A study of using cough sounds and deep neural networks for the early detection of Covid-19

Abstract

The current clinical diagnosis of COVID-19 requires person-to-person contact, needs variable time to produce results, and is expensive. It is even inaccessible to the general population in some developing countries due to insufficient healthcare facilities. Hence, a low-cost, quick, and easily accessible solution for COVID-19 diagnosis is vital. This paper presents a study that involves developing an algorithm for automated and noninvasive diagnosis of COVID-19 using cough sound samples and a deep neural network. The cough sounds provide essential information about the behavior of glottis under different respiratory pathological conditions. Hence, the characteristics of cough sounds can identify respiratory diseases like COVID-19. The proposed algorithm consists of three main steps (a) extraction of acoustic features from the cough sound samples, (b) formation of a feature vector, and (c) classification of the cough sound samples using a deep neural network. The output from the proposed system provides a COVID-19 likelihood diagnosis. In this work, we consider three acoustic feature vectors, namely (a) time-domain, (b) frequency-domain, and (c) mixed-domain (i.e., a combination of features in both time-domain and frequency-domain). The performance of the proposed algorithm is evaluated using cough sound samples collected from healthy and COVID-19 patients. The results show that the proposed algorithm automatically detects COVID-19 cough sound samples with an overall accuracy of 89.2%, 97.5%, and 93.8% using time-domain, frequency-domain, and mixed-domain feature vectors, respectively. The proposed algorithm, coupled with its high accuracy, demonstrates that it can be used for quick identification or early screening of COVID-19. We also compare our results with that of some state-of-the-art works.

Explainable COVID-19 detection using fractal dimension and vision transformer with Grad-CAM on cough sounds

“…Table 4 shows a comparative summary of the approaches that were conducted on cough-based COVID-19 diagnosis. As seen in Table 4 , Islam et al [16] and Manshouri [18] used the VIRUFY dataset and obtained 93.8% and 95.86% accuracy scores with their proposed methods. Hamdi et al [17] used the COUGHVID dataset and obtained a 91.13% accuracy score.…”

“…They have reported an area under curve (AUC) of 95%, precision of 100%, and 97 % recall score of 97% using the extra-trees classifier. Islam et al [16] used both time and frequency domain features to discriminate the healthy and COVID-19 cough signals. Authors used zero crossing rate, energy, and entropy features.…”

Classification of Cough Sounds Using Spectrogram Methods and a Parallel-Stream One-Dimensional Deep Convolutional Neural Network

“…Islam et al [29] employed a deep neural network (DNN) to detect COVID-19. They used time-, frequency-, and time-frequency-domain features for COVID-19 detection and obtained an accuracy of 97.5%.…”

A Highly Accurate Dysphonia Detection System Using Linear Discriminant Analysis

“…In general, short-time and long-time methods are applied as two categories for feature extraction from the speech signal. Two widely accepted methods, namely Short Time Energy (STE) and Zeros Crossing Rate (ZCR) [2,3], are used as short-time feature extraction methods. In our research however, we used different long-time parameters, such as Fundamental Frequency (F 0 ), Shimmer (%), Jitter (%), Harmonicto-Noise Ratio (HNR), and MFCC [1,4,5], to evaluate vocal tract health.…”