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Kosar A, Asif M, Ahmad MB, Akram W, Mahmood K, Kumari S. Towards classification and comprehensive analysis of AI-based COVID-19 diagnostic techniques: A survey. Artif Intell Med 2024; 151:102858. [PMID: 38583369 DOI: 10.1016/j.artmed.2024.102858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Revised: 01/02/2024] [Accepted: 03/25/2024] [Indexed: 04/09/2024]
Abstract
The unpredictable pandemic came to light at the end of December 2019, known as the novel coronavirus, also termed COVID-19, identified by the World Health Organization (WHO). The virus first originated in Wuhan (China) and rapidly affected most of the world's population. This outbreak's impact is experienced worldwide because it causes high mortality risk, many cases, and economic falls. Around the globe, the total number of cases and deaths reported till November 12, 2022, were >600 million and 6.6 million, respectively. During the period of COVID-19, several diverse diagnostic techniques have been proposed. This work presents a systematic review of COVID-19 diagnostic techniques in response to such acts. Initially, these techniques are classified into different categories based on their working principle and detection modalities, i.e. chest X-ray imaging, cough sound or respiratory patterns, RT-PCR, antigen testing, and antibody testing. After that, a comparative analysis is performed to evaluate these techniques' efficacy which may help to determine an optimum solution for a particular scenario. The findings of the proposed work show that Artificial Intelligence plays a vital role in developing COVID-19 diagnostic techniques which support the healthcare system. The related work can be a footprint for all the researchers, available under a single umbrella. Additionally, all the techniques are long-lasting and can be used for future pandemics.
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Affiliation(s)
- Amna Kosar
- Department of Computer Science, Lahore Garrison University, Lahore, Pakistan
| | - Muhammad Asif
- Department of Computer Science, Lahore Garrison University, Lahore, Pakistan
| | - Maaz Bin Ahmad
- College of Computing and Information Sciences, Karachi Institute of Economics and Technology (KIET), Karachi, Pakistan
| | - Waseem Akram
- Graduate School of Engineering Science and Technology, National Yunlin University of Science and Technology, Douliu, Taiwan, ROC
| | - Khalid Mahmood
- Graduate School of Intelligent Data Science, National Yunlin University of Science and Technology, Douliu, Taiwan, ROC.
| | - Saru Kumari
- Departement of Mathematics, Chaudhary Charan Singh University, Meerut, India
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Al Hossain F, Tonmoy MTH, Nuvvula S, Chapman BP, Gupta RK, Lover AA, Dinglasan RR, Carreiro S, Rahman T. Syndromic surveillance of population-level COVID-19 burden with cough monitoring in a hospital emergency waiting room. Front Public Health 2024; 12:1279392. [PMID: 38605877 PMCID: PMC11007176 DOI: 10.3389/fpubh.2024.1279392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 03/11/2024] [Indexed: 04/13/2024] Open
Abstract
Syndromic surveillance is an effective tool for enabling the timely detection of infectious disease outbreaks and facilitating the implementation of effective mitigation strategies by public health authorities. While various information sources are currently utilized to collect syndromic signal data for analysis, the aggregated measurement of cough, an important symptom for many illnesses, is not widely employed as a syndromic signal. With recent advancements in ubiquitous sensing technologies, it becomes feasible to continuously measure population-level cough incidence in a contactless, unobtrusive, and automated manner. In this work, we demonstrate the utility of monitoring aggregated cough count as a syndromic indicator to estimate COVID-19 cases. In our study, we deployed a sensor-based platform (Syndromic Logger) in the emergency room of a large hospital. The platform captured syndromic signals from audio, thermal imaging, and radar, while the ground truth data were collected from the hospital's electronic health record. Our analysis revealed a significant correlation between the aggregated cough count and positive COVID-19 cases in the hospital (Pearson correlation of 0.40, p-value < 0.001). Notably, this correlation was higher than that observed with the number of individuals presenting with fever (ρ = 0.22, p = 0.04), a widely used syndromic signal and screening tool for such diseases. Furthermore, we demonstrate how the data obtained from our Syndromic Logger platform could be leveraged to estimate various COVID-19-related statistics using multiple modeling approaches. Aggregated cough counts and other data, such as people density collected from our platform, can be utilized to predict COVID-19 patient visits related metrics in a hospital waiting room, and SHAP and Gini feature importance-based metrics showed cough count as the important feature for these prediction models. Furthermore, we have shown that predictions based on cough counting outperform models based on fever detection (e.g., temperatures over 39°C), which require more intrusive engagement with the population. Our findings highlight that incorporating cough-counting based signals into syndromic surveillance systems can significantly enhance overall resilience against future public health challenges, such as emerging disease outbreaks or pandemics.
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Affiliation(s)
- Forsad Al Hossain
- Manning College of Information and Computer Sciences, University of Massachusetts-Amherst, Amherst, MA, United States
| | - M. Tanjid Hasan Tonmoy
- Halıcıoǧlu Data Science Institute, University of California, San Diego, San Diego, CA, United States
| | - Sri Nuvvula
- Department of Emergency Medicine, UMass Chan Medical School, Worcester, MA, United States
| | - Brittany P. Chapman
- Department of Emergency Medicine, UMass Chan Medical School, Worcester, MA, United States
| | - Rajesh K. Gupta
- Halıcıoǧlu Data Science Institute, University of California, San Diego, San Diego, CA, United States
| | - Andrew A. Lover
- School of Public Health & Health Sciences, University of Massachusetts Amherst, Amherst, MA, United States
| | - Rhoel R. Dinglasan
- Infectious Diseases and Immunology, University of Florida, Gainesville, FL, United States
| | - Stephanie Carreiro
- Department of Emergency Medicine, UMass Chan Medical School, Worcester, MA, United States
| | - Tauhidur Rahman
- Halıcıoǧlu Data Science Institute, University of California, San Diego, San Diego, CA, United States
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Diab MS, Rodriguez-Villegas E. Feature evaluation of accelerometry signals for cough detection. Front Digit Health 2024; 6:1368574. [PMID: 38585283 PMCID: PMC10995234 DOI: 10.3389/fdgth.2024.1368574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 03/06/2024] [Indexed: 04/09/2024] Open
Abstract
Cough is a common symptom of multiple respiratory diseases, such as asthma and chronic obstructive pulmonary disorder. Various research works targeted cough detection as a means for continuous monitoring of these respiratory health conditions. This has been mainly achieved using sophisticated machine learning or deep learning algorithms fed with audio recordings. In this work, we explore the use of an alternative detection method, since audio can generate privacy and security concerns related to the use of always-on microphones. This study proposes the use of a non-contact tri-axial accelerometer for motion detection to differentiate between cough and non-cough events/movements. A total of 43 time-domain features were extracted from the acquired tri-axial accelerometry signals. These features were evaluated and ranked for their importance using six methods with adjustable conditions, resulting in a total of 11 feature rankings. The ranking methods included model-based feature importance algorithms, first principal component, leave-one-out, permutation, and recursive features elimination (RFE). The ranking results were further used in the feature selection of the top 10, 20, and 30 for use in cough detection. A total of 68 classification models using a simple logistic regression classifier are reported, using two approaches for data splitting: subject-record-split and leave-one-subject-out (LOSO). The best-performing model out of the 34 using subject-record-split obtained an accuracy of 92.20%, sensitivity of 90.87%, specificity of 93.52%, and F1 score of 92.09% using only 20 features selected by the RFE method. The best-performing model out of the 34 using LOSO obtained an accuracy of 89.57%, sensitivity of 85.71%, specificity of 93.43%, and F1 score of 88.72% using only 10 features selected by the RFE method. These results demonstrate the ability for future implementation of a motion-based wearable cough detector.
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Affiliation(s)
- Maha S. Diab
- Wearable Technologies Lab, Department of Electrical and Electronic Engineering, Imperial College London, London, United Kingdom
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Saeed T, Ijaz A, Sadiq I, Qureshi HN, Rizwan A, Imran A. An AI-Enabled Bias-Free Respiratory Disease Diagnosis Model Using Cough Audio. Bioengineering (Basel) 2024; 11:55. [PMID: 38247932 PMCID: PMC10813025 DOI: 10.3390/bioengineering11010055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 12/25/2023] [Accepted: 01/03/2024] [Indexed: 01/23/2024] Open
Abstract
Cough-based diagnosis for respiratory diseases (RDs) using artificial intelligence (AI) has attracted considerable attention, yet many existing studies overlook confounding variables in their predictive models. These variables can distort the relationship between cough recordings (input data) and RD status (output variable), leading to biased associations and unrealistic model performance. To address this gap, we propose the Bias-Free Network (RBF-Net), an end-to-end solution that effectively mitigates the impact of confounders in the training data distribution. RBF-Net ensures accurate and unbiased RD diagnosis features, emphasizing its relevance by incorporating a COVID-19 dataset in this study. This approach aims to enhance the reliability of AI-based RD diagnosis models by navigating the challenges posed by confounding variables. A hybrid of a Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks is proposed for the feature encoder module of RBF-Net. An additional bias predictor is incorporated in the classification scheme to formulate a conditional Generative Adversarial Network (c-GAN) that helps in decorrelating the impact of confounding variables from RD prediction. The merit of RBF-Net is demonstrated by comparing classification performance with a State-of-The-Art (SoTA) Deep Learning (DL) model (CNN-LSTM) after training on different unbalanced COVID-19 data sets, created by using a large-scale proprietary cough data set. RBF-Net proved its robustness against extremely biased training scenarios by achieving test set accuracies of 84.1%, 84.6%, and 80.5% for the following confounding variables-gender, age, and smoking status, respectively. RBF-Net outperforms the CNN-LSTM model test set accuracies by 5.5%, 7.7%, and 8.2%, respectively.
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Affiliation(s)
- Tabish Saeed
- AI4Networks Research Center, Department of Electrical & Computer Engineering, University of Oklahoma, Tulsa, OK 74135, USA; (H.N.Q.); (A.I.)
| | - Aneeqa Ijaz
- AI4Networks Research Center, Department of Electrical & Computer Engineering, University of Oklahoma, Tulsa, OK 74135, USA; (H.N.Q.); (A.I.)
| | - Ismail Sadiq
- James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK;
| | - Haneya Naeem Qureshi
- AI4Networks Research Center, Department of Electrical & Computer Engineering, University of Oklahoma, Tulsa, OK 74135, USA; (H.N.Q.); (A.I.)
| | - Ali Rizwan
- AI4lyf, Bahria Town Lahore, Lahore 54000, Pakistan;
| | - Ali Imran
- AI4Networks Research Center, Department of Electrical & Computer Engineering, University of Oklahoma, Tulsa, OK 74135, USA; (H.N.Q.); (A.I.)
- James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK;
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Khaddage-Soboh N, Tawil S. Navigating the crisis: A review of COVID-19 research and the importance of academic publications - The case of a private university in Lebanon. Heliyon 2023; 9:e22917. [PMID: 38282919 PMCID: PMC10812900 DOI: 10.1016/j.heliyon.2023.e22917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 11/22/2023] [Accepted: 11/22/2023] [Indexed: 01/30/2024] Open
Abstract
Objectives The threat of the Corona virus has had a profound global impact, prompting extensive discussions among academicians and medical researchers seeking to understand its implications across various fields. Consequently this review aims to explore the COVID-19 research approaches adopted at the Lebanese American University (LAU) between 2019 and 2022 and, to eventually shed light on the importance of the academic publications during this crisis period in the context of Lebanon. Design Data sources Eligibility criteria Studies related to "Coronavirus", "SARS-CoV-2," or "COVID-19″ were extracted from the SciVal database spanning the period 2019 to 2022. The identified studies, totaling 97 publications, were indexed in Scopus and Web of Science and underwent narrative analysis along with an evaluation using a predefined scale to determine their eligibility. The majority of the studies were literature reviews, followed by observational studies, modeling studies, systematic reviews, and meta-analyses. Results The majority of the identified studies (31 %) were focused on the medical field, primarily the impact of SARS-CoV-2 infection. Additionally, 22 % of studies discussed updates related to global finance and economic markets, while 18 % addressed the psychological burden of the pandemic. Other areas covered in the literature included the impact on performance, nutrition, tourism, politics, and telecommunication. Conclusion This study marks a pioneering endeavor that sparks a crucial dialogue regarding peer-reviewed scientific literature during a period of immense need for accurate information. The prevalence of literature reviews can be attributed to the demand for swift dissemination of preliminary findings and the increased call for COVID-19-related research. However, despite the abundance of publications in this specific domain, it is imperative for future research to shift its focus towards the development of novel therapies, preventive measures, psychological insights, and strategies to address the socioeconomic and financial burdens stemming from the pandemic. This study has the potential to establish a standardized framework for addressing similar crises across diverse fields and at various levels. Limitations The review readily acknowledges certain limitations. By solely relying on specific databases like Scopus and WoS, there is a possibility of inadvertently overlooking relevant studies. Although the study provides insights into the impact of COVID-19 across different fields and their respective publications, it is important to recognize that the continuous updates to databases and potential exclusions of related studies may have imposed constraints on the findings. Moreover, the urgency for expeditious peer-review during the pandemic may have heightened the chances of errors and diminished transparency. This urgency has unfortunately increased the risk of fraudulent activities and misconduct.
