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Kapetanidis P, Kalioras F, Tsakonas C, Tzamalis P, Kontogiannis G, Karamanidou T, Stavropoulos TG, Nikoletseas S. Respiratory Diseases Diagnosis Using Audio Analysis and Artificial Intelligence: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2024; 24:1173. [PMID: 38400330 PMCID: PMC10893010 DOI: 10.3390/s24041173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 02/03/2024] [Accepted: 02/04/2024] [Indexed: 02/25/2024]
Abstract
Respiratory diseases represent a significant global burden, necessitating efficient diagnostic methods for timely intervention. Digital biomarkers based on audio, acoustics, and sound from the upper and lower respiratory system, as well as the voice, have emerged as valuable indicators of respiratory functionality. Recent advancements in machine learning (ML) algorithms offer promising avenues for the identification and diagnosis of respiratory diseases through the analysis and processing of such audio-based biomarkers. An ever-increasing number of studies employ ML techniques to extract meaningful information from audio biomarkers. Beyond disease identification, these studies explore diverse aspects such as the recognition of cough sounds amidst environmental noise, the analysis of respiratory sounds to detect respiratory symptoms like wheezes and crackles, as well as the analysis of the voice/speech for the evaluation of human voice abnormalities. To provide a more in-depth analysis, this review examines 75 relevant audio analysis studies across three distinct areas of concern based on respiratory diseases' symptoms: (a) cough detection, (b) lower respiratory symptoms identification, and (c) diagnostics from the voice and speech. Furthermore, publicly available datasets commonly utilized in this domain are presented. It is observed that research trends are influenced by the pandemic, with a surge in studies on COVID-19 diagnosis, mobile data acquisition, and remote diagnosis systems.
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Affiliation(s)
- Panagiotis Kapetanidis
- Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece (C.T.); (G.K.); (S.N.)
| | - Fotios Kalioras
- Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece (C.T.); (G.K.); (S.N.)
| | - Constantinos Tsakonas
- Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece (C.T.); (G.K.); (S.N.)
| | - Pantelis Tzamalis
- Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece (C.T.); (G.K.); (S.N.)
| | - George Kontogiannis
- Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece (C.T.); (G.K.); (S.N.)
| | - Theodora Karamanidou
- Pfizer Center for Digital Innovation, 55535 Thessaloniki, Greece; (T.K.); (T.G.S.)
| | | | - Sotiris Nikoletseas
- Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece (C.T.); (G.K.); (S.N.)
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Xu W, He G, Shen D, Xu B, Jiang P, Liu F, Lou X, Guo L, Ma L. A noval pulmonary function evaluation method based on ResNet50 + SVR model and cough. Sci Rep 2023; 13:22065. [PMID: 38087014 PMCID: PMC10716123 DOI: 10.1038/s41598-023-49334-4] [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: 08/07/2023] [Accepted: 12/07/2023] [Indexed: 12/18/2023] Open
Abstract
Traditionally, the clinical evaluation of respiratory diseases was pulmonary function testing, which can be used for the detection of severity and prognosis through pulmonary function parameters. However, this method is limited by the complex process, which is impossible for patients to monitor daily. In order to evaluate pulmonary function parameters conveniently with less time and location restrictions, cough sound is the substitute parameter. In this paper, 371 cough sounds segments from 150 individuals were separated into 309 and 62 as the training and test samples. Short-time Fourier transform (STFT) was applied to transform cough sound into spectrogram, and ResNet50 model was used to extract 2048-dimensional features. Through support vector regression (SVR) model with biological attributes, the data were regressed with pulmonary function parameters, FEV1, FEV1%, FEV1/FVC, FVC, FVC%, and the performance of this models was evaluated with fivefold cross-validation. Combines with deep learning and machine learning technologies, the better results in the case of small samples were achieved. Using the coefficient of determination (R2), the ResNet50 + SVR model shows best performance in five basic pulmonary function parameters evaluation as FEV1(0.94), FEV1%(0.84), FEV1/FVC(0.68), FVC(0.92), and FVC%(0.72). This ResNet50 + SVR hybrid model shows excellent evaluation of pulmonary function parameters during coughing, making it possible to realize a simple and rapid evaluation for pneumonia patients. The technology implemented in this paper is beneficial in judge the patient's condition, realize early screening of respiratory diseases, evaluate postoperative disease changes and detect respiratory infectious diseases without time and location restrictions.
