EFFICIENT COVID-19 DISEASE DIAGNOSIS BASED ON COUGH SIGNAL PROCESSING AND SUPERVISED MACHINE LEARNING

The spread of the coronavirus has claimed the lives of millions worldwide, which led to the emergence of an economic and health crisis at the global level, which prompted many researchers to submit proposals for early diagnosis of the coronavirus to limit its spread. In this work, we propose an automated system to detect COVID-19 based on the cough as one of the most important infection indicators. Several studies have shown that coughing accounts for 65% of the total symptoms of infection. The proposed system is mainly based on three main steps: first, cough signal detection and segmentation;second, cough signal extraction;and third, three techniques of supervised machine learning-based classification: Support Vector Machine (SVM), K-Nearest Neighbours (KNN), and Decision Tree (DT). Our proposed system showed high performance through good accuracy values, where the best accuracy for classifying female coughs was 99.6% using KNN and 88% for males using SVM. © 2022 by the Authors.

COVID Detection Using Cough Sound

Due to the impact of COVID-19 on its emerging strains, there is now a greater need for quick identification and containment to prevent further superfluous cases. We aim to make a machine learning model which can distinguish an audio file/signal and categorize it as COVID likely or unlikely and identify the virus' infection by analyzing the user's cough sounds. By usage of real-time detection and a precisely trained ML model with verified data, the user can further assess their infection in conjunction with other available tools, which would instruct him/her to either seek medical attention or provide reassurance for a negative or false positive diagnosis provided by the other tools. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

Detection of COVID-19 by Cough Sound: A Method Based on DSC + BiLSTM

In recent years, the outbreak of COVID-19 has brought a new round of challenges to global health care, and daily large-scale testing has also increased the consumption of medical resources. However, studies have shown that the cough sounds of patients with COVID-19 are significantly different from other Characteristics of respiratory infectious diseases. Therefore, this paper considers the use of the patient's cough as a detection sample to give the preliminary screening results. The research was conducted on the COUGHVID dataset. The experiment is divided into two stages: (1) Preprocessing stage: use Pitch Shift and Time Stretch to perform data enhancement on audio data, and use spec Augment to perform data enhancement on mel spectrogram. (2) Model construction stage: use two layers of DSC and one layer of BILSTM to splicing to obtain a classification model. Finally, the method is compared with the baseline method using only two layers of LSTM. The results show that accuracy has increased by 1.9%, F1 has increased by 1.9%, and AUC has increased by 1.6%. © 2022 IEEE.

Early Detection of Covid Using Spectral Analysis of Cough and Deep Convolutional Neural Network

Now-a-days, there are numerous techniques and ICT tools for the detection of Covid-19. But, these techniques are working with the help;of culminated or peak of symptoms. However, there is a demanding need for the early detection of Covid with self-reported symptoms or even without any symptoms, which makes it easier for further diagnosis or treatment. This research paper proposes a novel approach for the early detection of Covid with the spectral analysis of Cough sound using discrete wavelet transform (DWT), followed by deep convolution neural network (DCNN) based classification. The proposed method with the cough spectral analysis and Deep Learning based algorithm returns the covid infection probability. The empirical results show that the proposed method of covid detection using cough spectral analysis using DWT and deep learning achieves better accuracy, while compared to the conventional methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Cough-based COVID-19 Detection with Multi-head Deep Neural Network

Clinicians have long used audio signals created by the human body as indications to diagnose sickness or track disease progression. Preliminary research indicates promise in detecting COVID-19 from voice and coughing acoustic signals. In this paper, various popular convolutional neural networks (CNN) are employed to detect COVID-19 from cough sounds available in the Coughvid opensource dataset. The CNN models are given input in the form of hand-crafted features or raw signals represented using spectrograms. The CNN architectures for both the types of inputs has been optimized to enhance performance. COVID-19 could be detected from cough sounds with an accuracy of 77.5% using CNN on handcrafted features, and 72.5% using VGG16 on spectrograms. However, result show that the concatenation of the two in a multi-head deep neural network yield higher accuracy as compared to just using hand-extracted features or spectrograms of raw signals as input. The classification improved to 81.25% when ResNet50 was employed in the multi-head deep neural network, which was higher than that obtained with VGG16 and MobileNet. © 2022 IEEE.

Spectral analysis of cough sounds of patients with COVID-19.