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Affiliation(s)
- Nada Khaddage-Soboh
- Adnan Kassar School of Business, Lebanese American University (LAU), Beirut, Lebanon
| | - Samah Tawil
- School of Medicine, Lebanese American University (LAU), Beirut, Lebanon
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Celik G. CovidCoughNet: A new method based on convolutional neural networks and deep feature extraction using pitch-shifting data augmentation for covid-19 detection from cough, breath, and voice signals. Comput Biol Med 2023; 163:107153. [PMID: 37321101 PMCID: PMC10249348 DOI: 10.1016/j.compbiomed.2023.107153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 05/25/2023] [Accepted: 06/07/2023] [Indexed: 06/17/2023]
Abstract
This study proposes a new deep learning-based method that demonstrates high performance in detecting Covid-19 disease from cough, breath, and voice signals. This impressive method, named CovidCoughNet, consists of a deep feature extraction network (InceptionFireNet) and a prediction network (DeepConvNet). The InceptionFireNet architecture, based on Inception and Fire modules, was designed to extract important feature maps. The DeepConvNet architecture, which is made up of convolutional neural network blocks, was developed to predict the feature vectors obtained from the InceptionFireNet architecture. The COUGHVID dataset containing cough data and the Coswara dataset containing cough, breath, and voice signals were used as the data sets. The pitch-shifting technique was used to data augmentation the signal data, which significantly contributed to improving performance. Additionally, Chroma features (CF), Root mean square energy (RMSE), Spectral centroid (SC), Spectral bandwidth (SB), Spectral rolloff (SR), Zero crossing rate (ZCR), and Mel frequency cepstral coefficients (MFCC) feature extraction techniques were used to extract important features from voice signals. Experimental studies have shown that using the pitch-shifting technique improved performance by around 3% compared to raw signals. When the proposed model was used with the COUGHVID dataset (Healthy, Covid-19, and Symptomatic), a high performance of 99.19% accuracy, 0.99 precision, 0.98 recall, 0.98 F1-Score, 97.77% specificity, and 98.44% AUC was achieved. Similarly, when the voice data in the Coswara dataset was used, higher performance was achieved compared to the cough and breath studies, with 99.63% accuracy, 100% precision, 0.99 recall, 0.99 F1-Score, 99.24% specificity, and 99.24% AUC. Moreover, when compared with current studies in the literature, the proposed model was observed to exhibit highly successful performance. The codes and details of the experimental studies can be accessed from the relevant Github page: (https://github.com/GaffariCelik/CovidCoughNet).
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Affiliation(s)
- Gaffari Celik
- Agri Ibrahim Cecen University, Department of Computer Technology, Agri, Turkey.
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Gupta BB, Gaurav A, Panigrahi PK. Analysis of retail sector research evolution and trends during COVID-19. TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE 2023; 194:122671. [PMID: 37305440 PMCID: PMC10239906 DOI: 10.1016/j.techfore.2023.122671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 05/26/2023] [Accepted: 05/26/2023] [Indexed: 06/13/2023]
Abstract
The purpose of this study is to analysis the evolution of the retail sector during the COVID-19 period and to identify future research issues. Scopus databases were searched for articles published in English between 2020 and 2022 to discover current trends and concerns in the retail industry. A total of 1071 empirical and nonempirical studies were compiled as a result of the evaluation process. During the study period, the number of articles published in scientific journals increased exponentially, indicating that the research topic is still in the developmental phase. It also highlights the most important research trends, allowing numerous new research lines to be proposed via visual mapping of Thematic Maps. This study makes an important contribution to the field of the retail sector, providing a comprehensive overview of the field's evolution and current status, as well as a comprehensive, synthesized, and organized summary of the various perspectives, definitions, and trends in the field.
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Affiliation(s)
- Brij B Gupta
- International Center for AI and Cyber Security Research and Innovations & Department of Computer Science and Information Engineering, Asia University, Taiching, Taiwan
- Kyung Hee University, 26 Kyungheedae-ro, Dongdaemun-gu, Seoul, 02447, Korea
- Lebanese American University, Beirut, 1102, Lebanon
- Symbiosis Centre for Information Technology (SCIT), Symbiosis International University, Pune, India
- Center for Interdisciplinary Research at University of Petroleum and Energy Studies (UPES), Dehradun, India
| | - Akshat Gaurav
- Ronin Institute, Montclair, NJ, USA
- University Center for Research & Development (UCRD), Chandigarh University, Chandigarh 140413, India
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Erb WM, Barrow EJ, Hofner AN, Lecorchick JL, Mitra Setia T, Vogel ER. Wildfire smoke linked to vocal changes in wild Bornean orangutans. iScience 2023; 26:107088. [PMID: 37456857 PMCID: PMC10339020 DOI: 10.1016/j.isci.2023.107088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 06/23/2022] [Accepted: 06/07/2023] [Indexed: 07/18/2023] Open
Abstract
Tropical peatlands are the sites of Earth's largest fire events, with outsized contributions to greenhouse gases, toxic smoke, and haze rich with particulate matter. The human health risks from wildfire smoke are well known, but its effects on wildlife inhabiting these ecosystems are poorly understood. In 2015, peatland fires on Borneo created a thick haze of smoke that blanketed the region. We studied its effects on the long call vocalizations of four adult male Bornean orangutans (Pongo pygmaeus wurmbii) in a peat swamp forest. During the period of heavy smoke, orangutans called less often and showed reduced vocal quality-lower pitch, increased harshness and perturbations, and more nonlinear phenomena-similar to changes in human smokers. Most of these changes persisted for two months after the smoke had cleared and likely signal changes in health. Our work contributes valuable information to support non-invasive acoustic monitoring of this Critically Endangered primate.
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Affiliation(s)
- Wendy M. Erb
- K. Lisa Yang Center for Conservation Bioacoustics, Cornell Lab of Ornithology, Cornell University, Ithaca, NY 14850, USA
- Department of Anthropology, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA
| | - Elizabeth J. Barrow
- Department of Social Sciences, Oxford Brookes University, Headington, Oxford OX3 0BP, UK
- Gunung Palung Orangutan Conservation Program, West Kalimantan, Ketapang 78811, Indonesia
| | - Alexandra N. Hofner
- Department of Integrative Conservation, University of Georgia, Athens, GA 30602, USA
- Department of Anthropology, University of Georgia, Athens, GA 30602, USA
| | - Jessica L. Lecorchick
- K. Lisa Yang Center for Conservation Bioacoustics, Cornell Lab of Ornithology, Cornell University, Ithaca, NY 14850, USA
| | - Tatang Mitra Setia
- Fakultas Biologi, Universitas Nasional, Jakarta 12520, Indonesia
- Primate Research Center, Universitas Nasional, Jakarta 12520, Indonesia
| | - Erin R. Vogel
- Department of Anthropology, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA
- Center for Human Evolutionary Studies, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA
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Samah T. Identifying health research in the era of COVID-19: A scoping review. SAGE Open Med 2023; 11:20503121231180030. [PMID: 37324118 PMCID: PMC10262656 DOI: 10.1177/20503121231180030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 05/18/2023] [Indexed: 06/17/2023] Open
Abstract
Background Health improvements are considered one of the most important fields of research. Since the coronavirus disease 2019 was declared a pandemic, it might have impacted clinical and public health research in various forms. Objectives The goal of this study is to explore health research approaches in the era of coronavirus disease 2019. Methods In this scoping review, we reviewed published medical full-text studies and identified potential areas of interest of health research in the era the coronavirus disease 2019 pandemic during the last 3 years within a higher educational setting. A bibliometric analysis was used to compare among published works. Results Among the 93 studies that met the inclusion criteria, most focused on mental health (n = 23; 24.7%). Twenty-one publications targeted coronavirus disease 2019 and its consequences on general health. Other studies have described hemato-oncological, cardiovascular, respiratory, and endocrinological diseases. 42 studies were cross-sectional or cohort studies and most of them published in Q1 journals. Almost half of them belonged to the Faculty of Medicine (49.5%) followed by the School of Arts, Sciences, and Psychology (26.9%). Conclusions Health research has been demonstrated to be important, at all times, especially during crises. Therefore, researchers need to invest more efforts into seeking new medical updates in various health-related fields, regardless of their correlation with coronavirus disease 2019.
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Affiliation(s)
- Tawil Samah
- School of Medicine, Lebanese American University, Beirut, Lebanon
- Institut National de Santé Publique d’Épidémiologie Clinique et de Toxicologie-Liban (INSPECT-LB), Beirut, Lebanon
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Ribeiro P, Marques JAL, Rodrigues PM. COVID-19 Detection by Means of ECG, Voice, and X-ray Computerized Systems: A Review. Bioengineering (Basel) 2023; 10:bioengineering10020198. [PMID: 36829692 PMCID: PMC9952817 DOI: 10.3390/bioengineering10020198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 01/31/2023] [Accepted: 02/01/2023] [Indexed: 02/05/2023] Open
Abstract
Since the beginning of 2020, Coronavirus Disease 19 (COVID-19) has attracted the attention of the World Health Organization (WHO). This paper looks into the infection mechanism, patient symptoms, and laboratory diagnosis, followed by an extensive assessment of different technologies and computerized models (based on Electrocardiographic signals (ECG), Voice, and X-ray techniques) proposed as a diagnostic tool for the accurate detection of COVID-19. The found papers showed high accuracy rate results, ranging between 85.70% and 100%, and F1-Scores from 89.52% to 100%. With this state-of-the-art, we concluded that the models proposed for the detection of COVID-19 already have significant results, but the area still has room for improvement, given the vast symptomatology and the better comprehension of individuals' evolution of the disease.
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Affiliation(s)
- Pedro Ribeiro
- CBQF—Centro de Biotecnologia e Química Fina—Laboratório Associado, Escola Superior de Biotecnologia, Universidade Católica Portuguesa, Rua de Diogo Botelho 1327, 4169-005 Porto, Portugal
| | | | - Pedro Miguel Rodrigues
- CBQF—Centro de Biotecnologia e Química Fina—Laboratório Associado, Escola Superior de Biotecnologia, Universidade Católica Portuguesa, Rua de Diogo Botelho 1327, 4169-005 Porto, Portugal
- Correspondence:
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Davidson C, Caguana OA, Lozano-García M, Arita Guevara M, Estrada-Petrocelli L, Ferrer-Lluis I, Castillo-Escario Y, Ausín P, Gea J, Jané R. Differences in acoustic features of cough by pneumonia severity in patients with COVID-19: a cross-sectional study. ERJ Open Res 2023; 9:00247-2022. [PMID: 37131524 PMCID: PMC9922471 DOI: 10.1183/23120541.00247-2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 01/07/2023] [Indexed: 02/05/2023] Open
Abstract
BackgroundAcute respiratory syndrome due to coronavirus 2 (SARS-CoV-2) is characterised by heterogeneous levels of disease severity. It is not necessarily apparent whether a patient will develop a severe disease or not. This cross-sectional study explores whether acoustic properties of the cough sound of patients with coronavirus disease (COVID-19), the illness caused by SARS-CoV-2, correlate with their disease and pneumonia severity, with the aim of identifying patients with a severe disease.MethodsVoluntary cough sounds were recorded using a smartphone in 70 COVID-19 patients within the first 24 h of their hospital arrival, between April 2020 and May 2021. Based on gas exchange abnormalities, patients were classified as mild, moderate, or severe. Time- and frequency-based variables were obtained from each cough effort and analysed using a linear mixed-effects modelling approach.ResultsRecords from 62 patients (37% female) were eligible for inclusion in the analysis, with mild, moderate, and severe groups consisting of 31, 14 and 17 patients respectively. 5 of the parameters examined were found to be significantly different in the cough of patients at different disease levels of severity, with a further 2 parameters found to be affected differently by the disease severity in men and women.ConclusionsWe suggest that all these differences reflect the progressive pathophysiological alterations occurring in the respiratory system of COVID-19 patients, and potentially would provide an easy and cost-effective way to initially stratify patients, identifying those with more severe disease, and thereby most effectively allocate healthcare resources.