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Affiliation(s)
- Wenlong Xu
- College of Information Engineering, China Jiliang University, Hangzhou, China
| | - Guoqiang He
- College of Information Engineering, China Jiliang University, Hangzhou, China
| | - Dan Shen
- The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Bingqiao Xu
- College of Information Engineering, China Jiliang University, Hangzhou, China
| | | | - Feng Liu
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia
| | - Xiaomin Lou
- Hangzhou Chest Hospital Affiliated, Zhejiang University Medical College, Hangzhou, China
| | - Lingling Guo
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, China.
| | - Li Ma
- Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, China.
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Pentakota P, Rudraraju G, Sripada NR, Mamidgi B, Gottipulla C, Jalukuru C, Palreddy SD, Bhoge NKR, Firmal P, Yechuri V, Jain M, Peddireddi VS, Bhimarasetty DM, Sreenivas S, Prasad K KL, Joshi N, Vijayan S, Turaga S, Avasarala V. Screening COVID-19 by Swaasa AI platform using cough sounds: a cross-sectional study. Sci Rep 2023; 13:18284. [PMID: 37880351 PMCID: PMC10600180 DOI: 10.1038/s41598-023-45104-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 10/16/2023] [Indexed: 10/27/2023] Open
Abstract
The Advent of Artificial Intelligence (AI) has led to the use of auditory data for detecting various diseases, including COVID-19. SARS-CoV-2 infection has claimed more than six million lives to date and therefore, needs a robust screening technique to control the disease spread. In the present study we created and validated the Swaasa AI platform, which uses the signature cough sound and symptoms presented by patients to screen and prioritize COVID-19 patients. We collected cough data from 234 COVID-19 suspects to validate our Convolutional Neural Network (CNN) architecture and Feedforward Artificial Neural Network (FFANN) (tabular features) based algorithm. The final output from both models was combined to predict the likelihood of having the disease. During the clinical validation phase, our model showed a 75.54% accuracy rate in detecting the likely presence of COVID-19, with 95.45% sensitivity and 73.46% specificity. We conducted pilot testing on 183 presumptive COVID subjects, of which 58 were truly COVID-19 positive, resulting in a Positive Predictive Value of 70.73%. Due to the high cost and technical expertise required for currently available rapid screening methods, there is a need for a cost-effective and remote monitoring tool that can serve as a preliminary screening method for potential COVID-19 subjects. Therefore, Swaasa would be highly beneficial in detecting the disease and could have a significant impact in reducing its spread.
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Affiliation(s)
| | | | | | | | | | - Charan Jalukuru
- Salcit Technologies, Jayabheri Silicon Towers, Hyderabad, India
| | | | | | - Priyanka Firmal
- Salcit Technologies, Jayabheri Silicon Towers, Hyderabad, India
| | - Venkat Yechuri
- Salcit Technologies, Jayabheri Silicon Towers, Hyderabad, India
| | - Manmohan Jain
- Salcit Technologies, Jayabheri Silicon Towers, Hyderabad, India
| | | | | | - S Sreenivas
- Andhra Medical College, Visakhapatnam, India
| | | | | | - Shibu Vijayan
- Qure.Ai Technologies, Oberoi Commerz II, Mumbai, India
| | | | - Vardhan Avasarala
- Otolaryngology - Head and Neck Surgery, Northeast Ohio Medical University, Rootstown, USA
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Theme 10 - Disease Stratification and Phenotyping of Patients. Amyotroph Lateral Scler Frontotemporal Degener 2022. [DOI: 10.1080/21678421.2022.2120686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Xu W, He G, Pan C, Shen D, Zhang N, Jiang P, Liu F, Chen J. A forced cough sound based pulmonary function assessment method by using machine learning. Front Public Health 2022; 10:1015876. [PMID: 36388361 PMCID: PMC9640833 DOI: 10.3389/fpubh.2022.1015876] [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: 08/10/2022] [Accepted: 09/30/2022] [Indexed: 01/27/2023] Open
Abstract
