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Rogers HP, Hseu A, Kim J, Silberholz E, Jo S, Dorste A, Jenkins K. Voice as a Biomarker of Pediatric Health: A Scoping Review. CHILDREN (BASEL, SWITZERLAND) 2024; 11:684. [PMID: 38929263 PMCID: PMC11201680 DOI: 10.3390/children11060684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Revised: 05/24/2024] [Accepted: 05/29/2024] [Indexed: 06/28/2024]
Abstract
The human voice has the potential to serve as a valuable biomarker for the early detection, diagnosis, and monitoring of pediatric conditions. This scoping review synthesizes the current knowledge on the application of artificial intelligence (AI) in analyzing pediatric voice as a biomarker for health. The included studies featured voice recordings from pediatric populations aged 0-17 years, utilized feature extraction methods, and analyzed pathological biomarkers using AI models. Data from 62 studies were extracted, encompassing study and participant characteristics, recording sources, feature extraction methods, and AI models. Data from 39 models across 35 studies were evaluated for accuracy, sensitivity, and specificity. The review showed a global representation of pediatric voice studies, with a focus on developmental, respiratory, speech, and language conditions. The most frequently studied conditions were autism spectrum disorder, intellectual disabilities, asphyxia, and asthma. Mel-Frequency Cepstral Coefficients were the most utilized feature extraction method, while Support Vector Machines were the predominant AI model. The analysis of pediatric voice using AI demonstrates promise as a non-invasive, cost-effective biomarker for a broad spectrum of pediatric conditions. Further research is necessary to standardize the feature extraction methods and AI models utilized for the evaluation of pediatric voice as a biomarker for health. Standardization has significant potential to enhance the accuracy and applicability of these tools in clinical settings across a variety of conditions and voice recording types. Further development of this field has enormous potential for the creation of innovative diagnostic tools and interventions for pediatric populations globally.
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Affiliation(s)
- Hannah Paige Rogers
- Department of Cardiology, Boston Children’s Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA 02115, USA
| | - Anne Hseu
- Department of Otolaryngology, Boston Children’s Hospital, 333 Longwood Ave, Boston, MA 02115, USA
| | - Jung Kim
- Department of Pediatrics, Boston Children’s Hospital, Boston, MA 02115, USA
| | | | - Stacy Jo
- Department of Otolaryngology, Boston Children’s Hospital, 333 Longwood Ave, Boston, MA 02115, USA
| | - Anna Dorste
- Boston Children’s Hospital, 300 Longwood Avenue, Boston, MA 02115, USA
| | - Kathy Jenkins
- Department of Cardiology, Boston Children’s Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA 02115, USA
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2
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Malik H, Anees T. Multi-modal deep learning methods for classification of chest diseases using different medical imaging and cough sounds. PLoS One 2024; 19:e0296352. [PMID: 38470893 DOI: 10.1371/journal.pone.0296352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Accepted: 12/11/2023] [Indexed: 03/14/2024] Open
Abstract
Chest disease refers to a wide range of conditions affecting the lungs, such as COVID-19, lung cancer (LC), consolidation lung (COL), and many more. When diagnosing chest disorders medical professionals may be thrown off by the overlapping symptoms (such as fever, cough, sore throat, etc.). Additionally, researchers and medical professionals make use of chest X-rays (CXR), cough sounds, and computed tomography (CT) scans to diagnose chest disorders. The present study aims to classify the nine different conditions of chest disorders, including COVID-19, LC, COL, atelectasis (ATE), tuberculosis (TB), pneumothorax (PNEUTH), edema (EDE), pneumonia (PNEU). Thus, we suggested four novel convolutional neural network (CNN) models that train distinct image-level representations for nine different chest disease classifications by extracting features from images. Furthermore, the proposed CNN employed several new approaches such as a max-pooling layer, batch normalization layers (BANL), dropout, rank-based average pooling (RBAP), and multiple-way data generation (MWDG). The scalogram method is utilized to transform the sounds of coughing into a visual representation. Before beginning to train the model that has been developed, the SMOTE approach is used to calibrate the CXR and CT scans as well as the cough sound images (CSI) of nine different chest disorders. The CXR, CT scan, and CSI used for training and evaluating the proposed model come from 24 publicly available benchmark chest illness datasets. The classification performance of the proposed model is compared with that of seven baseline models, namely Vgg-19, ResNet-101, ResNet-50, DenseNet-121, EfficientNetB0, DenseNet-201, and Inception-V3, in addition to state-of-the-art (SOTA) classifiers. The effectiveness of the proposed model is further demonstrated by the results of the ablation experiments. The proposed model was successful in achieving an accuracy of 99.01%, making it superior to both the baseline models and the SOTA classifiers. As a result, the proposed approach is capable of offering significant support to radiologists and other medical professionals.