Cough is a frequent manifestation of COVID-19 (COronaVIrus Disease 2019), therefore, it has an important diagnostic value. There is little information about the characteristics of cough of COVID-19 patients in the literature. To perform a spectral analysis of cough sounds in COVID-19 patients in comparison with induced cough of healthy individuals. Methods. The main group consisted of 218 COVID-19 patients (48.56% - men, 51.44% - women, average age 40.2 (32.4;50.1) years). The comparison group consisted of 60 healthy individuals (50.0% men, 50.0% women, average age 41.7 (31.2;53.0) years) who were induced to cough. Each subject had a cough sound recorded, followed by digital processing using a fast Fourier transform algorithm. The temporal-frequency parameters of cough sounds were evaluated: duration (ms), the ratio of the energy of low and medium frequencies (60 - 600 Hz) to the energy of high frequencies (600 - 6 000 Hz), the frequency of the maximum sound energy (Hz). These parameters were determined in relation to both the entire cough and individual phases of the cough sound. Results. Significant differences were found between some cough parameters in the main group and in the comparison group. The total duration of the coughing act was significantly shorter in patients with COVID-19, in contrast to the induced cough of healthy individuals (T = 342.5 (277.0;394.0) - in the main group;T (c) = 400.5 (359.0;457.0) - in the comparison group;p = 0.0000). In addition, it was found that the cough sounds of COVID-19 patients are dominated by the energy of higher frequencies as compared to the healthy controls (Q = 0.3095 (0.223;0.454) - in the main group;Q (c) = 0.4535 (0.3725;0.619) - in the comparison group;p = 0.0000). The maximum frequency of cough sound energy in the main group was significantly higher than in the comparison group (Fmax = 463.0 (274.0;761.0) - in the main group;Fmax = 347 (253.0;488.0) - in the comparison group;p = 0.0013). At the same time, there were no differences between the frequencies of the maximum energy of cough sound of the individual phases of cough act and the duration of the first phase. Conclusion. The cough of patients with COVID-19 is characterized by a shorter duration and a predominance of high-frequency energy compared to the induced cough of healthy individuals.Copyright © 2022 Budnevsky A.V. et al.

COVID-19 Detection Using Forced Cough Sounds and Medical Information

The World Health Organization (WHO) has declared the novel coronavirus (COVID-19) outbreak a global pandemic in March 2020. Through a lot of cooperation and the effort of scientists, several vaccines have been created. However, there is no guarantee that the virus will shortly disappear, even if a large part of the population is vaccinated. Therefore, non-invasive methods, with low cost and real-time results, are important to detect infected individuals and enable earlier adequate treatment, in addition to preventing the spread of the virus. An alternative is using forced cough sounds and medical information to distinguish a healthy person from those infected with COVID-19 via artificial intelligence. An additional challenge is the unbalancing of these data, as there are more samples of healthy individuals than contaminated ones. We propose here a Deep Neural Network model to classify people as healthy or sick concerning COVID-19. We used here a model composed by an Convolutional Neural Network and two other Neural Networks with two full-connected layers, each one trained with different data from the same individual. To evaluate the performance of the proposed method, we combined two datasets from the literature: COUGHVID and Coswara. That dataset contains clinical information regarding previous respiratory conditions, symptoms (fever or muscle pain), and a cough record. The results show that our model is simpler (with fewer parameters) than those from the literature and generalizes better the prediction of infected individuals. The proposal presents an average Area Under the ROC Curve (AUC) equal to 0.885 with a confidence interval (0.881-0.888), while the literature reports 0.771 with (0.752-0.783). © 2023, Federal University of Rio Grande do Sul, Institute of Informatics. All rights reserved.

Early Detection of Covid-19 Through Cough Sound Recognition using LPC and K-NN algorithm

COVID-19 (Coronavirus Disease 2019) is an infectious disease caused by the SARS-CoV-2 virus. This disease has spread worldwide since the beginning of 2020. Patients with this highly contagious disease generally experience only mild to moderate respiratory problems such as sore throat, cough, runny nose, fever, shortness of breath, and fatigue. However, some will become seriously ill and may cause severe respiratory distress or in severe cases multiple organ failure. Therefore, early identification of COVID-19 patients is very important. In this study, a disease detection system was created using an open dataset from COUGHVID which were contained the coughing sound of the Covid-19 disease. The implementation of the cough voice recognition system uses the K-Nearest Neighbor (K-NN) machine learning method and the Linear Predictive Coding (LPC) as method of extracting features from voice. The system was built using the Raspberry Pi 3 b+ microcontroller with microphone voice input and connected to a 3.5-inch LCD touchscreen display as the interface of the system device. The test uses a coughing sound as input through a microphone and processed by LPC feature extraction. At each running process, about 399 MB of memory is used from a total of 1 GB of memory. Meanwhile, the prediction of coughing sounds with the K-NN classification algorithm using 5 neighbors produces accuracy of 62% to predict disease. © 2022 ACM.