Pulmonary function testing (PFT) has important clinical value for the early detection of lung diseases, assessment of the disease severity, causes identification of dyspnea, and monitoring of critical patients. However, traditional PFT can only be carried out in a hospital environment, and it is challenging to meet the needs for daily and frequent evaluation of chronic respiratory diseases. In this study, we propose a novel method for accurately assessing pulmonary function by analyzing recorded forced cough sounds by mobile device without time and location restrictions. In the experiment, 309 clips of cough sound segments were separated from 133 patients who underwent PFT by using Audacity software. There are 247 clips of training samples and 62 clips of testing samples. Totally 52 features were extracted from the dataset, and principal component analysis (PCA) was used for feature reduction. Combined with biological attributes, the normalized features were regressed by using machine learning models with pulmonary function parameters (i.e., FEV1, FVC, FEV1/FVC, FEV1%, and FVC%). And a 5-fold cross-validation was applied to evaluate the performance of the regression models. As described in the experimental result, the result of coefficient of determination (R2) indicates that the support vector regression (SVR) model performed best in assessing FVC (0.84), FEV1% (0.61), and FVC% (0.62) among these models. The gradient boosting regression (GBR) model performs best in evaluating FEV1 (0.86) and FEV1/FVC (0.54). The result confirmed that the proposed method was capable of accurately assessing pulmonary function with forced cough sound. Besides, the cough sound sampling by a smartphone made it possible to conduct sampling and assess pulmonary function frequently in the home environment.
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Affiliation(s)
- Wenlong Xu
- College of Information Engineering, China Jiliang University, Hangzhou, Zhejiang, China,Wenlong Xu
| | - Guoqiang He
- College of Information Engineering, China Jiliang University, Hangzhou, Zhejiang, China
| | - Chen Pan
- College of Information Engineering, China Jiliang University, Hangzhou, Zhejiang, China
| | - Dan Shen
- The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Ning Zhang
- Lishui People's Hospital, Lishui, Zhejiang, China
| | | | - Feng Liu
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QL, Australia
| | - Jingjing Chen
- Department of Digital Urban Governance and School of Computer and Computing Science, Zhejiang University City College, Hangzhou, China,*Correspondence: Jingjing Chen
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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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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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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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Alam MZ, Simonetti A, Brillantino R, Tayler N, Grainge C, Siribaddana P, Nouraei SAR, Batchelor J, Rahman MS, Mancuzo EV, Holloway JW, Holloway JA, Rezwan FI. Predicting Pulmonary Function From the Analysis of Voice: A Machine Learning Approach. Front Digit Health 2022; 4:750226. [PMID: 35211691 PMCID: PMC8861188 DOI: 10.3389/fdgth.2022.750226] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 01/14/2022] [Indexed: 11/21/2022] Open
Abstract
Introduction To self-monitor asthma symptoms, existing methods (e.g. peak flow metre, smart spirometer) require special equipment and are not always used by the patients. Voice recording has the potential to generate surrogate measures of lung function and this study aims to apply machine learning approaches to predict lung function and severity of abnormal lung function from recorded voice for asthma patients. Methods A threshold-based mechanism was designed to separate speech and breathing from 323 recordings. Features extracted from these were combined with biological factors to predict lung function. Three predictive models were developed using Random Forest (RF), Support Vector Machine (SVM), and linear regression algorithms: (a) regression models to predict lung function, (b) multi-class classification models to predict severity of lung function abnormality, and (c) binary classification models to predict lung function abnormality. Training and test samples were separated (70%:30%, using balanced portioning), features were normalised, 10-fold cross-validation was used and model performances were evaluated on the test samples. Results The RF-based regression model performed better with the lowest root mean square error of 10·86. To predict severity of lung function impairment, the SVM-based model performed best in multi-class classification (accuracy = 73.20%), whereas the RF-based model performed best in binary classification models for predicting abnormal lung function (accuracy = 85%). Conclusion Our machine learning approaches can predict lung function, from recorded voice files, better than published approaches. This technique could be used to develop future telehealth solutions including smartphone-based applications which have potential to aid decision making and self-monitoring in asthma.