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Affiliation(s)
- Hassaan Malik
- Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
| | - Tayyaba Anees
- Department of Software Engineering, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
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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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Khanam UA, Gao Z, Adamko D, Kusalik A, Rennie DC, Goodridge D, Chu L, Lawson JA. A scoping review of asthma and machine learning. J Asthma 2023; 60:213-226. [PMID: 35171725 DOI: 10.1080/02770903.2022.2043364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
OBJECTIVE The objective of this study was to determine the extent of machine learning (ML) application in asthma research and to identify research gaps while mapping the existing literature. DATA SOURCES We conducted a scoping review. PubMed, ProQuest, and Embase Scopus databases were searched with an end date of September 18, 2020. STUDY SELECTION DistillerSR was used for data management. Inclusion criteria were an asthma focus, human participants, ML techniques, and written in English. Exclusion criteria were abstract only, simulation-based, not human based, or were reviews or commentaries. Descriptive statistics were presented. RESULTS A total of 6,317 potential articles were found. After removing duplicates, and reviewing the titles and abstracts, 102 articles were included for the full text analysis. Asthma episode prediction (24.5%), asthma phenotype classification (16.7%), and genetic profiling of asthma (12.7%) were the top three study topics. Cohort (52.9%), cross-sectional (20.6%), and case-control studies (11.8%) were the study designs most frequently used. Regarding the ML techniques, 34.3% of the studies used more than one technique. Neural networks, clustering, and random forests were the most common ML techniques used where they were used in 20.6%, 18.6%, and 17.6% of studies, respectively. Very few studies considered location of residence (i.e. urban or rural status). CONCLUSIONS The use of ML in asthma studies has been increasing with most of this focused on the three major topics (>50%). Future research using ML could focus on gaps such as a broader range of study topics and focus on its use in additional populations (e.g. location of residence). Supplemental data for this article is available online at http://dx.doi.org/ .
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Affiliation(s)
- Ulfat A Khanam
- Health Sciences Program, College of Medicine, Canadian Centre for Health and Safety in Agriculture, Respiratory Research Centre, University of Saskatchewan, Saskatoon, SK, Canada
| | - Zhiwei Gao
- Department of Medicine, Memorial University of Newfoundland, St. John's, NL, Canada
| | - Darryl Adamko
- Department of Paediatrics, College of Medicine, University of Saskatchewan, Saskatoon, SK, Canada
| | - Anthony Kusalik
- Department of Computer Science, College of Arts and Science, University of Saskatchewan, Saskatoon, SK, Canada
| | - Donna C Rennie
- College of Nursing and Canadian Centre for Health and Safety in Agriculture, University of Saskatchewan, Saskatoon, SK, Canada
| | - Donna Goodridge
- Department of Medicine, College of Medicine, University of Saskatchewan, Saskatoon, SK, Canada
| | - Luan Chu
- Provincial Research Data Services, Alberta Health Service, Calgary, AB, Canada
| | - Joshua A Lawson
- Department of Medicine, Canadian Centre for Health and Safety in Agriculture, and Respiratory Research Centre, College of Medicine, University of Saskatchewan, Saskatoon, SK, Canada
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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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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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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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Aly M, Alotaibi NS. A novel deep learning model to detect COVID-19 based on wavelet features extracted from Mel-scale spectrogram of patients' cough and breathing sounds. INFORMATICS IN MEDICINE UNLOCKED 2022; 32:101049. [PMID: 35989705 PMCID: PMC9375256 DOI: 10.1016/j.imu.2022.101049] [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: 06/29/2022] [Revised: 08/08/2022] [Accepted: 08/09/2022] [Indexed: 10/26/2022] Open
Abstract
