F1-score). Authors Methods Number of Data Performance(%) Pahar et al (2020) [ 43 ] MFCCs, log energy, zero-crossing rate 1171 Rec=93.0 Spe=95.0 and kurtosis/ResNet50 Acc=98.0 Laguarta et al (2020) [ 18 ] MFCCs/3 parallel ResNet50 5320 Rec=98.5 Spe=94.2 Sharma et al (2020) [ 20 ] MFCCs, and other spectral measurements/ 941 Acc=67.7 random forest Mouawad et al (2020) [ 21 ] MFCCs and RQA/XGBoost 1927 Acc=97.0 F1=62.0 Pal ve Sankarasubbu (2020) [ 22 ] MFCCs, time domain and, 150 Rec=96.9 Spe=96.8 non-linear measurements/TabNet network Acc=97.0 F1=97.3 Imran et al (2020) [ 23 ] MFCC and PCA/SVM 543 Rec=96.0 Spe=95.2 Acc=95.6 F1=95.6 This study IMF and DWT based features+ 1187 Rec=99.5 Spe=97.3 ReliefF feature selection/SVM Acc=98.4 F1=98.6 This study ResNet50 based deep features+ 1187 Rec=98.5 Spe=97.3 ReliefF feature selection/SVM …”

“…They reported that they achieved high performance with five different classification algorithms. Their performance was calculated to have an accuracy value of 97.0% and F1-score value of 62.0% [ 21 ]. Pal and Sankarasubbu proposed a study based on four classes of cough signals: COVID-19, asthma, bronchitis, and healthy.…”

Do you have COVID-19? An artificial intelligence-based screening tool for COVID-19 using acoustic parameters

“…A study conducted by Imran et al (2020) used a convolutional neural network (CNN) to introduce a new application to perform direct COVID-19 diagnostics based on cough sounds. Moreover, Mouawad et al (2021) showed the robustness of mel-frequency cepstral coefficient (MFCC) features for automatic detection of COVID-19 through cough and sustained vowel, respectively. A small portion of studies on COVID-19 have focused on machine learning (ML)-based voice quality, a topic that has recently come under the scrutiny of researchers.…”