Cough Audio Signal-Based Clinical Emergency Classification of Corona Variant Infected Patients Using Multiclass SVM

The variants of coronavirus both delta and omicron are much more contagious and affecting greater percentage of human population. In this research, an attempt is made to predict classification of clinical emergency treatment of corona variant infected patients using their recorded cough sound file. Cough audio signal features such as zero crossing and mel-frequency cepstral coefficients (MFCC), chromo gram (chroma_stft), spectral centroid, spectral roll off, spectral-bandwidth are to be extracted and stored along with patient ID, date, and timings. Digital signal processing of recorded cough audio file obtained needs to be cleaned and pre-processed and normalized to get a training dataset in order to build intelligent ML model using multiclass classifier SVM for predicting the class labels with maximum accuracy. The model proposed in this research paper helps to systematically plan and handle emergency treatment of the patients by classifying their severity based on the cough audio signal using SVM. The built model predicts and classifies the emergency treatment level as low, medium, and high with 96% accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Covid-19 detection using Cough Sound with Neural Networks

COVID-19 has already had a significant influence on our everyday lives and with the influx of patients infected with the newer emerging variants there arises a need for a quick, accurate, and remote mode of identification. Cough sounds can play a vital role in the identification of COVID-19 in individuals. They can be used as an important factor to determine if the person is infected by COVID-19 or not, even with the prior existence of a respiratory ailment. Hence, we focused on providing a widely accessible and scalable solution through the method of a real-time mode of detection of the 'COVID cough' via a machine learning model trained 'COVID cough' recorded dataset. Based on the input, the person is provided with the diagnosis after being assessed by the model. © 2022 IEEE.

A Machine Learning framework for Covid Detection Using Cough Sounds

The present COVID-19 diagnosis necessitates direct patient interaction, involves variable duration to get outcomes, and is costly. In certain poor nations, this is even unreachable to the populace at large, leading to a shortage of medical care. Therefore, a moderate, rapid, but also readily available method for the diagnosis of COVID-19 is essential. Several initiatives have been made to use smartphone-collected sounds and coughs to build machine learning algorithms that can categories and discriminate COVID-19 sounds with healthy tissue. The majority of prior studies used sounds like breathing or coughing to train their analyzers as well as get impressive outcomes. In order to carry out this significant investigation, we used this Coswara dataset, which contains recordings of nine distinct sound varieties of the COVID-19 state of cough, breathing, and speech. COVID-19 could be diagnosed more accurately using trained models on a variety of audio instead of a specific model trained on cough alone. This work examines the potential prospect of using machine learning techniques to enhance the identification of COVID-19 in such an initial and non-invasive manner through the monitoring of audio sounds. The XGBoost outperforms existing benchmark classification algorithms and achieves 92% accuracy with all sounds. Vowel/e/sound random forest with 98.36% was determined to be among the most effective, and the vowel/e/can also evaluated for the purpose of detecting compared to the other vowels;the impact of COVID-19 on sound quality is more precise. © 2022 IEEE.

Use of Voluntary Cough Sounds and Deep Learning for Pulmonary Disease Screening in Low-Resource Areas

In low-resource areas, pulmonary diseases are often misdiagnosed or underdiagnosed due to a lack of trained clinical staff and diagnostic lab equipment (e.g. spirometry, DLCO). In these settings, traditional methods of pulmonary disease screening often include a lengthy questionnaire (>30 questions) and stethoscope auscultation. Unfortunately, such tools are not appropriate for general practitioner (GP) doctors or community health workers who have little time or experience diagnosing pulmonary disease. We propose a computer-based deep learning algorithm that could enable rapid screening of the most common pulmonary diseases (COPD, Asthma, and respiratory infection (COVID-19)) using voluntary cough sounds alone. Using a dataset of 348 cough recordings, raw cough recordings were segmented into individual coughs and converted to Mel Spectrogram images. We trained two types of models for comparison, binary and multi-class, using transfer learning with VGG19. The resulting Receiver Operating Characteristic (ROC) curves and the Area Under Curve (AUC) accuracy for each model was calculated to evaluate performance. Binary AUC accuracies were 0.73, 0.70, 0.87, and 0.70 for healthy, asthma, COPD, and COVID-19 respectively, while multi-class AUC accuracies were 0.78, 0.67, 0.95, 0.70. This demonstrates good potential for creating a simple low-cost screening tool that is fast to administer. Future versions of the model will use ongoing data collection to expand to more diseases including tuberculosis and pneumonia. © 2022 IEEE.