The goal of this paper is to classify the various cough and breath sounds of COVID-19 artefacts in the signals from dynamic real-life environments. The main reason for choosing cough and breath sounds than other common symptoms to detect COVID-19 patients from the comfort of their homes, so that they do not overload the Medicare system and therefore do not unwittingly spread the disease by regularly monitoring themselves. The presented model includes two main phases. The first phase is the sound-to-image transformation, which is improved by the Mel-scale spectrogram approach. The second phase consists of extraction of features and classification using nine deep transfer models (ResNet18/34/50/100/101, GoogLeNet, SqueezeNet, MobileNetv2, and NasNetmobile). The dataset contains information data from almost 1600 people (1185 Male and 415 Female) from all over the world. Our classification model is the most accurate, its accuracy is 99.2% according to the SGDM optimizer. The accuracy is good enough that a large set of labelled cough and breath data may be used to check the possibility for generalization. The results demonstrate that ResNet18 is the best stable model for classifying cough and breath tones from a restricted dataset, with a sensitivity of 98.3% and a specificity of 97.8%. Finally, the presented model is shown to be more trustworthy and accurate than any other present model. Cough and breath study accuracy is promising enough to put extrapolation and generalization to the test.
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Affiliation(s)
- Mohammed Aly
- Department of Artificial Intelligence, Faculty of Computers and Artificial Intelligence, Egyptian Russian University, Badr City, 11829, Cairo, Egypt
| | - Nouf Saeed Alotaibi
- Department of Computer Science, College of Science, Shaqra University, Shaqra City, 11961, Saudi Arabia
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Yun I, Jeung J, Kim Y, Song Y, Chung Y. Ultra-Low-Power Wearable Vibration Sensor with Highly Accurate Embedded Classifier. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:2451-2454. [PMID: 36086454 DOI: 10.1109/embc48229.2022.9871084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Reducing the power consumption of wearable sensors is a very important issue in relation to the device usage time and form factor. However, continuous wireless communication to analyze the measured signal in real-time significantly increases the power consumption of the wearable sensor. In this study, we propose a wearable vibration sensor that operates with extremely low power through an embedded signal classifier, which exhibits high accuracy and low calculation load. We demonstrate cough detection through the proposed sensor system. The result exhibits an accuracy of 93.0%, which is 24.3% higher than the conventional embedded classification algorithm. Also, the proposed approach reduces the average power consumption of the wearable sensor by 8.8 times. Clinical Relevance-People can measure the vibration from the body using an ultra-low-power wearable sensor. It provides a solution to automatically monitor cough symptoms in numerous patients.
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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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11
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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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12
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Sharma NK, Muguli A, Krishnan P, Kumar R, Chetupalli SR, Ganapathy S. Towards sound based testing of COVID-19-Summary of the first Diagnostics of COVID-19 using Acoustics (DiCOVA) Challenge. COMPUT SPEECH LANG 2021; 73:101320. [PMID: 34840419 PMCID: PMC8610834 DOI: 10.1016/j.csl.2021.101320] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 09/20/2021] [Accepted: 10/28/2021] [Indexed: 11/30/2022]
Abstract
The technology development for point-of-care tests (POCTs) targeting respiratory diseases has witnessed a growing demand in the recent past. Investigating the presence of acoustic biomarkers in modalities such as cough, breathing and speech sounds, and using them for building POCTs can offer fast, contactless and inexpensive testing. In view of this, over the past year, we launched the “Coswara” project to collect cough, breathing and speech sound recordings via worldwide crowdsourcing. With this data, a call for development of diagnostic tools was announced in the Interspeech 2021 as a special session titled “Diagnostics of COVID-19 using Acoustics (DiCOVA) Challenge”. The goal was to bring together researchers and practitioners interested in developing acoustics-based COVID-19 POCTs by enabling them to work on the same set of development and test datasets. As part of the challenge, datasets with breathing, cough, and speech sound samples from COVID-19 and non-COVID-19 individuals were released to the participants. The challenge consisted of two tracks. The Track-1 focused only on cough sounds, and participants competed in a leaderboard setting. In Track-2, breathing and speech samples were provided for the participants, without a competitive leaderboard. The challenge attracted 85 plus registrations with 29 final submissions for Track-1. This paper describes the challenge (datasets, tasks, baseline system), and presents a focused summary of the various systems submitted by the participating teams. An analysis of the results from the top four teams showed that a fusion of the scores from these teams yields an area-under-the-receiver operating curve (AUC-ROC) of 95.1% on the blind test data. By summarizing the lessons learned, we foresee the challenge overview in this paper to help accelerate technological development of acoustic-based POCTs.