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Altshuler E, Tannir B, Jolicoeur G, Rudd M, Saleem C, Cherabuddi K, Doré DH, Nagarsheth P, Brew J, Small PM, Glenn Morris J, Grandjean Lapierre S. Digital cough monitoring - A potential predictive acoustic biomarker of clinical outcomes in hospitalized COVID-19 patients. J Biomed Inform 2023; 138:104283. [PMID: 36632859 PMCID: PMC9827741 DOI: 10.1016/j.jbi.2023.104283] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 01/03/2023] [Accepted: 01/05/2023] [Indexed: 01/11/2023]
Abstract
PURPOSE Recent developments in the field of artificial intelligence and acoustics have made it possible to objectively monitor cough in clinical and ambulatory settings. We hypothesized that time patterns of objectively measured cough in COVID-19 patients could predict clinical prognosis and help rapidly identify patients at high risk of intubation or death. METHODS One hundred and twenty-three patients hospitalized with COVID-19 were enrolled at University of Florida Health Shands and the Centre Hospitalier de l'Université de Montréal. Patients' cough was continuously monitored digitally along with clinical severity of disease until hospital discharge, intubation, or death. The natural history of cough in hospitalized COVID-19 disease was described and logistic models fitted on cough time patterns were used to predict clinical outcomes. RESULTS In both cohorts, higher early coughing rates were associated with more favorable clinical outcomes. The transitional cough rate, or maximum cough per hour rate predicting unfavorable outcomes, was 3·40 and the AUC for cough frequency as a predictor of unfavorable outcomes was 0·761. The initial 6 h (0·792) and 24 h (0·719) post-enrolment observation periods confirmed this association and showed similar predictive value. INTERPRETATION Digital cough monitoring could be used as a prognosis biomarker to predict unfavorable clinical outcomes in COVID-19 disease. With early sampling periods showing good predictive value, this digital biomarker could be combined with clinical and paraclinical evaluation and is well adapted for triaging patients in overwhelmed or resources-limited health programs.
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Affiliation(s)
- Ellery Altshuler
- Department of Internal Medicine, University of Florida College of Medicine, 1600 SW, Archer Road, PO Box 100294, Gainesville, FL, USA
| | - Bouchra Tannir
- Centre de Recherche du Centre Hospitalier de l’Université de Montréal, 900, Saint-Denis, Montréal, Québec H2X 0A9, Canada
| | - Gisèle Jolicoeur
- Centre de Recherche du Centre Hospitalier de l’Université de Montréal, 900, Saint-Denis, Montréal, Québec H2X 0A9, Canada
| | - Matthew Rudd
- Department of Mathematics and Computer Science, The University of the South, 735, University Avenue, Sewanee, TN 37383, USA
| | - Cyrus Saleem
- Emerging Pathogens Institute, University of Florida, 2055, Mowry Rd, Gainesville, FL 32603, USA
| | - Kartikeya Cherabuddi
- Department of Internal Medicine, University of Florida College of Medicine, 1600 SW, Archer Road, PO Box 100294, Gainesville, FL, USA,Emerging Pathogens Institute, University of Florida, 2055, Mowry Rd, Gainesville, FL 32603, USA
| | - Dominique Hélène Doré
- Centre de Recherche du Centre Hospitalier de l’Université de Montréal, 900, Saint-Denis, Montréal, Québec H2X 0A9, Canada
| | | | - Joe Brew
- Hyfe Inc, 1209, Orange Street, Wilmington, DE 19801, USA
| | - Peter M. Small
- Hyfe Inc, 1209, Orange Street, Wilmington, DE 19801, USA,Department of Global Health, School of Medicine, University of Washington, WA 98105, USA
| | - J. Glenn Morris
- Department of Internal Medicine, University of Florida College of Medicine, 1600 SW, Archer Road, PO Box 100294, Gainesville, FL, USA,Emerging Pathogens Institute, University of Florida, 2055, Mowry Rd, Gainesville, FL 32603, USA
| | - Simon Grandjean Lapierre
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, 900, Saint-Denis, Montréal, Québec H2X 0A9, Canada; Département de Microbiologie, Infectiologie et Immunologie, Université de Montréal, 2900, Boul Edouard-Montpetit, Montréal, Québec H3T 1J4, Canada.
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13
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Najaran MHT. An evolutionary ensemble learning for diagnosing COVID-19 via cough signals. INTELLIGENT MEDICINE 2023; 3:S2667-1026(23)00002-5. [PMID: 36743333 PMCID: PMC9882956 DOI: 10.1016/j.imed.2023.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 01/10/2023] [Accepted: 01/11/2023] [Indexed: 01/30/2023]
Abstract
Objective The spread of the COVID-19 disease has caused great concern around the world and detecting the positive cases is crucial in curbing the pandemic. One of the symptoms of the disease is the dry cough it causes. It has previously been shown that cough signals can be used to identify a variety of diseases including tuberculosis, asthma, etc. In this paper, we proposed an algorithm to diagnose via cough signals the COVID-19 disease. Methods The proposed algorithm is an ensemble scheme that consists of a number of base learners, where each base learner uses a different feature extractor method, including statistical approaches and convolutional neural networks (CNN) for automatic feature extraction. Features are extracted from the raw signal and some transforms performed it, including Fourier, wavelet, Hilbert-Huang, and short-term Fourier transforms. The outputs of these base-learners are aggregated via a weighted voting scheme, with the weights optimised via an evolutionary paradigm. This paper also proposes a memetic algorithm for training the CNNs in the base-learners, which combines the speed of gradient descent (GD) algorithms and global search space coverage of the evolutionary algorithms. Results Experiments were performed on the proposed algorithm and different rival algorithms which included a number of CNN architectures in the literature and generic machine learning algorithms. The results suggested that the proposed algorithm achieves better performance compared to the existing algorithms in diagnosing COVID-19 via cough signals. Conclusion This research showed that COVID-19 could be diagnosed via cough signals and CNNs could be employed to process these signals and it may be further improved by the optimization of CNN architecture.
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14
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Chetupalli SR, Krishnan P, Sharma N, Muguli A, Kumar R, Nanda V, Pinto LM, Ghosh PK, Ganapathy S. Multi-Modal Point-of-Care Diagnostics for COVID-19 Based on Acoustics and Symptoms. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2023; 11:199-210. [PMID: 36909300 PMCID: PMC9994626 DOI: 10.1109/jtehm.2023.3250700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 12/05/2022] [Accepted: 02/22/2023] [Indexed: 03/14/2023]
Abstract
BACKGROUND The COVID-19 pandemic has highlighted the need to invent alternative respiratory health diagnosis methodologies which provide improvement with respect to time, cost, physical distancing and detection performance. In this context, identifying acoustic bio-markers of respiratory diseases has received renewed interest. OBJECTIVE In this paper, we aim to design COVID-19 diagnostics based on analyzing the acoustics and symptoms data. Towards this, the data is composed of cough, breathing, and speech signals, and health symptoms record, collected using a web-application over a period of twenty months. METHODS We investigate the use of time-frequency features for acoustic signals and binary features for encoding different health symptoms. We experiment with use of classifiers like logistic regression, support vector machines and long-short term memory (LSTM) network models on the acoustic data, while decision tree models are proposed for the symptoms data. RESULTS We show that a multi-modal integration of inference from different acoustic signal categories and symptoms achieves an area-under-curve (AUC) of 96.3%, a statistically significant improvement when compared against any individual modality ([Formula: see text]). Experimentation with different feature representations suggests that the mel-spectrogram acoustic features performs relatively better across the three kinds of acoustic signals. Further, a score analysis with data recorded from newer SARS-CoV-2 variants highlights the generalization ability of the proposed diagnostic approach for COVID-19 detection. CONCLUSION The proposed method shows a promising direction for COVID-19 detection using a multi-modal dataset, while generalizing to new COVID variants.
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Affiliation(s)
- Srikanth Raj Chetupalli
- LEAP LaboratoryDepartment of Electrical EngineeringIndian Institute of Science Bengaluru 560012 India
| | - Prashant Krishnan
- LEAP LaboratoryDepartment of Electrical EngineeringIndian Institute of Science Bengaluru 560012 India
| | - Neeraj Sharma
- LEAP LaboratoryDepartment of Electrical EngineeringIndian Institute of Science Bengaluru 560012 India
| | - Ananya Muguli
- LEAP LaboratoryDepartment of Electrical EngineeringIndian Institute of Science Bengaluru 560012 India
| | - Rohit Kumar
- LEAP LaboratoryDepartment of Electrical EngineeringIndian Institute of Science Bengaluru 560012 India
| | - Viral Nanda
- P. D. Hinduja National Hospital and Medical Research Center Mumbai 400016 India
| | - Lancelot Mark Pinto
- P. D. Hinduja National Hospital and Medical Research Center Mumbai 400016 India
| | - Prasanta Kumar Ghosh
- LEAP LaboratoryDepartment of Electrical EngineeringIndian Institute of Science Bengaluru 560012 India
| | - Sriram Ganapathy
- LEAP LaboratoryDepartment of Electrical EngineeringIndian Institute of Science Bengaluru 560012 India
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15
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Hamidi M, Zealouk O, Satori H, Laaidi N, Salek A. COVID-19 assessment using HMM cough recognition system. INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY : AN OFFICIAL JOURNAL OF BHARATI VIDYAPEETH'S INSTITUTE OF COMPUTER APPLICATIONS AND MANAGEMENT 2023; 15:193-201. [PMID: 36313860 PMCID: PMC9595586 DOI: 10.1007/s41870-022-01120-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 10/13/2022] [Indexed: 11/06/2022]
Abstract
This paper is a part of our contributions to research on the ongoing COVID-19 pandemic around the world. This research aims to use Hidden Markov Model (HMM) based automatic speech recognition system to analyze the cough signal and determine whether the signal belongs to a sick or healthy speaker. We built a configurable model by using HMMs, Gaussian Mixture Models (GMMs), Mel frequency spectral coefficients (MFCCs) and a cough corpus collected from healthy and sick voluntary speakers. Our proposed method is able to classify dry cough with sensitivity from 85.86% to 91.57%, differentiate the dry cough, and cough COVID-19 symptom with specificity from 5 to 10%. The obtained results are very encouraging to enrich our corpus with more data and increase the performance of our diagnostic system.
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Affiliation(s)
- Mohamed Hamidi
- Advanced Systems Engineering Laboratory, ENSA-UIT, Kenitra, Morocco ,grid.412150.30000 0004 0648 5985Multimedia and Arts Department, FLLA, UIT, Kenitra, Morocco
| | - Ouissam Zealouk
- LISAC, Department of Mathematics and Computer Science, FSDM, USMBA, Fez, Morocco
| | - Hassan Satori
- LISAC, Department of Mathematics and Computer Science, FSDM, USMBA, Fez, Morocco
| | - Naouar Laaidi
- LISAC, Department of Mathematics and Computer Science, FSDM, USMBA, Fez, Morocco
| | - Amine Salek
- Faculty of Medicine and Pharmacy, UMP, Oujda, Morocco
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16
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Cough Audio Analysis for COVID-19 Diagnosis. SN COMPUTER SCIENCE 2023; 4:125. [PMID: 36589771 PMCID: PMC9791965 DOI: 10.1007/s42979-022-01522-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 11/23/2022] [Indexed: 12/27/2022]
Abstract
Humanity has suffered catastrophically due to the COVID-19 pandemic. One of the most reliable diagnoses of COVID-19 is RT-PCR (reverse-transcription polymer chain reaction) testing. This method, however, has its limitations. It is time consuming and requires scalability. This research work carries out a preliminary prognosis of COVID-19, which is scalable and less time consuming. The research carried out a competitive analysis of four machine-learning models namely, Multilayer Perceptron, Convolutional Neural Networks, Recurrent Neural Networks with Long Short-Term Memory, and VGG-19 with Support Vector Machines. Out of these models, Multilayer Perceptron outperformed with higher specificity of 94.5% and accuracy of 96.8%. The results show that Multilayer Perceptron was able to distinguish between positive and negative COVID-19 coughs by a robust feature embedding technique.
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17
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Shiomi M, Kubota A, Kimoto M, Iio T, Shimohara K. Stay away from me: Coughing increases social distance even in a virtual environment. PLoS One 2022; 17:e0279717. [PMID: 36576927 PMCID: PMC9797075 DOI: 10.1371/journal.pone.0279717] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 12/12/2022] [Indexed: 12/29/2022] Open
Abstract
This study investigated whether the coughing behaviors of virtual agents encourage infection avoidance behavior, i.e., distancing behaviors. We hypothesized that the changes in people's lifestyles in physical environments due to COVID-19 probably influence their behaviors, even in virtual environments where no infection risk is present. We focused on different types of virtual agents because non-human agents, such as robot-like agents, cannot spread a virus by coughing. We prepared four kinds of virtual agents (human-like/robot-like and male/female) and coughing behaviors for them and experimentally measured the personal distance maintained by participants toward them. Our experiment results showed that participants chose a greater distance from coughing agents, regardless of the types, and negatively evaluated them. They also chose a greater distance from male agents than from female agents.