CoviFL: Edge-Assisted Federated Learning for Remote COVID-19 Detection in an AIoMT Framework

Detection of COVID-19 has been a global challenge due to the lack of proper resources across all regions. Recently, research has been conducted for non-invasive testing of COVID-19 using an individual's cough audio as input to deep learning models. However, these methods do not pay sufficient attention to resource and infrastructure constraints for real-life practical deployment and the lack of focus on maintaining user data privacy makes these solutions unsuitable for large-scale use. We propose a resource-efficient CoviFL framework using an AIoMT approach for remote COVID-19 detection while maintaining user data privacy. Federated learning has been used to decentralize the CoviFL CNN model training and test the COVID-19 status of users with an accuracy of 93.01 % on portable AIoMT edge devices. Experiments on real-world datasets suggest that the proposed CoviFL solution is promising for large-scale deployment even in resource and infrastructure-constrained environments making it suitable for remote COVID-19 detection. © 2022 IEEE.

Fruit-CoV: An efficient vision-based framework for speedy detection and diagnosis of SARS-CoV-2 infections through recorded cough sounds.

COVID-19 is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This deadly virus has spread worldwide, leading to a global pandemic since March 2020. A recent variant of SARS-CoV-2 named Delta is intractably contagious and responsible for more than four million deaths globally. Therefore, developing an efficient self-testing service for SARS-CoV-2 at home is vital. In this study, a two-stage vision-based framework, namely Fruit-CoV, is introduced for detecting SARS-CoV-2 infections through recorded cough sounds. Specifically, audio signals are converted into Log-Mel spectrograms, and the EfficientNet-V2 network is used to extract their visual features in the first stage. In the second stage, 14 convolutional layers extracted from the large-scale Pretrained Audio Neural Networks for audio pattern recognition (PANNs) and the Wavegram-Log-Mel-CNN are employed to aggregate feature representations of the Log-Mel spectrograms and the waveform. Finally, the combined features are used to train a binary classifier. In this study, a dataset provided by the AICovidVN 115M Challenge is employed for evaluation. It includes 7,371 recorded cough sounds collected throughout Vietnam, India, and Switzerland. Experimental results indicate that the proposed model achieves an Area Under the Receiver Operating Characteristic Curve (AUC) score of 92.8% and ranks first on the final leaderboard of the AICovidVN 115M Challenge. Our code is publicly available.

Diagnosis of Covid-19 by Sound Analysis

COVID-19 virus;has dragged the world into an epidemic that has infected more than 413 million people and caused the death of nearly 6 million people. Although biomedical tests provide the diagnosis of COVID-19 with high accuracy in the diagnosis of the disease, it increases the risk of infection due to the fact that it is a method that requires contact. Machine learning models have been proposed as an alternative to biomedical testing. Cough has been identified by the World Health Organization as one of the symptoms of COVID-19 disease. In this study, the success performance of the positive case situation with machine learning was examined using the COUGHVID dataset with cough voice recordings. In order to increase the performance of the model, MFCC, Δ-MFCC and Mel Coefficients attributes were obtained after preprocessing the sound recordings. In the ensemble learning model, features were used as independent variables and a value of 0.65 AUC-ROC was reached. In addition to these performance-enhancing changes, since the acoustic properties of male and female cough sounds are different, the training of persons was carried out separately from each other, and AUC-ROC values of 0.70 for females and 0.68 for males were obtained. Trimming the silent regions at the beginning and end of the recordings, using the ensemble learning model, and grouping based on gender provided better results for this study compared to previous studies. © 2022 IEEE.

Performance Comparison of Oral, Laryngeal and Thoracic Sounds in the Detection of COVID-19 by Employing Machine Learning Techniques

COVID-19 can directly or indirectly cause lung involvements by crossing the upper airways. It is essential to quickly detect the lung involvement condition and to follow up and treat these patients by early hospitalization. In recent COVID-19 diagnosis procedure, PCR testing is applied to the samples taken from the patients and a quarantine period is applied to the patient until the test results are received. As a complement to PCR tests and for faster diagnosis, thin-section lung computed tomography (CT) imaging is used in COVID-19 patients. In this study, it is aimed to develop a method that is as reliable as CT, and compared to CT, less risky, more accessible, and less costly for the diagnosis of COVID-19 disease. For this purpose, first speech and cough sounds from the oral, laryngeal and thoracic regions of COVID-19 patients and healthy individuals were obtained with the multi-channel voice recording system we proposed, the obtained data were processed with machine learning methods and their accuracies in COVID-19 diagnosis were presented comparatively. In our study, the best results were obtained with the features extracted from the cough sounds taken from the oral region. © 2022 IEEE.