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Affiliation(s)
- Neeraj Kumar Sharma
- Learning and Extraction of Acoustic Patterns (LEAP) Lab, Electrical Engineering, Indian Institute of Science, Bangalore, India
| | - Ananya Muguli
- Learning and Extraction of Acoustic Patterns (LEAP) Lab, Electrical Engineering, Indian Institute of Science, Bangalore, India
| | - Prashant Krishnan
- Learning and Extraction of Acoustic Patterns (LEAP) Lab, Electrical Engineering, Indian Institute of Science, Bangalore, India
| | - Rohit Kumar
- Learning and Extraction of Acoustic Patterns (LEAP) Lab, Electrical Engineering, Indian Institute of Science, Bangalore, India
| | - Srikanth Raj Chetupalli
- Learning and Extraction of Acoustic Patterns (LEAP) Lab, Electrical Engineering, Indian Institute of Science, Bangalore, India
| | - Sriram Ganapathy
- Learning and Extraction of Acoustic Patterns (LEAP) Lab, Electrical Engineering, Indian Institute of Science, Bangalore, India
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Loey M, Mirjalili S. COVID-19 cough sound symptoms classification from scalogram image representation using deep learning models. Comput Biol Med 2021; 139:105020. [PMID: 34775155 PMCID: PMC8628520 DOI: 10.1016/j.compbiomed.2021.105020] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Revised: 11/02/2021] [Accepted: 11/02/2021] [Indexed: 12/23/2022]
Abstract
Deep Learning shows promising performance in diverse fields and has become an emerging technology in Artificial Intelligence. Recent visual recognition is based on the ranking of photographs and the finding of artefacts in those images. The aim of this research is to classify the different cough sounds of COVID-19 artefacts in the signals of altered real-life environments. The introduced model takes into consideration two major steps. The first step is the transformation phase from sound to image that is optimized by the scalogram technique. The second step involves feature extraction and classification based on six deep transfer models (GoogleNet, ResNet18, ResNet50, ResNet101, MobileNetv2, and NasNetmobile). The dataset used contains 1457 (755 of COVID-19 and 702 of healthy) wave cough sounds. Although our recognition model performs the best, its accuracy only reaches 94.9% based on SGDM optimizer. The accuracy is promising enough for a wide set of labeled cough data to test the potential for generalization. The outcomes show that ResNet18 is the most stable model to classify the cough sounds from a limited dataset with a sensitivity of 94.44% and a specificity of 95.37%. Finally, a comparison of the research with a similar analysis is made. It is observed that the proposed model is more reliable and accurate than any current models. Cough research precision is promising enough to test the ability for extrapolation and generalization.
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Affiliation(s)
- Mohamed Loey
- Department of Computer Science, Faculty of Computers and Artificial Intelligence, Benha University, Benha, 13518, Egypt; Information Technology Program, New Cairo Technological University, New Cairo, Egypt.
| | - Seyedali Mirjalili
- Center for Artificial Intelligence Research and Optimization, Torrens University Australia, Fortitude Valley, Brisbane, QLD, 4006, Australia; Yonsei Frontier Lab, Yonsei University, Seoul, South Korea.