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Affiliation(s)
- Masahiro Shiomi
- Department of Agent Interaction Design Laboratory, Advanced Telecommunications Research Institute International, Kyoto, Japan
- * E-mail:
| | - Atsumu Kubota
- Department of Agent Interaction Design Laboratory, Advanced Telecommunications Research Institute International, Kyoto, Japan
- Faculty of Science and Engineering, Doshisha University, Kyoto, Japan
| | - Mitsuhiko Kimoto
- Department of Agent Interaction Design Laboratory, Advanced Telecommunications Research Institute International, Kyoto, Japan
| | - Takamasa Iio
- Department of Agent Interaction Design Laboratory, Advanced Telecommunications Research Institute International, Kyoto, Japan
- Faculty of Culture and Information Science, Doshisha University, Kyoto, Japan
| | - Katsunori Shimohara
- Department of Agent Interaction Design Laboratory, Advanced Telecommunications Research Institute International, Kyoto, Japan
- Faculty of Science and Engineering, Doshisha University, Kyoto, Japan
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18
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Grant D, McLane I, Rennoll V, West J. Considerations and Challenges for Real-World Deployment of an Acoustic-Based COVID-19 Screening System. SENSORS (BASEL, SWITZERLAND) 2022; 22:9530. [PMID: 36502232 PMCID: PMC9739601 DOI: 10.3390/s22239530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 11/30/2022] [Accepted: 12/03/2022] [Indexed: 06/17/2023]
Abstract
Coronavirus disease 2019 (COVID-19) has led to countless deaths and widespread global disruptions. Acoustic-based artificial intelligence (AI) tools could provide a simple, scalable, and prompt method to screen for COVID-19 using easily acquirable physiological sounds. These systems have been demonstrated previously and have shown promise but lack robust analysis of their deployment in real-world settings when faced with diverse recording equipment, noise environments, and test subjects. The primary aim of this work is to begin to understand the impacts of these real-world deployment challenges on the system performance. Using Mel-Frequency Cepstral Coefficients (MFCC) and RelAtive SpecTrAl-Perceptual Linear Prediction (RASTA-PLP) features extracted from cough, speech, and breathing sounds in a crowdsourced dataset, we present a baseline classification system that obtains an average receiver operating characteristic area under the curve (AUC-ROC) of 0.77 when discriminating between COVID-19 and non-COVID subjects. The classifier performance is then evaluated on four additional datasets, resulting in performance variations between 0.64 and 0.87 AUC-ROC, depending on the sound type. By analyzing subsets of the available recordings, it is noted that the system performance degrades with certain recording devices, noise contamination, and with symptom status. Furthermore, performance degrades when a uniform classification threshold from the training data is subsequently used across all datasets. However, the system performance is robust to confounding factors, such as gender, age group, and the presence of other respiratory conditions. Finally, when analyzing multiple speech recordings from the same subjects, the system achieves promising performance with an AUC-ROC of 0.78, though the classification does appear to be impacted by natural speech variations. Overall, the proposed system, and by extension other acoustic-based diagnostic aids in the literature, could provide comparable accuracy to rapid antigen testing but significant deployment challenges need to be understood and addressed prior to clinical use.
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19
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Kuluozturk M, Kobat MA, Barua PD, Dogan S, Tuncer T, Tan RS, Ciaccio EJ, Acharya UR. DKPNet41: Directed knight pattern network-based cough sound classification model for automatic disease diagnosis. Med Eng Phys 2022; 110:103870. [PMID: 35989223 PMCID: PMC9356574 DOI: 10.1016/j.medengphy.2022.103870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 08/03/2022] [Accepted: 08/05/2022] [Indexed: 01/18/2023]
Abstract
PROBLEM Cough-based disease detection is a hot research topic for machine learning, and much research has been published on the automatic detection of Covid-19. However, these studies are useful for the diagnosis of different diseases. AIM In this work, we collected a new and large (n=642 subjects) cough sound dataset comprising four diagnostic categories: 'Covid-19', 'heart failure', 'acute asthma', and 'healthy', and used it to train, validate, and test a novel model designed for automatic detection. METHOD The model consists of four main components: novel feature generation based on a specifically directed knight pattern (DKP), signal decomposition using four pooling methods, feature selection using iterative neighborhood analysis (INCA), and classification using the k-nearest neighbor (kNN) classifier with ten-fold cross-validation. Multilevel multiple pooling decomposition combined with DKP yielded 41 feature vectors (40 extracted plus one original cough sound). From these, the ten best feature vectors were selected. Based on each vector's misclassification rate, redundant feature vectors were eliminated and then merged. The merged vector's most informative features automatically selected using INCA were input to a standard kNN classifier. RESULTS The model, called DKPNet41, attained a high accuracy of 99.39% for cough sound-based multiclass classification of the four categories. CONCLUSIONS The results obtained in the study showed that the DKPNet41 model automatically and efficiently classifies cough sounds for disease diagnosis.
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Affiliation(s)
- Mutlu Kuluozturk
- Department of Pulmonology, Firat University Hospital, Elazig, Turkey
| | - Mehmet Ali Kobat
- Department of Cardiology, Firat University Hospital, Elazig, Turkey
| | - Prabal Datta Barua
- School of Management & Enterprise, University of Southern Queensland, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Australia
| | - Sengul Dogan
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey.
| | - Turker Tuncer
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
| | - Ru-San Tan
- Department of Cardiology, National Heart Centre Singapore, Singapore; Duke-NUS Medical School, Singapore
| | - Edward J Ciaccio
- Department of Medicine, Columbia University Irving Medical Center, USA
| | - U Rajendra Acharya
- Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, 599489, Singapore; Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore; Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
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20
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Aleixandre JG, Elgendi M, Menon C. The Use of Audio Signals for Detecting COVID-19: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:8114. [PMID: 36365811 PMCID: PMC9653621 DOI: 10.3390/s22218114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 10/17/2022] [Accepted: 10/20/2022] [Indexed: 06/16/2023]
Abstract
A systematic review on the topic of automatic detection of COVID-19 using audio signals was performed. A total of 48 papers were obtained after screening 659 records identified in the PubMed, IEEE Xplore, Embase, and Google Scholar databases. The reviewed studies employ a mixture of open-access and self-collected datasets. Because COVID-19 has only recently been investigated, there is a limited amount of available data. Most of the data are crowdsourced, which motivated a detailed study of the various pre-processing techniques used by the reviewed studies. Although 13 of the 48 identified papers show promising results, several have been performed with small-scale datasets (<200). Among those papers, convolutional neural networks and support vector machine algorithms were the best-performing methods. The analysis of the extracted features showed that Mel-frequency cepstral coefficients and zero-crossing rate continue to be the most popular choices. Less common alternatives, such as non-linear features, have also been proven to be effective. The reported values for sensitivity range from 65.0% to 99.8% and those for accuracy from 59.0% to 99.8%.
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Affiliation(s)
- José Gómez Aleixandre
- Biomedical and Mobile Health Technology Lab, ETH Zurich, 8008 Zurich, Switzerland
- Department of Physics, ETH Zurich, 8093 Zurich, Switzerland
| | - Mohamed Elgendi
- Biomedical and Mobile Health Technology Lab, ETH Zurich, 8008 Zurich, Switzerland
| | - Carlo Menon
- Biomedical and Mobile Health Technology Lab, ETH Zurich, 8008 Zurich, Switzerland
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21
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Albadr MAA, Tiun S, Ayob M, AL-Dhief FT. Particle Swarm Optimization-Based Extreme Learning Machine for COVID-19 Detection. Cognit Comput 2022:1-16. [PMID: 36247809 PMCID: PMC9554849 DOI: 10.1007/s12559-022-10063-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Accepted: 10/05/2022] [Indexed: 12/15/2022]
Abstract
COVID-19 (coronavirus disease 2019) is an ongoing global pandemic caused by severe acute respiratory syndrome coronavirus 2. Recently, it has been demonstrated that the voice data of the respiratory system (i.e., speech, sneezing, coughing, and breathing) can be processed via machine learning (ML) algorithms to detect respiratory system diseases, including COVID-19. Consequently, many researchers have applied various ML algorithms to detect COVID-19 by using voice data from the respiratory system. However, most of the recent COVID-19 detection systems have worked on a limited dataset. In other words, the systems utilize cough and breath voices only and ignore the voices of the other respiratory system, such as speech and vowels. In addition, another issue that should be considered in COVID-19 detection systems is the classification accuracy of the algorithm. The particle swarm optimization-extreme learning machine (PSO-ELM) is an ML algorithm that can be considered an accurate and fast algorithm in the process of classification. Therefore, this study proposes a COVID-19 detection system by utilizing the PSO-ELM as a classifier and mel frequency cepstral coefficients (MFCCs) for feature extraction. In this study, respiratory system voice samples were taken from the Corona Hack Respiratory Sound Dataset (CHRSD). The proposed system involves thirteen different scenarios: breath deep, breath shallow, all breath, cough heavy, cough shallow, all cough, count fast, count normal, all count, vowel a, vowel e, vowel o, and all vowels. The experimental results demonstrated that the PSO-ELM was capable of attaining the highest accuracy, reaching 95.83%, 91.67%, 89.13%, 96.43%, 92.86%, 88.89%, 96.15%, 96.43%, 88.46%, 96.15%, 96.15%, 95.83%, and 82.89% for breath deep, breath shallow, all breath, cough heavy, cough shallow, all cough, count fast, count normal, all count, vowel a, vowel e, vowel o, and all vowel scenarios, respectively. The PSO-ELM is an efficient technique for the detection of COVID-19 utilizing voice data from the respiratory system.
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Affiliation(s)
| | - Sabrina Tiun
- CAIT, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor Malaysia
| | - Masri Ayob
- CAIT, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor Malaysia
| | - Fahad Taha AL-Dhief
- School of Electrical Engineering, Department of Communication Engineering, Universiti Teknologi Malaysia, UTM Johor Bahru, Johor, Malaysia
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22
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Sunitha G, Arunachalam R, Abd‐Elnaby M, Eid MMA, Rashed ANZ. A comparative analysis of deep neural network architectures for the dynamic diagnosis of COVID-19 based on acoustic cough features. INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2022; 32:1433-1446. [PMID: 35941929 PMCID: PMC9348187 DOI: 10.1002/ima.22749] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 03/31/2022] [Accepted: 05/03/2022] [Indexed: 06/15/2023]
Abstract
The study aims to assess the detection performance of a rapid primary screening technique for COVID-19 that is purely based on the cough sound extracted from 2200 clinically validated samples using laboratory molecular testing (1100 COVID-19 negative and 1100 COVID-19 positive). Results and severity of samples based on quantitative RT-PCR (qRT-PCR), cycle threshold, and patient lymphocyte numbers were clinically labeled. Our suggested general methods consist of a tensor based on audio characteristics and deep-artificial neural network classification with deep cough convolutional layers, based on the dilated temporal convolution neural network (DTCN). DTCN has approximately 76% accuracy, 73.12% in TCN, and 72.11% in CNN-LSTM which have been trained at a learning rate of 0.2%, respectively. In our scenario, CNN-LSTM can no longer be employed for COVID-19 predictions, as they would generally offer questionable forecasts. In the previous stage, we discussed the exactness of the total cases of TCN, dilated TCN, and CNN-LSTM models which were truly predicted. Our proposed technique to identify COVID-19 can be considered as a robust and in-demand technique to rapidly detect the infection. We believe it can considerably hinder the COVID-19 pandemic worldwide.