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Affiliation(s)
- Md. Zahangir Alam
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
- Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh
| | - Albino Simonetti
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
- Department of Information and Electrical Engineering and Applied Mathematics/DIEM, University of Salerno, Fisciano, Italy
| | - Raffaele Brillantino
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
- Department of Information and Electrical Engineering and Applied Mathematics/DIEM, University of Salerno, Fisciano, Italy
| | - Nick Tayler
- Peter Doherty Institute, The University of Melbourne, Melbourne, VIC, Australia
| | - Chris Grainge
- Hunter Medical Research Institute, The University of Newcastle, Newcastle, NSW, Australia
- Department of Respiratory Medicine, John Hunter Hospital, Newcastle, NSW, Australia
| | - Pandula Siribaddana
- Postgraduate Institute of Medicine, University of Colombo, Colombo, Sri Lanka
| | - S. A. Reza Nouraei
- Clinical Informatics Research Unit, University of Southampton, Southampton, United Kingdom
- Robert White Centre for Airway Voice and Swallowing, Poole Hospital, Poole, United Kingdom
| | - James Batchelor
- Clinical Informatics Research Unit, University of Southampton, Southampton, United Kingdom
| | - M. Sohel Rahman
- Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh
| | - Eliane V. Mancuzo
- Medical School, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - John W. Holloway
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
- National Institute for Health Research Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, United Kingdom
| | - Judith A. Holloway
- Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
- MSc Allergy, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
| | - Faisal I. Rezwan
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
- Department of Computer Science, Aberystwyth University, Aberystwyth, United Kingdom
- *Correspondence: Faisal I. Rezwan
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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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Pahar M, Klopper M, Reeve B, Warren R, Theron G, Niesler T. Automatic cough classification for tuberculosis screening in a real-world environment. Physiol Meas 2021; 42. [PMID: 34649231 DOI: 10.1088/1361-6579/ac2fb8] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 10/14/2021] [Indexed: 11/12/2022]
Abstract
Objective.The automatic discrimination between the coughing sounds produced by patients with tuberculosis (TB) and those produced by patients with other lung ailments.Approach.We present experiments based on a dataset of 1358 forced cough recordings obtained in a developing-world clinic from 16 patients with confirmed active pulmonary TB and 35 patients suffering from respiratory conditions suggestive of TB but confirmed to be TB negative. Using nested cross-validation, we have trained and evaluated five machine learning classifiers: logistic regression (LR), support vector machines, k-nearest neighbour, multilayer perceptrons and convolutional neural networks.Main Results.Although classification is possible in all cases, the best performance is achieved using LR. In combination with feature selection by sequential forward selection, our best LR system achieves an area under the ROC curve (AUC) of 0.94 using 23 features selected from a set of 78 high-resolution mel-frequency cepstral coefficients. This system achieves a sensitivity of 93% at a specificity of 95% and thus exceeds the 90% sensitivity at 70% specificity specification considered by the World Health Organisation (WHO) as a minimal requirement for a community-based TB triage test.Significance.The automatic classification of cough audio sounds, when applied to symptomatic patients requiring investigation for TB, can meet the WHO triage specifications for the identification of patients who should undergo expensive molecular downstream testing. This makes it a promising and viable means of low cost, easily deployable frontline screening for TB, which can benefit especially developing countries with a heavy TB burden.
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Affiliation(s)
- Madhurananda Pahar
- Department of Electrical and Electronic Engineering, Stellenbosch University, South Africa
| | - Marisa Klopper
- SAMRC Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, DSI/NRF Centre of Excellence for Biomedical Tuberculosis Research, Faculty of Medicine and Health Sciences, Stellenbosch University, South Africa
| | - Byron Reeve
- SAMRC Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, DSI/NRF Centre of Excellence for Biomedical Tuberculosis Research, Faculty of Medicine and Health Sciences, Stellenbosch University, South Africa
| | - Rob Warren
- SAMRC Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, DSI/NRF Centre of Excellence for Biomedical Tuberculosis Research, Faculty of Medicine and Health Sciences, Stellenbosch University, South Africa
| | - Grant Theron
- SAMRC Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, DSI/NRF Centre of Excellence for Biomedical Tuberculosis Research, Faculty of Medicine and Health Sciences, Stellenbosch University, South Africa
| | - Thomas Niesler
- Department of Electrical and Electronic Engineering, Stellenbosch University, South Africa
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Alharbi A, Abdur Rahman MD. Review of Recent Technologies for Tackling COVID-19. SN COMPUTER SCIENCE 2021; 2:460. [PMID: 34549196 PMCID: PMC8444512 DOI: 10.1007/s42979-021-00841-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 08/26/2021] [Indexed: 01/09/2023]
Abstract
The current pandemic caused by the COVID-19 virus requires more effort, experience, and science-sharing to overcome the damage caused by the pathogen. The fast and wide human-to-human transmission of the COVID-19 virus demands a significant role of the newest technologies in the form of local and global computing and information sharing, data privacy, and accurate tests. The advancements of deep neural networks, cloud computing solutions, blockchain technology, and beyond 5G (B5G) communication have contributed to the better management of the COVID-19 impacts on society. This paper reviews recent attempts to tackle the COVID-19 situation using these technological advancements.