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14
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Chung Y, Jin J, Jo HI, Lee H, Kim SH, Chung SJ, Yoon HJ, Park J, Jeon JY. Diagnosis of Pneumonia by Cough Sounds Analyzed with Statistical Features and AI. SENSORS 2021; 21:s21217036. [PMID: 34770341 PMCID: PMC8586978 DOI: 10.3390/s21217036] [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: 06/29/2021] [Revised: 10/21/2021] [Accepted: 10/21/2021] [Indexed: 11/28/2022]
Abstract
Pneumonia is a serious disease often accompanied by complications, sometimes leading to death. Unfortunately, diagnosis of pneumonia is frequently delayed until physical and radiologic examinations are performed. Diagnosing pneumonia with cough sounds would be advantageous as a non-invasive test that could be performed outside a hospital. We aimed to develop an artificial intelligence (AI)-based pneumonia diagnostic algorithm. We collected cough sounds from thirty adult patients with pneumonia or the other causative diseases of cough. To quantify the cough sounds, loudness and energy ratio were used to represent the level and its spectral variations. These two features were used for constructing the diagnostic algorithm. To estimate the performance of developed algorithm, we assessed the diagnostic accuracy by comparing with the diagnosis by pulmonologists based on cough sound alone. The algorithm showed 90.0% sensitivity, 78.6% specificity and 84.9% overall accuracy for the 70 cases of cough sound in pneumonia group and 56 cases in non-pneumonia group. For same cases, pulmonologists correctly diagnosed the cough sounds with 56.4% accuracy. These findings showed that the proposed AI algorithm has value as an effective assistant technology to diagnose adult pneumonia patients with significant reliability.
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Affiliation(s)
- Youngbeen Chung
- Department of Mechanical Engineering, Hanyang University, 222 Wangsimri-ro, Seongdong-gu, Seoul 04763, Korea;
| | - Jie Jin
- School of Electromechanical and Automotive Engineering, Yantai University, 30 Qingquan Road, Laishan District, Yantai 264005, China;
| | - Hyun In Jo
- Department of Architectural Engineering, Hanyang University, 222 Wangsimri-ro, Seongdong-gu, Seoul 04763, Korea;
| | - Hyun Lee
- Department of Internal Medicine, Hanyang University Hospital, Hanyang University College of Medicine, 222 Wangsimri-ro, Seongdong-gu, Seoul 04763, Korea; (H.L.); (S.J.C.); (H.J.Y.)
| | - Sang-Heon Kim
- Department of Internal Medicine, Hanyang University Hospital, Hanyang University College of Medicine, 222 Wangsimri-ro, Seongdong-gu, Seoul 04763, Korea; (H.L.); (S.J.C.); (H.J.Y.)
- Correspondence: (S.-H.K.); (J.P.); Tel.: +82-02-2220-8336 (S.-H.K.); +82-02-2220-0424 (J.P.)
| | - Sung Jun Chung
- Department of Internal Medicine, Hanyang University Hospital, Hanyang University College of Medicine, 222 Wangsimri-ro, Seongdong-gu, Seoul 04763, Korea; (H.L.); (S.J.C.); (H.J.Y.)
| | - Ho Joo Yoon
- Department of Internal Medicine, Hanyang University Hospital, Hanyang University College of Medicine, 222 Wangsimri-ro, Seongdong-gu, Seoul 04763, Korea; (H.L.); (S.J.C.); (H.J.Y.)
| | - Junhong Park
- Department of Mechanical Engineering, Hanyang University, 222 Wangsimri-ro, Seongdong-gu, Seoul 04763, Korea;
- Correspondence: (S.-H.K.); (J.P.); Tel.: +82-02-2220-8336 (S.-H.K.); +82-02-2220-0424 (J.P.)