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Affiliation(s)
- Gurram Sunitha
- Department of Computer Science EngineeringSree Vidyanikethan Engineering CollegeTirupatiAndhra PradeshIndia
| | - Rajesh Arunachalam
- Department of Electronics and Communication EngineeringCVR College of Engineering (Autonomous)HyderabadTelangana501510India
| | - Mohammed Abd‐Elnaby
- Department of Computer Engineering, College of Computers and Information TechnologyTaif UniversityP.O. Box 11099Taif 21944Saudi Arabia
| | - Mahmoud M. A. Eid
- Department of Electrical Engineering, College of EngineeringTaif UniversityP.O. Box 11099Taif 21944Saudi Arabia
| | - Ahmed Nabih Zaki Rashed
- Electronics and Electrical Communications Engineering DepartmentFaculty of Electronic Engineering, Menoufia UniversityMenoufEgypt
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23
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Ren Z, Chang Y, Bartl-Pokorny KD, Pokorny FB, Schuller BW. The Acoustic Dissection of Cough: Diving Into Machine Listening-based COVID-19 Analysis and Detection. J Voice 2022:S0892-1997(22)00166-7. [PMID: 35835648 PMCID: PMC9197794 DOI: 10.1016/j.jvoice.2022.06.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 05/25/2022] [Accepted: 06/09/2022] [Indexed: 12/05/2022]
Abstract
OBJECTIVES The coronavirus disease 2019 (COVID-19) has caused a crisis worldwide. Amounts of efforts have been made to prevent and control COVID-19's transmission, from early screenings to vaccinations and treatments. Recently, due to the spring up of many automatic disease recognition applications based on machine listening techniques, it would be fast and cheap to detect COVID-19 from recordings of cough, a key symptom of COVID-19. To date, knowledge of the acoustic characteristics of COVID-19 cough sounds is limited but would be essential for structuring effective and robust machine learning models. The present study aims to explore acoustic features for distinguishing COVID-19 positive individuals from COVID-19 negative ones based on their cough sounds. METHODS By applying conventional inferential statistics, we analyze the acoustic correlates of COVID-19 cough sounds based on the ComParE feature set, i.e., a standardized set of 6,373 acoustic higher-level features. Furthermore, we train automatic COVID-19 detection models with machine learning methods and explore the latent features by evaluating the contribution of all features to the COVID-19 status predictions. RESULTS The experimental results demonstrate that a set of acoustic parameters of cough sounds, e.g., statistical functionals of the root mean square energy and Mel-frequency cepstral coefficients, bear essential acoustic information in terms of effect sizes for the differentiation between COVID-19 positive and COVID-19 negative cough samples. Our general automatic COVID-19 detection model performs significantly above chance level, i.e., at an unweighted average recall (UAR) of 0.632, on a data set consisting of 1,411 cough samples (COVID-19 positive/negative: 210/1,201). CONCLUSIONS Based on the acoustic correlates analysis on the ComParE feature set and the feature analysis in the effective COVID-19 detection approach, we find that several acoustic features that show higher effects in conventional group difference testing are also higher weighted in the machine learning models.
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Affiliation(s)
- Zhao Ren
- EIHW - Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany; L3S Research Center, Hannover, Germany.
| | - Yi Chang
- GLAM - Group on Language, Audio, & Music, Imperial College London, London, United Kingdom
| | - Katrin D Bartl-Pokorny
- EIHW - Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany; Division of Phoniatrics, Medical University of Graz, Graz, Austria; Division of Physiology, Medical University of Graz, Graz, Austria.
| | - Florian B Pokorny
- EIHW - Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany; Division of Phoniatrics, Medical University of Graz, Graz, Austria; Division of Physiology, Medical University of Graz, Graz, Austria
| | - Björn W Schuller
- EIHW - Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany; GLAM - Group on Language, Audio, & Music, Imperial College London, London, United Kingdom
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24
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Chowdhury NK, Kabir MA, Rahman MM, Islam SMS. Machine learning for detecting COVID-19 from cough sounds: An ensemble-based MCDM method. Comput Biol Med 2022; 145:105405. [PMID: 35318171 PMCID: PMC8926945 DOI: 10.1016/j.compbiomed.2022.105405] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Revised: 03/10/2022] [Accepted: 03/11/2022] [Indexed: 12/16/2022]
Abstract
This research aims to analyze the performance of state-of-the-art machine learning techniques for classifying COVID-19 from cough sounds and to identify the model(s) that consistently perform well across different cough datasets. Different performance evaluation metrics (precision, sensitivity, specificity, AUC, accuracy, etc.) make selecting the best performance model difficult. To address this issue, in this paper, we propose an ensemble-based multi-criteria decision making (MCDM) method for selecting top performance machine learning technique(s) for COVID-19 cough classification. We use four cough datasets, namely Cambridge, Coswara, Virufy, and NoCoCoDa to verify the proposed method. At first, our proposed method uses the audio features of cough samples and then applies machine learning (ML) techniques to classify them as COVID-19 or non-COVID-19. Then, we consider a multi-criteria decision-making (MCDM) method that combines ensemble technologies (i.e., soft and hard) to select the best model. In MCDM, we use the technique for order preference by similarity to ideal solution (TOPSIS) for ranking purposes, while entropy is applied to calculate evaluation criteria weights. In addition, we apply the feature reduction process through recursive feature elimination with cross-validation under different estimators. The results of our empirical evaluations show that the proposed method outperforms the state-of-the-art models. We see that when the proposed method is used for analysis using the Extra-Trees classifier, it has achieved promising results (AUC: 0.95, Precision: 1, Recall: 0.97).
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Affiliation(s)
- Nihad Karim Chowdhury
- Department of Computer Science and Engineering, University of Chittagong, Bangladesh,Corresponding author
| | - Muhammad Ashad Kabir
- Data Science Research Unit, School of Computing, Mathematics and Engineering, Charles Sturt University, NSW, Australia
| | - Md. Muhtadir Rahman
- Department of Computer Science and Engineering, University of Chittagong, Bangladesh
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25
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Santosh KC, Rasmussen N, Mamun M, Aryal S. A systematic review on cough sound analysis for Covid-19 diagnosis and screening: is my cough sound COVID-19? PeerJ Comput Sci 2022; 8:e958. [PMID: 35634112 PMCID: PMC9138020 DOI: 10.7717/peerj-cs.958] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 04/04/2022] [Indexed: 06/15/2023]
Abstract
For COVID-19, the need for robust, inexpensive, and accessible screening becomes critical. Even though symptoms present differently, cough is still taken as one of the primary symptoms in severe and non-severe infections alike. For mass screening in resource-constrained regions, artificial intelligence (AI)-guided tools have progressively contributed to detect/screen COVID-19 infections using cough sounds. Therefore, in this article, we review state-of-the-art works in both years 2020 and 2021 by considering AI-guided tools to analyze cough sound for COVID-19 screening primarily based on machine learning algorithms. In our study, we used PubMed central repository and Web of Science with key words: (Cough OR Cough Sounds OR Speech) AND (Machine learning OR Deep learning OR Artificial intelligence) AND (COVID-19 OR Coronavirus). For better meta-analysis, we screened for appropriate dataset (size and source), algorithmic factors (both shallow learning and deep learning models) and corresponding performance scores. Further, in order not to miss up-to-date experimental research-based articles, we also included articles outside of PubMed and Web of Science, but pre-print articles were strictly avoided as they are not peer-reviewed.
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Affiliation(s)
- KC Santosh
- 2AI: Applied Artificial Intelligence Lab, Computer Science, University of South Dakota, Vermiillion, South Dakota, United States
| | - Nicholas Rasmussen
- 2AI: Applied Artificial Intelligence Lab, Computer Science, University of South Dakota, Vermiillion, South Dakota, United States
| | - Muntasir Mamun
- 2AI: Applied Artificial Intelligence Lab, Computer Science, University of South Dakota, Vermiillion, South Dakota, United States
| | - Sunil Aryal
- School of Information Technology, Deakin University, Victoria, Australia
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26
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Serrurier A, Neuschaefer-Rube C, Röhrig R. Past and Trends in Cough Sound Acquisition, Automatic Detection and Automatic Classification: A Comparative Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:2896. [PMID: 35458885 PMCID: PMC9027375 DOI: 10.3390/s22082896] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 04/07/2022] [Accepted: 04/08/2022] [Indexed: 11/16/2022]
Abstract
Cough is a very common symptom and the most frequent reason for seeking medical advice. Optimized care goes inevitably through an adapted recording of this symptom and automatic processing. This study provides an updated exhaustive quantitative review of the field of cough sound acquisition, automatic detection in longer audio sequences and automatic classification of the nature or disease. Related studies were analyzed and metrics extracted and processed to create a quantitative characterization of the state-of-the-art and trends. A list of objective criteria was established to select a subset of the most complete detection studies in the perspective of deployment in clinical practice. One hundred and forty-four studies were short-listed, and a picture of the state-of-the-art technology is drawn. The trend shows an increasing number of classification studies, an increase of the dataset size, in part from crowdsourcing, a rapid increase of COVID-19 studies, the prevalence of smartphones and wearable sensors for the acquisition, and a rapid expansion of deep learning. Finally, a subset of 12 detection studies is identified as the most complete ones. An unequaled quantitative overview is presented. The field shows a remarkable dynamic, boosted by the research on COVID-19 diagnosis, and a perfect adaptation to mobile health.
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Affiliation(s)
- Antoine Serrurier
- Institute of Medical Informatics, University Hospital of the RWTH Aachen, 52057 Aachen, Germany;
- Clinic for Phoniatrics, Pedaudiology & Communication Disorders, University Hospital of the RWTH Aachen, 52057 Aachen, Germany;
| | - Christiane Neuschaefer-Rube
- Clinic for Phoniatrics, Pedaudiology & Communication Disorders, University Hospital of the RWTH Aachen, 52057 Aachen, Germany;
| | - Rainer Röhrig
- Institute of Medical Informatics, University Hospital of the RWTH Aachen, 52057 Aachen, Germany;
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27
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Rahman T, Ibtehaz N, Khandakar A, Hossain MSA, Mekki YMS, Ezeddin M, Bhuiyan EH, Ayari MA, Tahir A, Qiblawey Y, Mahmud S, Zughaier SM, Abbas T, Al-Maadeed S, Chowdhury MEH. QUCoughScope: An Intelligent Application to Detect COVID-19 Patients Using Cough and Breath Sounds. Diagnostics (Basel) 2022; 12:920. [PMID: 35453968 PMCID: PMC9028864 DOI: 10.3390/diagnostics12040920] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 02/17/2022] [Accepted: 02/28/2022] [Indexed: 11/17/2022] Open
Abstract
Problem-Since the outbreak of the COVID-19 pandemic, mass testing has become essential to reduce the spread of the virus. Several recent studies suggest that a significant number of COVID-19 patients display no physical symptoms whatsoever. Therefore, it is unlikely that these patients will undergo COVID-19 testing, which increases their chances of unintentionally spreading the virus. Currently, the primary diagnostic tool to detect COVID-19 is a reverse-transcription polymerase chain reaction (RT-PCR) test from the respiratory specimens of the suspected patient, which is invasive and a resource-dependent technique. It is evident from recent researches that asymptomatic COVID-19 patients cough and breathe in a different way than healthy people. Aim-This paper aims to use a novel machine learning approach to detect COVID-19 (symptomatic and asymptomatic) patients from the convenience of their homes so that they do not overburden the healthcare system and also do not spread the virus unknowingly by continuously monitoring themselves. Method-A Cambridge University research group shared such a dataset of cough and breath sound samples from 582 healthy and 141 COVID-19 patients. Among the COVID-19 patients, 87 were asymptomatic while 54 were symptomatic (had a dry or wet cough). In addition to the available dataset, the proposed work deployed a real-time deep learning-based backend server with a web application to crowdsource cough and breath datasets and also screen for COVID-19 infection from the comfort of the user's home. The collected dataset includes data from 245 healthy individuals and 78 asymptomatic and 18 symptomatic COVID-19 patients. Users can simply use the application from any web browser without installation and enter their symptoms, record audio clips of their cough and breath sounds, and upload the data anonymously. Two different pipelines for screening were developed based on the symptoms reported by the users: asymptomatic and symptomatic. An innovative and novel stacking CNN model was developed using three base learners from of eight state-of-the-art deep learning CNN algorithms. The stacking CNN model is based on a logistic regression classifier meta-learner that uses the spectrograms generated from the breath and cough sounds of symptomatic and asymptomatic patients as input using the combined (Cambridge and collected) dataset. Results-The stacking model outperformed the other eight CNN networks with the best classification performance for binary classification using cough sound spectrogram images. The accuracy, sensitivity, and specificity for symptomatic and asymptomatic patients were 96.5%, 96.42%, and 95.47% and 98.85%, 97.01%, and 99.6%, respectively. For breath sound spectrogram images, the metrics for binary classification of symptomatic and asymptomatic patients were 91.03%, 88.9%, and 91.5% and 80.01%, 72.04%, and 82.67%, respectively. Conclusion-The web-application QUCoughScope records coughing and breathing sounds, converts them to a spectrogram, and applies the best-performing machine learning model to classify the COVID-19 patients and healthy subjects. The result is then reported back to the test user in the application interface. Therefore, this novel system can be used by patients in their premises as a pre-screening method to aid COVID-19 diagnosis by prioritizing the patients for RT-PCR testing and thereby reducing the risk of spreading of the disease.