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Affiliation(s)
- Ayman Alharbi
- Department Of Computer Engineering, College of Computer and Information systems, Umm AL-Qura University, Mecca, Saudi Arabia
| | - MD Abdur Rahman
- Department of Cyber Security and Forensic Computing, College of Computer and Cyber Sciences, University of Prince Mugrin, Madinah, 41499 Saudi Arabia
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Jayachitra VP, Nivetha S, Nivetha R, Harini R. A cognitive IoT-based framework for effective diagnosis of COVID-19 using multimodal data. Biomed Signal Process Control 2021; 70:102960. [PMID: 34249142 PMCID: PMC8260502 DOI: 10.1016/j.bspc.2021.102960] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Revised: 06/22/2021] [Accepted: 07/01/2021] [Indexed: 11/24/2022]
Abstract
The COVID-19 emerged at the end of 2019 and has become a global pandemic. There are many methods for COVID-19 prediction using a single modality. However, none of them predicts with 100% accuracy, as each individual exhibits varied symptoms for the disease. To decrease the rate of misdiagnosis, multiple modalities can be used for prediction. Besides, there is also a need for a self-diagnosis system to narrow down the risk of virus spread in testing centres. Therefore, we propose a robust IoT and deep learning-based multi-modal data classification method for the accurate prediction of COVID-19. Generally, highly accurate models require deep architectures. In this work, we introduce two lightweight models, namely CovParaNet for audio (cough, speech, breathing) classification and CovTinyNet for image (X-rays, CT scans) classification. These two models were identified as the best unimodal models after comparative analysis with the existing benchmark models. Finally, the obtained results of the five independently trained unimodal models are integrated by a novel dynamic multimodal Random Forest classifier. The lightweight CovParaNet and CovTinyNet models attain a maximum accuracy of 97.45% and 99.19% respectively even with a small dataset. The proposed dynamic multimodal fusion model predicts the final result with 100% accuracy, precision, and recall, and the online retraining mechanism enables it to extend its support even in a noisy environment. Furthermore, the computational complexity of all the unimodal models is minimized tremendously and the system functions effectively with 100% reliability even in the absence of any one of the input modalities during testing.
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Affiliation(s)
- V P Jayachitra
- Department of Computer Technology, MIT campus, Anna University, Chennai, India
| | - S Nivetha
- Department of Computer Technology, MIT campus, Anna University, Chennai, India
| | - R Nivetha
- Department of Computer Technology, MIT campus, Anna University, Chennai, India
| | - R Harini
- Department of Computer Technology, MIT campus, Anna University, Chennai, India
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14
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Alqudaihi KS, Aslam N, Khan IU, Almuhaideb AM, Alsunaidi SJ, Ibrahim NMAR, Alhaidari FA, Shaikh FS, Alsenbel YM, Alalharith DM, Alharthi HM, Alghamdi WM, Alshahrani MS. Cough Sound Detection and Diagnosis Using Artificial Intelligence Techniques: Challenges and Opportunities. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:102327-102344. [PMID: 34786317 PMCID: PMC8545201 DOI: 10.1109/access.2021.3097559] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 07/09/2021] [Indexed: 06/02/2023]
Abstract
Coughing is a common symptom of several respiratory diseases. The sound and type of cough are useful features to consider when diagnosing a disease. Respiratory infections pose a significant risk to human lives worldwide as well as a significant economic downturn, particularly in countries with limited therapeutic resources. In this study we reviewed the latest proposed technologies that were used to control the impact of respiratory diseases. Artificial Intelligence (AI) is a promising technology that aids in data analysis and prediction of results, thereby ensuring people's well-being. We conveyed that the cough symptom can be reliably used by AI algorithms to detect and diagnose different types of known diseases including pneumonia, pulmonary edema, asthma, tuberculosis (TB), COVID19, pertussis, and other respiratory diseases. We also identified different techniques that produced the best results for diagnosing respiratory disease using cough samples. This study presents the most recent challenges, solutions, and opportunities in respiratory disease detection and diagnosis, allowing practitioners and researchers to develop better techniques.