COVID-19 Disease Classification by Cough Records Analysis using Machine Learning

The rapid spreading rate of the Coronavirus disease 2019 (COVID-19) has resulted in more than 6.2 million deceased cases. Furthermore, the patients of the latest Omicron variation carry light to almost no symptoms of the disease themselves. Thus, the requirement for a new diagnosis method besides Reverse Transcription-Polymerase Chain Reaction (RT-PCR) becomes the most important step to successfully detect infected cases. In this research, the application of the KNN, Ensemble and SincNet models are implemented as the main models for classification diagnosis based on cough sound records of infected patients. After pre-processing steps for removing silence ranges in the audio scripts, the cough sounds are augmented, subsequently separated into single cough samples, then generated 3 testing scenarios for dealing with the imbalanced problem between the sample classes. Afterward, MelFrequency information and MelSprectrogram are extracted as main features for analysis in order to distinguish patients with COVID-19 disease and healthy cases. The AICV115M dataset consisting of two classes COVID-19 and NonCOVID-19 is implemented for performance evaluation. The recorded highest accuracy on the models KNN, Ensemble and SincNet are 92.49%, 90.1% and 85.15%, respectively. © 2022 IEEE.

Learning complementary representations via attention-based ensemble learning for cough-based COVID-19 recognition

Coughs sounds have shown promising as-potential marker for distinguishing COVID individuals from non-COVID ones. In this paper, we propose an attention-based ensemble learning approach to learn complementary representations from cough samples. Unlike most traditional schemes such as mere maxing or averaging, the proposed approach fairly considers the contribution of the representation generated by each single model. The attention mechanism is further investigated at the feature level and the decision level. Evaluated on the Track-1 test set of the DiCOVA challenge 2021, the experimental results demonstrate that the proposed feature-level attention-based ensemble learning achieves the best performance (Area Under Curve, AUC: 77.96%), resulting in an 8.05% improvement over the challenge baseline. ©

CoughNet-V2: A Scalable Multimodal DNN Framework for Point-of-Care Edge Devices to Detect Symptomatic COVID-19 Cough

With the emergence of COVID-19 pandemic, new attention has been given to different acoustic bio-markers of the respiratory disorders. Deep Neural Network (DNN) has become very popular with the audio classification task due to its impressive performance for speech detection, audio event classification etc. This paper presents CoughNet-V2 - a scalable multimodal DNN framework to detect symptomatic COVID-19 cough. The framework was designed to be implemented on point-of-care edge devices to help the doctors at pre-screening stage for COVID-19 detection. A crowd-sourced multimodal data resource which contains subjects' cough audio along with other relevant medical information was used to design the CoughNet-V2 framework. CoughNet-V2 shows multimodal integration of cough audio along with medical records improves the classification performance than that of any unimodal frameworks. Proposed CoughNet-V2 achieved an area-under-curve (AUC) of 88.9% for the binary classification task of symptomatic COVID-19 cough detection. Finally, measurement of the deployment attributes of the CoughNet-V2 model onto processing components of an NVIDIA TX2 development board is presented as a proposition to bring the healthcare system to consumers' fingertips. Clinical relevance - CoughNet-V2 will help medical practitioners to asses whether the patients need intensive medical help without physically interacting with them. © 2022 IEEE.

COVID-19 Detection in Cough Audio Dataset Using Deep Learning Model

Using Deep Learning methods might be a proper answer to the need of the world for a fast, automatic solution for COVID-19 early-stage diagnosis. This article tries to take advantage of Convolutional Neural Network (CNN) systems for this purpose. Our proposed model is based on a CNN network and is trained based on the COUGHVID dataset. By implementing feature extraction using MFCC and using data augmentation methods, we tried to develop a fully functional model. The results show there were improvements compared to other state-of-the-art projects. Based on the metrics used in this work, we achieved an area under the curve of the receiver operating characteristics (AUC-ROC) of 0.94 on the task of COVID-19 classification. © 2022 IEEE.