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Affiliation(s)
- Tawsifur Rahman
- Electrical Engineering Department, College of Engineering, Qatar University, Doha 2713, Qatar; (T.R.); (N.I.); (A.K.); (M.S.A.H.); (M.E.); (A.T.); (Y.Q.); (S.M.)
| | - Nabil Ibtehaz
- Electrical Engineering Department, College of Engineering, Qatar University, Doha 2713, Qatar; (T.R.); (N.I.); (A.K.); (M.S.A.H.); (M.E.); (A.T.); (Y.Q.); (S.M.)
| | - Amith Khandakar
- Electrical Engineering Department, College of Engineering, Qatar University, Doha 2713, Qatar; (T.R.); (N.I.); (A.K.); (M.S.A.H.); (M.E.); (A.T.); (Y.Q.); (S.M.)
| | - Md Sakib Abrar Hossain
- Electrical Engineering Department, College of Engineering, Qatar University, Doha 2713, Qatar; (T.R.); (N.I.); (A.K.); (M.S.A.H.); (M.E.); (A.T.); (Y.Q.); (S.M.)
| | | | - Maymouna Ezeddin
- Electrical Engineering Department, College of Engineering, Qatar University, Doha 2713, Qatar; (T.R.); (N.I.); (A.K.); (M.S.A.H.); (M.E.); (A.T.); (Y.Q.); (S.M.)
| | - Enamul Haque Bhuiyan
- BioMedical Engineering and Imaging Institute (BMEII), Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA;
| | - Mohamed Arselene Ayari
- Department of Civil Engineering, College of Engineering, Qatar University, Doha 2713, Qatar;
| | - Anas Tahir
- Electrical Engineering Department, College of Engineering, Qatar University, Doha 2713, Qatar; (T.R.); (N.I.); (A.K.); (M.S.A.H.); (M.E.); (A.T.); (Y.Q.); (S.M.)
| | - Yazan Qiblawey
- Electrical Engineering Department, College of Engineering, Qatar University, Doha 2713, Qatar; (T.R.); (N.I.); (A.K.); (M.S.A.H.); (M.E.); (A.T.); (Y.Q.); (S.M.)
| | - Sakib Mahmud
- Electrical Engineering Department, College of Engineering, Qatar University, Doha 2713, Qatar; (T.R.); (N.I.); (A.K.); (M.S.A.H.); (M.E.); (A.T.); (Y.Q.); (S.M.)
| | - Susu M. Zughaier
- College of Medicine, Qatar University, Doha 2713, Qatar; (Y.M.S.M.); (S.M.Z.)
| | - Tariq Abbas
- Urology Division, Surgery Department, Sidra Medicine, Doha 26999, Qatar;
| | - Somaya Al-Maadeed
- Department of Computer Science and Engineering, College of Engineering, Qatar University, Doha 2713, Qatar;
| | - Muhammad E. H. Chowdhury
- Electrical Engineering Department, College of Engineering, Qatar University, Doha 2713, Qatar; (T.R.); (N.I.); (A.K.); (M.S.A.H.); (M.E.); (A.T.); (Y.Q.); (S.M.)
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Anand S, Sharma V, Pourush R, Jaiswal S. A comprehensive survey on the biomedical signal processing methods for the detection of COVID-19. Ann Med Surg (Lond) 2022; 76:103519. [PMID: 35401978 PMCID: PMC8975609 DOI: 10.1016/j.amsu.2022.103519] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 03/09/2022] [Accepted: 03/26/2022] [Indexed: 12/16/2022] Open
Abstract
The novel coronavirus, renamed SARS-CoV-2 and most commonly referred to as COVID-19, has infected nearly 44.83 million people in 224 countries and has been designated SARS-CoV-2. In this study, we used 'web of Science', 'Scopus' and 'goggle scholar' with the keywords of "SARS-CoV-2 detection" or "coronavirus 2019 detection" or "COVID 2019 detection" or "COVID 19 detection" "corona virus techniques for detection of COVID-19", "audio techniques for detection of COVID-19", "speech techniques for detection of COVID-19", for period of 2019-2021. Some COVID-19 instances have an impact on speech production, which suggests that researchers should look for signs of disease detection in speech utilising audio and speech recognition signals from humans to better understand the condition. It is presented in this review that an overview of human audio signals is presented using an AI (Artificial Intelligence) model to diagnose, spread awareness, and monitor COVID-19, employing bio and non-obtrusive signals that communicated human speech and non-speech audio information is presented. Development of accurate and rapid screening techniques that permit testing at a reasonable cost is critical in the current COVID-19 pandemic crisis, according to the World Health Organization. In this context, certain existing investigations have shown potential in the detection of COVID 19 diagnostic signals from relevant auditory noises, which is a promising development. According to authors, it is not a single "perfect" COVID-19 test that is required, but rather a combination of rapid and affordable tests, non-clinic pre-screening tools, and tools from a variety of supply chains and technologies that will allow us to safely return to our normal lives while we await the completion of the hassle free COVID-19 vaccination process for all ages. This review was able to gather information on biomedical signal processing in the detection of speech, coughing sounds, and breathing signals for the purpose of diagnosing and screening the COVID-19 virus.
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Affiliation(s)
- Satyajit Anand
- Electronics and Communication Engineering, Mody University of Science and Technology, India
| | - Vikrant Sharma
- Mechanical Engineering, Mody University of Science and Technology, India
| | - Rajeev Pourush
- Electronics and Communication Engineering, Mody University of Science and Technology, India
| | - Sandeep Jaiswal
- Biomedical Engineering, Mody University of Science and Technology, India
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29
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Roland T, Böck C, Tschoellitsch T, Maletzky A, Hochreiter S, Meier J, Klambauer G. Domain Shifts in Machine Learning Based Covid-19 Diagnosis From Blood Tests. J Med Syst 2022. [PMID: 35348909 DOI: 10.1101/2021.04.06.21254997] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Many previous studies claim to have developed machine learning models that diagnose COVID-19 from blood tests. However, we hypothesize that changes in the underlying distribution of the data, so called domain shifts, affect the predictive performance and reliability and are a reason for the failure of such machine learning models in clinical application. Domain shifts can be caused, e.g., by changes in the disease prevalence (spreading or tested population), by refined RT-PCR testing procedures (way of taking samples, laboratory procedures), or by virus mutations. Therefore, machine learning models for diagnosing COVID-19 or other diseases may not be reliable and degrade in performance over time. We investigate whether domain shifts are present in COVID-19 datasets and how they affect machine learning methods. We further set out to estimate the mortality risk based on routinely acquired blood tests in a hospital setting throughout pandemics and under domain shifts. We reveal domain shifts by evaluating the models on a large-scale dataset with different assessment strategies, such as temporal validation. We present the novel finding that domain shifts strongly affect machine learning models for COVID-19 diagnosis and deteriorate their predictive performance and credibility. Therefore, frequent re-training and re-assessment are indispensable for robust models enabling clinical utility.
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Affiliation(s)
- Theresa Roland
- ELLIS Unit Linz, LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria.
| | - Carl Böck
- Department of Anesthesiology and Critical Care Medicine, Kepler University Hospital GmbH, Johannes Kepler University Linz, Linz, Austria
| | - Thomas Tschoellitsch
- Department of Anesthesiology and Critical Care Medicine, Kepler University Hospital GmbH, Johannes Kepler University Linz, Linz, Austria
| | | | - Sepp Hochreiter
- ELLIS Unit Linz, LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria
| | - Jens Meier
- Department of Anesthesiology and Critical Care Medicine, Kepler University Hospital GmbH, Johannes Kepler University Linz, Linz, Austria
| | - Günter Klambauer
- ELLIS Unit Linz, LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria
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30
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Roland T, Böck C, Tschoellitsch T, Maletzky A, Hochreiter S, Meier J, Klambauer G. Domain Shifts in Machine Learning Based Covid-19 Diagnosis From Blood Tests. J Med Syst 2022; 46:23. [PMID: 35348909 PMCID: PMC8960704 DOI: 10.1007/s10916-022-01807-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 02/10/2022] [Indexed: 12/23/2022]
Abstract
AbstractMany previous studies claim to have developed machine learning models that diagnose COVID-19 from blood tests. However, we hypothesize that changes in the underlying distribution of the data, so called domain shifts, affect the predictive performance and reliability and are a reason for the failure of such machine learning models in clinical application. Domain shifts can be caused, e.g., by changes in the disease prevalence (spreading or tested population), by refined RT-PCR testing procedures (way of taking samples, laboratory procedures), or by virus mutations. Therefore, machine learning models for diagnosing COVID-19 or other diseases may not be reliable and degrade in performance over time. We investigate whether domain shifts are present in COVID-19 datasets and how they affect machine learning methods. We further set out to estimate the mortality risk based on routinely acquired blood tests in a hospital setting throughout pandemics and under domain shifts. We reveal domain shifts by evaluating the models on a large-scale dataset with different assessment strategies, such as temporal validation. We present the novel finding that domain shifts strongly affect machine learning models for COVID-19 diagnosis and deteriorate their predictive performance and credibility. Therefore, frequent re-training and re-assessment are indispensable for robust models enabling clinical utility.
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Affiliation(s)
- Theresa Roland
- ELLIS Unit Linz, LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria.
| | - Carl Böck
- Department of Anesthesiology and Critical Care Medicine, Kepler University Hospital GmbH, Johannes Kepler University Linz, Linz, Austria
| | - Thomas Tschoellitsch
- Department of Anesthesiology and Critical Care Medicine, Kepler University Hospital GmbH, Johannes Kepler University Linz, Linz, Austria
| | | | - Sepp Hochreiter
- ELLIS Unit Linz, LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria
| | - Jens Meier
- Department of Anesthesiology and Critical Care Medicine, Kepler University Hospital GmbH, Johannes Kepler University Linz, Linz, Austria
| | - Günter Klambauer
- ELLIS Unit Linz, LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria
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Husain M, Simpkin A, Gibbons C, Talkar T, Low D, Bonato P, Ghosh SS, Quatieri T, O'Keeffe DT. Artificial Intelligence for Detecting COVID-19 With the Aid of Human Cough, Breathing and Speech Signals: Scoping Review. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2022; 3:235-241. [PMID: 36819937 PMCID: PMC9933914 DOI: 10.1109/ojemb.2022.3143688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 11/30/2021] [Accepted: 12/26/2021] [Indexed: 09/01/2023] Open
Abstract
Goal: Official tests for COVID-19 are time consuming, costly, can produce high false negatives, use up vital chemicals and may violate social distancing laws. Therefore, a fast and reliable additional solution using recordings of cough, breathing and speech data for preliminary screening may help alleviate these issues. Objective: This scoping review explores how Artificial Intelligence (AI) technology aims to detect COVID-19 disease by using cough, breathing and speech recordings, as reported in the literature. Here, we describe and summarize attributes of the identified AI techniques and datasets used for their implementation. Methods: A scoping review was conducted following the guidelines of PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). Electronic databases (Google Scholar, Science Direct, and IEEE Xplore) were searched between 1st April 2020 and 15th August 2021. Terms were selected based on the target intervention (i.e., AI), the target disease (i.e., COVID-19) and acoustic correlates of the disease (i.e., speech, breathing and cough). A narrative approach was used to summarize the extracted data. Results: 24 studies and 8 Apps out of the 86 retrieved studies met the inclusion criteria. Half of the publications and Apps were from the USA. The most prominent AI architecture used was a convolutional neural network, followed by a recurrent neural network. AI models were mainly trained, tested and run-on websites and personal computers, rather than on phone apps. More than half of the included studies reported area-under-the-curve performance of greater than 0.90 on symptomatic and negative datasets while one study achieved 100% sensitivity in predicting asymptomatic COVID-19 from cough-, breathing- or speech-based acoustic features. Conclusions: The included studies show that AI has the potential to help detect COVID-19 using cough, breathing and speech samples. The proposed methods (with some time and appropriate clinical testing) could prove to be an effective method in detecting various diseases related to respiratory and neurophysiological changes in the human body.