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Affiliation(s)
- Kawther S. Alqudaihi
- Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Nida Aslam
- Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Irfan Ullah Khan
- Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Abdullah M. Almuhaideb
- Department of Networks and CommunicationsCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Shikah J. Alsunaidi
- Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Nehad M. Abdel Rahman Ibrahim
- Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Fahd A. Alhaidari
- Department of Networks and CommunicationsCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Fatema S. Shaikh
- Department of Computer Information SystemsCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Yasmine M. Alsenbel
- Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Dima M. Alalharith
- Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Hajar M. Alharthi
- Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Wejdan M. Alghamdi
- Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Mohammed S. Alshahrani
- Department of Emergency MedicineCollege of MedicineImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
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Pahar M, Klopper M, Warren R, Niesler T. COVID-19 cough classification using machine learning and global smartphone recordings. Comput Biol Med 2021; 135:104572. [PMID: 34182331 PMCID: PMC8213969 DOI: 10.1016/j.compbiomed.2021.104572] [Citation(s) in RCA: 90] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 06/09/2021] [Accepted: 06/09/2021] [Indexed: 12/15/2022]
Abstract
We present a machine learning based COVID-19 cough classifier which can discriminate COVID-19 positive coughs from both COVID-19 negative and healthy coughs recorded on a smartphone. This type of screening is non-contact, easy to apply, and can reduce the workload in testing centres as well as limit transmission by recommending early self-isolation to those who have a cough suggestive of COVID-19. The datasets used in this study include subjects from all six continents and contain both forced and natural coughs, indicating that the approach is widely applicable. The publicly available Coswara dataset contains 92 COVID-19 positive and 1079 healthy subjects, while the second smaller dataset was collected mostly in South Africa and contains 18 COVID-19 positive and 26 COVID-19 negative subjects who have undergone a SARS-CoV laboratory test. Both datasets indicate that COVID-19 positive coughs are 15%–20% shorter than non-COVID coughs. Dataset skew was addressed by applying the synthetic minority oversampling technique (SMOTE). A leave-p-out cross-validation scheme was used to train and evaluate seven machine learning classifiers: logistic regression (LR), k-nearest neighbour (KNN), support vector machine (SVM), multilayer perceptron (MLP), convolutional neural network (CNN), long short-term memory (LSTM) and a residual-based neural network architecture (Resnet50). Our results show that although all classifiers were able to identify COVID-19 coughs, the best performance was exhibited by the Resnet50 classifier, which was best able to discriminate between the COVID-19 positive and the healthy coughs with an area under the ROC curve (AUC) of 0.98. An LSTM classifier was best able to discriminate between the COVID-19 positive and COVID-19 negative coughs, with an AUC of 0.94 after selecting the best 13 features from a sequential forward selection (SFS). Since this type of cough audio classification is cost-effective and easy to deploy, it is potentially a useful and viable means of non-contact COVID-19 screening.
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Affiliation(s)
- Madhurananda Pahar
- Department of Electrical and Electronic Engineering, Stellenbosch University, South Africa.
| | - Marisa Klopper
- SAMRC Centre for Tuberculosis Research, DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, South Africa.
| | - Robin Warren
- SAMRC Centre for Tuberculosis Research, DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, South Africa.
| | - Thomas Niesler
- Department of Electrical and Electronic Engineering, Stellenbosch University, South Africa.