| | - Jin Yong Jeon
- Department of Medical and Digital Engineering, Hanyang University, 222 Wangsimri-ro, Seongdong-gu, Seoul 04763, Korea;
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15
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Balamurali BT, Hee HI, Kapoor S, Teoh OH, Teng SS, Lee KP, Herremans D, Chen JM. Deep Neural Network-Based Respiratory Pathology Classification Using Cough Sounds. SENSORS (BASEL, SWITZERLAND) 2021; 21:5555. [PMID: 34450996 PMCID: PMC8402243 DOI: 10.3390/s21165555] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 08/05/2021] [Accepted: 08/09/2021] [Indexed: 11/24/2022]
Abstract
Intelligent systems are transforming the world, as well as our healthcare system. We propose a deep learning-based cough sound classification model that can distinguish between children with healthy versus pathological coughs such as asthma, upper respiratory tract infection (URTI), and lower respiratory tract infection (LRTI). To train a deep neural network model, we collected a new dataset of cough sounds, labelled with a clinician's diagnosis. The chosen model is a bidirectional long-short-term memory network (BiLSTM) based on Mel-Frequency Cepstral Coefficients (MFCCs) features. The resulting trained model when trained for classifying two classes of coughs-healthy or pathology (in general or belonging to a specific respiratory pathology)-reaches accuracy exceeding 84% when classifying the cough to the label provided by the physicians' diagnosis. To classify the subject's respiratory pathology condition, results of multiple cough epochs per subject were combined. The resulting prediction accuracy exceeds 91% for all three respiratory pathologies. However, when the model is trained to classify and discriminate among four classes of coughs, overall accuracy dropped: one class of pathological coughs is often misclassified as the other. However, if one considers the healthy cough classified as healthy and pathological cough classified to have some kind of pathology, then the overall accuracy of the four-class model is above 84%. A longitudinal study of MFCC feature space when comparing pathological and recovered coughs collected from the same subjects revealed the fact that pathological coughs, irrespective of the underlying conditions, occupy the same feature space making it harder to differentiate only using MFCC features.
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Affiliation(s)
- B T Balamurali
- Science, Mathematics and Technology, Singapore University of Technology and Design, Singapore 487372, Singapore; (S.K.); (J.M.C.)
| | - Hwan Ing Hee
- Department of Paediatric Anaesthesia, KK Women’s and Children’s Hospital, Singapore 229899, Singapore;
- Anaesthesiology and Perioperative Sciences, Duke-NUS Medical School, 8 College Road, Singapore 169857, Singapore
| | - Saumitra Kapoor
- Science, Mathematics and Technology, Singapore University of Technology and Design, Singapore 487372, Singapore; (S.K.); (J.M.C.)
| | - Oon Hoe Teoh
- Respiratory Medicine Service, Department of Paediatrics, KK Women’s and Children’s Hospital, Singapore 229899, Singapore;
| | - Sung Shin Teng
- Department of Emergency Medicine, KK Women’s and Children’s Hospital, Singapore 229899, Singapore; (S.S.T.); (K.P.L.)
| | - Khai Pin Lee
- Department of Emergency Medicine, KK Women’s and Children’s Hospital, Singapore 229899, Singapore; (S.S.T.); (K.P.L.)
| | - Dorien Herremans
- Information Systems, Technology, and Design, Singapore University of Technology and Design, Singapore 487372, Singapore;
| | - Jer Ming Chen
- Science, Mathematics and Technology, Singapore University of Technology and Design, Singapore 487372, Singapore; (S.K.); (J.M.C.)
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16
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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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17
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B T B, Hee HI, Teoh OH, Lee KP, Kapoor S, Herremans D, Chen JM. Asthmatic versus healthy child classification based on cough and vocalised /ɑ:/ sounds. THE JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA 2020; 148:EL253. [PMID: 33003873 DOI: 10.1121/10.0001933] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 08/13/2020] [Indexed: 05/27/2023]
Abstract
Cough is a common symptom presenting in asthmatic children. In this investigation, an audio-based classification model is presented that can differentiate between healthy and asthmatic children, based on the combination of cough and vocalised /ɑ:/ sounds. A Gaussian mixture model using mel-frequency cepstral coefficients and constant-Q cepstral coefficients was trained. When comparing the predicted labels with the clinician's diagnosis, this cough sound model reaches an overall accuracy of 95.3%. The vocalised /ɑ:/ model reaches an accuracy of 72.2%, which is still significant because the dataset contains only 333 /ɑ:/ sounds versus 2029 cough sounds.