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Affiliation(s)
- Mouzzam Husain
- Health Innovation Via Engineering (HIVE) Lab, Curam, Lero, School of MedicineLambe Institute for Translational ResearchNational University of Ireland GalwayH91 TK33GalwayIreland
| | - Andrew Simpkin
- School of Mathematics, Statistics and Applied MathematicsNational University of IrelandH91 TK33GalwayIreland
| | - Claire Gibbons
- Health Innovation Via Engineering (HIVE) Lab, Curam, Lero, School of MedicineLambe Institute for Translational ResearchNational University of Ireland GalwayH91 TK33GalwayIreland
| | - Tanya Talkar
- MIT Lincoln LaboratoryLexingtonMA02421USA
- Program in Speech and Hearing Bioscience and TechnologyHarvard Medical SchoolBostonMA02115USA
| | - Daniel Low
- Program in Speech and Hearing Bioscience and TechnologyHarvard Medical SchoolBostonMA02115USA
- MIT McGovern Institute for Brain Research, CambridgeMA02139USA
| | - Paolo Bonato
- Department of Physical Medicine and RehabilitationHarvard Medical School, Spaulding Rehabilitation HospitalBostonMAUSA
| | - Satrajit S. Ghosh
- Program in Speech and Hearing Bioscience and TechnologyHarvard Medical SchoolBostonMA02115USA
- MIT McGovern Institute for Brain Research, CambridgeMA02139USA
| | - Thomas Quatieri
- MIT Lincoln LaboratoryLexingtonMA02421USA
- Program in Speech and Hearing Bioscience and TechnologyHarvard Medical SchoolBostonMA02115USA
| | - Derek T. O'Keeffe
- Health Innovation Via Engineering (HIVE) Lab, Curam, Lero, School of MedicineLambe Institute for Translational ResearchNational University of Ireland GalwayH91 TK33GalwayIreland
- University Hospital Galway, Saolta, Health Services ExecutiveIreland
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32
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Abstract
The highly contagious and rapidly mutating COVID-19 virus is affecting individuals worldwide. A rapid and large-scale method for COVID-19 testing is needed to prevent infection. Cough testing using AI has been shown to be potentially valuable. In this paper, we propose a COVID-19 diagnostic method based on an AI cough test. We used only crowdsourced cough sound data to distinguish between the cough sound of COVID-19-positive people and that of healthy people. First, we used the COUGHVID cough database to segment only the cough sound from the original cough data. An effective audio feature set was then extracted from the segmented cough sounds. A deep learning model was trained on the extracted feature set. The COVID-19 diagnostic system constructed using this method had a sensitivity of 93% and a specificity of 94%, and achieved better results than models trained by other existing methods.
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Ijaz A, Nabeel M, Masood U, Mahmood T, Hashmi MS, Posokhova I, Rizwan A, Imran A. Towards using cough for respiratory disease diagnosis by leveraging Artificial Intelligence: A survey. INFORMATICS IN MEDICINE UNLOCKED 2022. [DOI: 10.1016/j.imu.2021.100832] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
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34
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You M, Wang W, Li Y, Liu J, Xu X, Qiu Z. Automatic cough detection from realistic audio recordings using C-BiLSTM with boundary regression. Biomed Signal Process Control 2022; 72:103304. [PMID: 36569172 PMCID: PMC9760237 DOI: 10.1016/j.bspc.2021.103304] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Revised: 10/08/2021] [Accepted: 10/23/2021] [Indexed: 12/27/2022]
Abstract
Automatic cough detection in the patients' realistic audio recordings is of great significance to diagnose and monitor respiratory diseases, such as COVID-19. Many detection methods have been developed so far, but they are still unable to meet the practical requirements. In this paper, we present a deep convolutional bidirectional long short-term memory (C-BiLSTM) model with boundary regression for cough detection, where cough and non-cough parts need to be classified and located. We added convolutional layers before the LSTM to enhance the cough features and preserve the temporal information of the audio data. Considering the importance of the cough event integrity for subsequent analysis, the novel model includes an embedded boundary regression on the last feature map for both higher detection accuracy and more accurate boundaries. We delicately designed, collected and labelled a realistic audio dataset containing recordings of patients with respiratory diseases, named the Corp Dataset. 168 h of recordings with 9969 coughs from 42 different patients are included. The dataset is published online on the MARI Lab website (https://mari.tongji.edu.cn/info/1012/1030.htm). The results show that the system achieves a sensitivity of 84.13%, a specificity of 99.82% and an intersection-over-union (IoU) of 0.89, which is significantly superior to other related models. With the proposed method, all the criteria on cough detection significantly increased. The open source Corp Dataset provides useful material and a benchmark for researchers investigating cough detection. We propose the state-of-the-art system with boundary regression, laying the foundation for identifying cough sounds in real-world audio data.
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Affiliation(s)
- Mingyu You
- Department of Control Science and Engineering, Tongji University, Shanghai, China,Frontiers Science Center for Intelligent Autonomous Systems, Shanghai, China,Corresponding author at: Department of Control Science and Engineering, Tongji University, Shanghai, China
| | - Weihao Wang
- Department of Control Science and Engineering, Tongji University, Shanghai, China
| | - You Li
- Department of Control Science and Engineering, Tongji University, Shanghai, China
| | - Jiaming Liu
- Department of Computer Vision Technology (VIS), Baidu Inc, Beijing, China
| | - Xianghuai Xu
- Tongji Hospital of Tongji University, Shanghai, China
| | - Zhongmin Qiu
- Tongji Hospital of Tongji University, Shanghai, China
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Alkhodari M, Khandoker AH. Detection of COVID-19 in smartphone-based breathing recordings: A pre-screening deep learning tool. PLoS One 2022; 17:e0262448. [PMID: 35025945 PMCID: PMC8758005 DOI: 10.1371/journal.pone.0262448] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 12/24/2021] [Indexed: 12/14/2022] Open
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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Affiliation(s)
- Mohanad Alkhodari
- Healthcare Engineering Innovation Center (HEIC), Department of Biomedical Engineering, Khalifa University, Abu Dhabi, UAE
| | - Ahsan H. Khandoker
- Healthcare Engineering Innovation Center (HEIC), Department of Biomedical Engineering, Khalifa University, Abu Dhabi, UAE
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Islam R, Abdel-Raheem E, Tarique M. A study of using cough sounds and deep neural networks for the early detection of Covid-19. BIOMEDICAL ENGINEERING ADVANCES 2022; 3:100025. [PMID: 35013733 PMCID: PMC8732907 DOI: 10.1016/j.bea.2022.100025] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 12/15/2021] [Accepted: 01/04/2022] [Indexed: 11/30/2022] Open
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.
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Affiliation(s)
- Rumana Islam
- Department of Electrical and Computer Engineering, University of Windsor, ON N9B 3P4, Canada
| | - Esam Abdel-Raheem
- Department of Electrical and Computer Engineering, University of Windsor, ON N9B 3P4, Canada
| | - Mohammed Tarique
- Department of Electrical Engineering, University of Science and Technology of Fujairah, P.O. Box 2202, UAE
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Keen EM, True EJ, Summers AR, Smith EC, Brew J, Grandjean Lapierre S. High-throughput digital cough recording on a university campus: A SARS-CoV-2-negative curated open database and operational template for acoustic screening of respiratory diseases. Digit Health 2022; 8:20552076221097513. [PMID: 35558638 PMCID: PMC9087241 DOI: 10.1177/20552076221097513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 04/12/2022] [Indexed: 11/16/2022] Open
Abstract
Objective Respiratory illnesses have information-rich acoustic biomarkers, such as cough, that
can potentially play an important role in screening populations for disease risk. To
realize that potential, datasets of paired acoustic-clinical samples are needed for the
development and validation of acoustic screening models, and protocols for collecting
acoustic samples must be efficient and safe. We collected cough acoustic signatures at a
high-throughput SARS-CoV-2 testing site on a college campus. Here, we share logistical
details and the dataset of acoustic cough signatures paired with the gold standard in
SARS-CoV-2 testing of SARS-CoV-2 genomic sequences using qRT-PCR. Methods Cough recordings were collected in winter-spring 2021 at a rural residential college
(Sewanee, TN, USA), where approximately 2000 students were tested for SARS-CoV-2 on a
weekly basis. Cough collection was managed by student volunteers using custom
software. Results 4302 coughs were recorded from 960 participants over 11 weeks. All coughs were COVID-19
negative. Approximately 30 s were required to check-in a participant and collect their
cough. Conclusion The value of acoustic screening tools depends upon our ability to develop and implement
them reliably and quickly. For that to happen, high-quality datasets and logistical
insights must be collected and shared on an ongoing basis.
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Affiliation(s)
- Eric M. Keen
- Sewanee: The University of the South, Sewanee, TN, USA
- Hyfe, Inc., Wilmington, DE, USA
| | - Emily J. True
- Sewanee: The University of the South, Sewanee, TN, USA
| | | | | | | | - Simon Grandjean Lapierre
- Department of Microbiology, Infectious Diseases and Immunology, Université de Montréal, Montréal, Québec, Canada
- Immunopathology Axis, Centre de Recherche du Centre Hospitalier de l’Université de Montréal, Montréal, Québec, Canada
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Kang HS, Lee EG, Kim CK, Jung A, Song C, Im S. Cough Sounds Recorded via Smart Devices as Useful Non-Invasive Digital Biomarkers of Aspiration Risk: A Case Report. SENSORS 2021; 21:s21238056. [PMID: 34884059 PMCID: PMC8659921 DOI: 10.3390/s21238056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 11/26/2021] [Accepted: 11/30/2021] [Indexed: 11/26/2022]
Abstract
Spirometer measurements can reflect cough strength but might not be routinely available for patients with severe neurological or medical conditions. A digital device that can record and help track abnormal cough sound changes serially in a noninvasive but reliable manner would be beneficial for monitoring such individuals. This report includes two cases of respiratory distress whose cough changes were monitored via assessments performed using recordings made with a digital device. The cough sounds were recorded using an iPad (Apple, Cupertino, CA, USA) through an embedded microphone. Cough sounds were recorded at the bedside, with no additional special equipment. The two patients were able to complete the recordings with no complications. The maximum root mean square values obtained from the cough sounds were significantly reduced when both cases were diagnosed with aspiration pneumonia. In contrast, higher values became apparent when the patients demonstrated a less severe status. Based on an analysis of our two cases, the patients’ cough sounds recorded with a commercial digital device show promise as potential digital biomarkers that may reflect aspiration risk related to attenuated cough force. Serial monitoring aided the decision making to resume oral feeding. Future studies should further explore the clinical utility of this technique.
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Affiliation(s)
- Hye-Seon Kang
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Bucheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 14647, Korea; (H.-S.K.); (E.-G.L.)
| | - Eung-Gu Lee
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Bucheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 14647, Korea; (H.-S.K.); (E.-G.L.)
| | - Cheol-Ki Kim
- Department of Rehabilitation Medicine, Bucheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 14647, Korea;
| | - Andy Jung
- Soundable Health, Inc., San Francisco, CA 94105, USA; (A.J.); (C.S.)
| | - Catherine Song
- Soundable Health, Inc., San Francisco, CA 94105, USA; (A.J.); (C.S.)
| | - Sun Im
- Department of Rehabilitation Medicine, Bucheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 14647, Korea;
- Correspondence: or ; Tel.: +82-32-340-2170
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Chang Z, Zhan Z, Zhao Z, You Z, Liu Y, Yan Z, Fu Y, Liang W, Zhao L. Application of artificial intelligence in COVID-19 medical area: a systematic review. J Thorac Dis 2021; 13:7034-7053. [PMID: 35070385 PMCID: PMC8743418 DOI: 10.21037/jtd-21-747] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 09/02/2021] [Indexed: 01/08/2023]
Abstract
BACKGROUND Coronavirus disease 2019 (COVID-19) has caused a large-scale global epidemic, impacting international politics and the economy. At present, there is no particularly effective medicine and treatment plan. Therefore, it is urgent and significant to find new technologies to diagnose early, isolate early, and treat early. Multimodal data drove artificial intelligence (AI) can potentially be the option. During the COVID-19 Pandemic, AI provided cutting-edge applications in disease, medicine, treatment, and target recognition. This paper reviewed the literature on the intersection of AI and medicine to analyze and compare different AI model applications in the COVID-19 Pandemic, evaluate their effectiveness, show their advantages and differences, and introduce the main models and their characteristics. METHODS We searched PubMed, arXiv, medRxiv, and Google Scholar through February 2020 to identify studies on AI applications in the medical areas for the COVID-19 Pandemic. RESULTS We summarize the main AI applications in six areas: (I) epidemiology, (II) diagnosis, (III) progression, (IV) treatment, (V) psychological health impact, and (VI) data security. The ongoing development in AI has significantly improved prediction, contact tracing, screening, diagnosis, treatment, medication, and vaccine development for the COVID-19 Pandemic and reducing human intervention in medical practice. DISCUSSION This paper provides strong advice for using AI-based auxiliary tools for related applications of human diseases. We also discuss the clinicians' role in the further development of AI. They and AI researchers can integrate AI technology with current clinical processes and information systems into applications. In the future, AI personnel and medical workers will further cooperate closely.