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Stasak B, Huang Z, Razavi S, Joachim D, Epps J. Automatic Detection of COVID-19 Based on Short-Duration Acoustic Smartphone Speech Analysis. JOURNAL OF HEALTHCARE INFORMATICS RESEARCH 2021; 5:201-217. [PMID: 33723525 PMCID: PMC7948650 DOI: 10.1007/s41666-020-00090-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 11/11/2020] [Accepted: 12/03/2020] [Indexed: 12/16/2022]
Abstract
Currently, there is an increasing global need for COVID-19 screening to help reduce the rate of infection and at-risk patient workload at hospitals. Smartphone-based screening for COVID-19 along with other respiratory illnesses offers excellent potential due to its rapid-rollout remote platform, user convenience, symptom tracking, comparatively low cost, and prompt result processing timeframe. In particular, speech-based analysis embedded in smartphone app technology can measure physiological effects relevant to COVID-19 screening that are not yet digitally available at scale in the healthcare field. Using a selection of the Sonde Health COVID-19 2020 dataset, this study examines the speech of COVID-19-negative participants exhibiting mild and moderate COVID-19-like symptoms as well as that of COVID-19-positive participants with mild to moderate symptoms. Our study investigates the classification potential of acoustic features (e.g., glottal, prosodic, spectral) from short-duration speech segments (e.g., held vowel, pataka phrase, nasal phrase) for automatic COVID-19 classification using machine learning. Experimental results indicate that certain feature-task combinations can produce COVID-19 classification accuracy of up to 80% as compared with using the all-acoustic feature baseline (68%). Further, with brute-forced n-best feature selection and speech task fusion, automatic COVID-19 classification accuracy of upwards of 82-86% was achieved, depending on whether the COVID-19-negative participant had mild or moderate COVID-19-like symptom severity.
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Affiliation(s)
- Brian Stasak
- School of Electrical Engineering & Telecommunications, University of New South Wales, Sydney, NSW Australia
| | - Zhaocheng Huang
- School of Electrical Engineering & Telecommunications, University of New South Wales, Sydney, NSW Australia
| | | | | | - Julien Epps
- School of Electrical Engineering & Telecommunications, University of New South Wales, Sydney, NSW Australia
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Chuma EL, Iano Y. A Movement Detection System Using Continuous-Wave Doppler Radar Sensor and Convolutional Neural Network to Detect Cough and Other Gestures. IEEE SENSORS JOURNAL 2021; 21:2921-2928. [PMID: 37975064 PMCID: PMC8768982 DOI: 10.1109/jsen.2020.3028494] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 09/29/2020] [Accepted: 09/29/2020] [Indexed: 11/15/2023]
Abstract
The 2019 coronavirus disease (COVID-19) pandemic has contaminated millions of people, resulting in high fatality rates. Recently emerging artificial intelligence technologies like the convolutional neural network (CNN) are strengthening the power of imaging tools and can help medical specialists. CNN combined with other sensors creates a new solution to fight COVID-19 transmission. This paper presents a novel method to detect coughs (an important symptom of COVID-19) using a K-band continuous-wave Doppler radar with most popular CNNs architectures: AlexNet, VGG-19, and GoogLeNet. The proposed method has cough detection test accuracy of 88.0% using AlexNet CNN with people 1 m away from the microwave radar sensor, test accuracy of 80.0% with people 3 m away from the radar sensor, and test accuracy of 86.5% with a single mixed dataset with people 1 m and 3 m away from the radar sensor. The K-band radar sensor is inexpensive, completely camera-free and collects no personally-identifying information, allaying privacy concerns while still providing in-depth public health data on individual, local, and national levels. Additionally, the measurements are conducted without human contact, making the process proposed in this work safe for the investigation of contagious diseases such as COVID-19. The proposed cough detection system using microwave radar sensor has environmental robustness and dark/light-independence, unlike traditional cameras. The proposed microwave radar sensor can be used alone or in group with other sensors in a fusion sensor system to create a robust system to detect cough and other movements, mainly if using CNNs.
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Affiliation(s)
- Euclides Lourenço Chuma
- School of Electrical and Computer EngineeringUniversity of Campinas (UNICAMP)Campinas13083-970Brazil
| | - Yuzo Iano
- School of Electrical and Computer EngineeringUniversity of CampinasCampinas13083-970Brazil
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