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Affiliation(s)
- Balamurali B T
- Singapore University of Technology and Design, Singapore, Singapore
| | - Hwan Ing Hee
- KK Women's and Children's Hospital, Singapore, , , , , , ,
| | - O H Teoh
- KK Women's and Children's Hospital, Singapore, , , , , , ,
| | - K P Lee
- KK Women's and Children's Hospital, Singapore, , , , , , ,
| | - Saumitra Kapoor
- Singapore University of Technology and Design, Singapore, Singapore
| | - Dorien Herremans
- Singapore University of Technology and Design, Singapore, Singapore
| | - Jer-Ming Chen
- Singapore University of Technology and Design, Singapore, Singapore
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18
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Bisballe-Müller N, Chang AB, Plumb EJ, Oguoma VM, Halken S, McCallum GB. Can Acute Cough Characteristics From Sound Recordings Differentiate Common Respiratory Illnesses in Children?: A Comparative Prospective Study. Chest 2020; 159:259-269. [PMID: 32653569 DOI: 10.1016/j.chest.2020.06.067] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Revised: 06/21/2020] [Accepted: 06/24/2020] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Acute respiratory illnesses cause substantial morbidity worldwide. Cough is a common symptom in these childhood respiratory illnesses, but no large cohort data are available on whether various cough characteristics can differentiate between these etiologies. RESEARCH QUESTION Can various clinically based cough characteristics (frequency [daytime/ nighttime], the sound itself, or type [wet/dry]) be used to differentiate common etiologies (asthma, bronchiolitis, pneumonia, other acute respiratory infections) of acute cough in children? STUDY DESIGN AND METHODS Between 2017 and 2019, children aged 2 weeks to ≤16 years, hospitalized with asthma, bronchiolitis, pneumonia, other acute respiratory infections, or control subjects were enrolled. Spontaneous coughs were digitally recorded over 24 hours except for the control subjects, who provided three voluntary coughs. Coughs were extracted and frequency defined (coughs/hour). Cough sounds and type were assessed independently by two observers blinded to the clinical data. Cough scored by a respiratory specialist was compared with discharge diagnosis using agreement (Cohen's kappa coefficient [қ]), sensitivity, and specificity. Caregiver-reported cough scores were related with objective cough frequency using Spearman coefficient (rs). RESULTS A cohort of 148 children (n = 118 with respiratory illnesses, n = 30 control subjects), median age = 2.0 years (interquartile range, 0.7-3.9), 58% males, and 50% First Nations children were enrolled. In those with respiratory illnesses, caregiver-reported cough scores and wet cough (range, 42%-63%) was similar. Overall agreement in diagnosis between the respiratory specialist and discharge diagnosis was slight (қ = 0.13; 95% CI, 0.03 to 0.22). Among diagnoses, specificity (8%-74%) and sensitivity (53%-100%) varied. Interrater agreement in cough type (wet/dry) between blinded observers was almost perfect (қ = 0.89; 95% CI, 0.81 to 0.97). Objective cough frequency was significantly correlated with reported cough scores using visual analog scale (rs = 0.43; bias-corrected 95% CI, 0.25 to 0.56) and verbal categorical description daytime score (rs = 0.39; bias-corrected 95% CI, 0.22 to 0.54). INTERPRETATION Cough characteristics alone are not distinct enough to accurately differentiate between common acute respiratory illnesses in children.