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Affiliation(s)
- Zhoulin Chang
- College of Mechanical and Electrical Engineering, Guangdong University of Science and Technology, Dongguan, China
| | - Zhiqing Zhan
- The Third Clinical College, Guangzhou Medical University, Guangzhou, China
| | - Zifan Zhao
- Nanshan College, Guangzhou Medical University, Guangzhou, China
| | - Zhixuan You
- Nanshan College, Guangzhou Medical University, Guangzhou, China
| | - Yang Liu
- School of Information Engineering, Zhengzhou University, Zhengzhou, China
| | - Zhihong Yan
- Kuangji Medical Technology (Guangdong Hengqin) Co., Ltd., Zhuhai, China
| | - Yong Fu
- Kuangji Medical Technology (Guangdong Hengqin) Co., Ltd., Zhuhai, China
| | - Wenhua Liang
- Department of Thoracic Surgery, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Lei Zhao
- Department of Physiology, School of Basic Medical Sciences, Guangzhou Medical University, Guangzhou, China
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Despotovic V, Ismael M, Cornil M, Call RM, Fagherazzi G. Detection of COVID-19 from voice, cough and breathing patterns: Dataset and preliminary results. Comput Biol Med 2021; 138:104944. [PMID: 34656870 PMCID: PMC8513517 DOI: 10.1016/j.compbiomed.2021.104944] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 10/04/2021] [Accepted: 10/11/2021] [Indexed: 12/16/2022]
Abstract
COVID-19 heavily affects breathing and voice and causes symptoms that make patients' voices distinctive, creating recognizable audio signatures. Initial studies have already suggested the potential of using voice as a screening solution. In this article we present a dataset of voice, cough and breathing audio recordings collected from individuals infected by SARS-CoV-2 virus, as well as non-infected subjects via large scale crowdsourced campaign. We describe preliminary results for detection of COVID-19 from cough patterns using standard acoustic features sets, wavelet scattering features and deep audio embeddings extracted from low-level feature representations (VGGish and OpenL3). Our models achieve accuracy of 88.52%, sensitivity of 88.75% and specificity of 90.87%, confirming the applicability of audio signatures to identify COVID-19 symptoms. We furthermore provide an in-depth analysis of the most informative acoustic features and try to elucidate the mechanisms that alter the acoustic characteristics of coughs of people with COVID-19.
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Affiliation(s)
- Vladimir Despotovic
- University of Luxembourg, Department of Computer Science, Esch-sur-Alzette, Luxembourg,Corresponding author
| | - Muhannad Ismael
- Luxembourg Institute of Science and Technology, IT for Innovation in Services Department, Esch-sur-Alzette, Luxembourg
| | - Maël Cornil
- Luxembourg Institute of Science and Technology, IT for Innovation in Services Department, Esch-sur-Alzette, Luxembourg
| | - Roderick Mc Call
- Luxembourg Institute of Science and Technology, IT for Innovation in Services Department, Esch-sur-Alzette, Luxembourg
| | - Guy Fagherazzi
- Luxembourg Institute of Health, Department of Population Health, Deep Digital Phenotyping Research Unit, Strassen, Luxembourg
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Kobat MA, Kivrak T, Barua PD, Tuncer T, Dogan S, Tan RS, Ciaccio EJ, Acharya UR. Automated COVID-19 and Heart Failure Detection Using DNA Pattern Technique with Cough Sounds. Diagnostics (Basel) 2021; 11:1962. [PMID: 34829308 PMCID: PMC8620352 DOI: 10.3390/diagnostics11111962] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 10/17/2021] [Accepted: 10/19/2021] [Indexed: 01/22/2023] Open
Abstract
COVID-19 and heart failure (HF) are common disorders and although they share some similar symptoms, they require different treatments. Accurate diagnosis of these disorders is crucial for disease management, including patient isolation to curb infection spread of COVID-19. In this work, we aim to develop a computer-aided diagnostic system that can accurately differentiate these three classes (normal, COVID-19 and HF) using cough sounds. A novel handcrafted model was used to classify COVID-19 vs. healthy (Case 1), HF vs. healthy (Case 2) and COVID-19 vs. HF vs. healthy (Case 3) automatically using deoxyribonucleic acid (DNA) patterns. The model was developed using the cough sounds collected from 241 COVID-19 patients, 244 HF patients, and 247 healthy subjects using a hand phone. To the best our knowledge, this is the first work to automatically classify healthy subjects, HF and COVID-19 patients using cough sounds signals. Our proposed model comprises a graph-based local feature generator (DNA pattern), an iterative maximum relevance minimum redundancy (ImRMR) iterative feature selector, with classification using the k-nearest neighbor classifier. Our proposed model attained an accuracy of 100.0%, 99.38%, and 99.49% for Case 1, Case 2, and Case 3, respectively. The developed system is completely automated and economical, and can be utilized to accurately detect COVID-19 versus HF using cough sounds.
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Affiliation(s)
- Mehmet Ali Kobat
- Department of Cardiology, Firat University Hospital, Firat University, Elazig 23119, Turkey; (M.A.K.); (T.K.)
| | - Tarik Kivrak
- Department of Cardiology, Firat University Hospital, Firat University, Elazig 23119, Turkey; (M.A.K.); (T.K.)
| | - Prabal Datta Barua
- School of Management & Enterprise, University of Southern Queensland, Toowoomba, QLD 4350, Australia;
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
- Cogninet Brain Team, Cogninet Australia, Sydney, NSW 2010, Australia
| | - Turker Tuncer
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig 23119, Turkey; (T.T.); (S.D.)
| | - Sengul Dogan
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig 23119, Turkey; (T.T.); (S.D.)
| | - Ru-San Tan
- Department of Cardiology, National Heart Centre Singapore, Singapore 169609, Singapore;
- Department of Cardiology, Duke-NUS Graduate Medical School, Singapore 169857, Singapore
| | - Edward J. Ciaccio
- Department of Medicine, Celiac Disease Center, Columbia University Irving Medical Center, New York, NY 10032, USA;
| | - U. Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
- Department of Biomedical Engineering, School of Science and Technology, SUSS University, Clementi 599494, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
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Vahedian-Azimi A, Keramatfar A, Asiaee M, Atashi SS, Nourbakhsh M. Do you have COVID-19? An artificial intelligence-based screening tool for COVID-19 using acoustic parameters. THE JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA 2021; 150:1945. [PMID: 34598596 PMCID: PMC8487069 DOI: 10.1121/10.0006104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 08/13/2021] [Accepted: 08/16/2021] [Indexed: 05/09/2023]
Abstract
This study aimed to develop an artificial intelligence (AI)-based tool for screening COVID-19 patients based on the acoustic parameters of their voices. Twenty-five acoustic parameters were extracted from voice samples of 203 COVID-19 patients and 171 healthy individuals who produced a sustained vowel, i.e., /a/, as long as they could after a deep breath. The selected acoustic parameters were from different categories including fundamental frequency and its perturbation, harmonicity, vocal tract function, airflow sufficiency, and periodicity. After the feature extraction, different machine learning methods were tested. A leave-one-subject-out validation scheme was used to tune the hyper-parameters and record the test set results. Then the models were compared based on their accuracy, precision, recall, and F1-score. Based on accuracy (89.71%), recall (91.63%), and F1-score (90.62%), the best model was the feedforward neural network (FFNN). Its precision function (89.63%) was a bit lower than the logistic regression (90.17%). Based on these results and confusion matrices, the FFNN model was employed in the software. This screening tool could be practically used at home and public places to ensure the health of each individual's respiratory system. If there are any related abnormalities in the test taker's voice, the tool recommends that they seek a medical consultant.
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Affiliation(s)
- Amir Vahedian-Azimi
- Trauma Research Center, Nursing Faculty, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | | | - Maral Asiaee
- Department of Linguistics, Faculty of Literature, Alzahra University, Tehran, Iran
| | - Seyed Shahab Atashi
- Food and Drug Control Department, Jundishapour University of Medical Sciences, Ahvaz, Iran
| | - Mandana Nourbakhsh
- Department of Linguistics, Faculty of Literature, Alzahra University, Tehran, Iran
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Erdoğan YE, Narin A. COVID-19 detection with traditional and deep features on cough acoustic signals. Comput Biol Med 2021; 136:104765. [PMID: 34416571 PMCID: PMC8364172 DOI: 10.1016/j.compbiomed.2021.104765] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Revised: 08/09/2021] [Accepted: 08/10/2021] [Indexed: 12/23/2022]
Abstract
The COVID-19 epidemic, in which millions of people suffer, has affected the whole world in a short time. This virus, which has a high rate of transmission, directly affects the respiratory system of people. While symptoms such as difficulty in breathing, cough, and fever are common, hospitalization and fatal consequences can be seen in progressive situations. For this reason, the most important issue in combating the epidemic is to detect COVID-19(+) early and isolate those with COVID-19(+) from other people. In addition to the RT-PCR test, those with COVID-19(+) can be detected with imaging methods. In this study, it was aimed to detect COVID-19(+) patients with cough acoustic data, which is one of the important symptoms. Based on these data, features were obtained from traditional feature extraction methods using empirical mode decomposition (EMD) and discrete wavelet transform (DWT). Deep features were also obtained using pre-trained ResNet50 and pre-trained MobileNet models. Feature selection was applied to all obtained features with the ReliefF algorithm. In this case, the highest 98.4% accuracy and 98.6% F1-score values were obtained by selecting the EMD + DWT features using ReliefF. In another study in which deep features were used, features obtained from ResNet50 and MobileNet using scalogram images were used. For the features selected using the ReliefF algorithm, the highest performance was found with support vector machines-cubic as 97.8% accuracy and 98.0% F1-score. It has been determined that the features obtained by traditional feature approaches show higher performance than deep features. Among the chaotic measurements, the approximate entropy measurement was determined to be the highest distinguishing feature. According to the results, a highly successful study is presented with cough acoustic data that can easily be obtained from mobile and computer-based applications. We anticipate that this study will be useful as a decision support system in this epidemic period, when it is important to correctly identify even one person.
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Affiliation(s)
- Yunus Emre Erdoğan
- Zonguldak Bulent Ecevit University, Faculty of Engineering, Department of Electrical and Electronics Engineering, Zonguldak, Turkey; Eregli Iron and Steel Works Co., Electronics Automation Department, Zonguldak, Turkey.
| | - Ali Narin
- Zonguldak Bulent Ecevit University, Faculty of Engineering, Department of Electrical and Electronics Engineering, Zonguldak, Turkey.
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Peyvandi A, Majidi B, Peyvandi S, Patra J. Computer-Aided-Diagnosis as a Service on Decentralized Medical Cloud for Efficient and Rapid Emergency Response Intelligence. NEW GENERATION COMPUTING 2021; 39:677-700. [PMID: 34219860 PMCID: PMC8236221 DOI: 10.1007/s00354-021-00131-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Accepted: 06/19/2021] [Indexed: 05/05/2023]
Abstract
The COVID-19 pandemic resulted in a significant increase in the workload for the emergency systems and healthcare providers all around the world. The emergency systems are dealing with large number of patients in various stages of deteriorating conditions which require significant medical expertise for accurate and rapid diagnosis and treatment. This issue will become more prominent in places with lack of medical experts and state-of-the-art clinical equipment, especially in developing countries. The machine intelligence aided medical diagnosis systems can provide rapid, dependable, autonomous, and low-cost solutions for medical diagnosis in emergency conditions. In this paper, a privacy-preserving computer-aided diagnosis (CAD) framework, called Decentralized deep Emergency response Intelligence (D-EI), which provides secure machine learning based medical diagnosis on the cloud is proposed. The proposed framework provides a blockchain based decentralized machine learning solution to aid the health providers with medical diagnosis in emergency conditions. The D-EI uses blockchain smart contracts to train the CAD machine learning models using all the data on the medical cloud while preserving the privacy of patients' records. Using the proposed framework, the data of each patient helps to increase the overall accuracy of the CAD model by balancing the diagnosis datasets with minority classes and special cases. As a case study, the D-EI is demonstrated as a solution for COVID-19 diagnosis. The D-EI framework can help in pandemic management by providing rapid and accurate diagnosis in overwhelming medical workload conditions.
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Affiliation(s)
| | - Babak Majidi
- Department of Computer Engineering, Khatam University, Tehran, Iran
- Emergency and Rapid Response Simulation (ADERSIM) Artificial Intelligence Group, Faculty of Liberal Arts and Professional Studies, York University, Toronto, Canada
| | - Soodeh Peyvandi
- Process Management and Business Intelligence, University of Applied Sciences Upper Austria, Steyr, Austria
| | - Jagdish Patra
- Faculty of Science, Engineering and Technology, Swinburne University of Technology, Melbourne, Australia
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