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Affiliation(s)
- Nina Bisballe-Müller
- Child Health Division, Menzies School of Health Research, Charles Darwin University, Darwin, NT, Australia; Department for Clinical Research, University of Southern Denmark, Odense, Denmark.
| | - Anne B Chang
- Child Health Division, Menzies School of Health Research, Charles Darwin University, Darwin, NT, Australia; Centre for Children's Health Research, Queensland University of Technology, Brisbane, QLD, Australia; Department of Respiratory and Sleep Medicine, Queensland Children's Hospital, Brisbane, QLD, Australia
| | - Erin J Plumb
- Child Health Division, Menzies School of Health Research, Charles Darwin University, Darwin, NT, Australia
| | - Victor M Oguoma
- Child Health Division, Menzies School of Health Research, Charles Darwin University, Darwin, NT, Australia
| | - Susanne Halken
- Hans Christian Andersen Children's Hospital, Odense University Hospital, Odense, Denmark
| | - Gabrielle B McCallum
- Child Health Division, Menzies School of Health Research, Charles Darwin University, Darwin, NT, Australia
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19
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Hosseini SA, Jamshidnezhad A, Zilaee M, Fouladi Dehaghi B, Mohammadi A, Hosseini SM. Neural Network-Based Clinical Prediction System for Identifying the Clinical Effects of Saffron (Crocus sativus L) Supplement Therapy on Allergic Asthma: Model Evaluation Study. JMIR Med Inform 2020; 8:e17580. [PMID: 32628613 PMCID: PMC7381052 DOI: 10.2196/17580] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Revised: 02/22/2020] [Accepted: 02/26/2020] [Indexed: 01/16/2023] Open
Abstract
Background Asthma is commonly associated with chronic airway inflammation and is the underlying cause of over a million deaths each year. Crocus sativus L, commonly known as saffron, when used in the form of traditional medicines, has demonstrated anti-inflammatory effects which may be beneficial to individuals with asthma. Objective The objective of this study was to develop a clinical prediction system using an artificial neural network to detect the effects of C sativus L supplements on patients with allergic asthma. Methods A genetic algorithm–modified neural network predictor system was developed to detect the level of effectiveness of C sativus L using features extracted from the clinical, immunologic, hematologic, and demographic information of patients with asthma. The study included data from men (n=40) and women (n=40) individuals with mild or moderate allergic asthma from 18 to 65 years of age. The aim of the model was to estimate and predict the level of effect of C sativus L supplements on each asthma risk factor and to predict the level of alleviation in patients with asthma. A genetic algorithm was used to extract input features for the clinical prediction system to improve its predictive performance. Moreover, an optimization model was developed for the artificial neural network component that classifies the patients with asthma using C sativus L supplement therapy. Results The best overall performance of the clinical prediction system was an accuracy greater than 99% for training and testing data. The genetic algorithm–modified neural network predicted the level of effect with high accuracy for anti–heat shock protein (anti-HSP), high sensitivity C-reactive protein (hs-CRP), forced expiratory volume in the first second of expiration (FEV1), forced vital capacity (FVC), the ratio of FEV1/FVC, and forced expiratory flow (FEF25%-75%) for testing data (anti-HSP: 96.5%; hs-CRP: 98.9%; FEV1: 98.1%; FVC: 97.5%; FEV1/FVC ratio: 97%; and FEF25%-75%: 96.7%, respectively). Conclusions The clinical prediction system developed in this study was effective in predicting the effect of C sativus L supplements on patients with allergic asthma. This clinical prediction system may help clinicians to identify early on which clinical factors in asthma will improve over the course of treatment and, in doing so, help clinicians to develop effective treatment plans for patients with asthma.
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Affiliation(s)
- Seyed Ahmad Hosseini
- Nutrition and Metabolic Diseases Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.,Department of Nutrition, Faculty of Allied Medical Sciences, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Amir Jamshidnezhad
- Nutrition and Metabolic Diseases Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.,Department of Health Information Technology, Faculty of Allied Medical Sciences, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Marzie Zilaee
- Nutrition and Metabolic Diseases Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Behzad Fouladi Dehaghi
- Environmental Technologies Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.,Department of Occupational Health, School of Public Health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Abbas Mohammadi
- Environmental Technologies Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.,Department of Occupational Health, School of Public Health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Seyed Mohsen Hosseini
- Department of Health Information Technology, Faculty of Allied Medical Sciences, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
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