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Zhu Z, Zhao S, Li J, Wang Y, Xu L, Jia Y, Li Z, Li W, Chen G, Wu X. Development and application of a deep learning-based comprehensive early diagnostic model for chronic obstructive pulmonary disease. Respir Res 2024; 25:167. [PMID: 38637823 PMCID: PMC11027407 DOI: 10.1186/s12931-024-02793-3] [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: 12/06/2023] [Accepted: 03/28/2024] [Indexed: 04/20/2024] Open
Abstract
BACKGROUND Chronic obstructive pulmonary disease (COPD) is a frequently diagnosed yet treatable condition, provided it is identified early and managed effectively. This study aims to develop an advanced COPD diagnostic model by integrating deep learning and radiomics features. METHODS We utilized a dataset comprising CT images from 2,983 participants, of which 2,317 participants also provided epidemiological data through questionnaires. Deep learning features were extracted using a Variational Autoencoder, and radiomics features were obtained using the PyRadiomics package. Multi-Layer Perceptrons were used to construct models based on deep learning and radiomics features independently, as well as a fusion model integrating both. Subsequently, epidemiological questionnaire data were incorporated to establish a more comprehensive model. The diagnostic performance of standalone models, the fusion model and the comprehensive model was evaluated and compared using metrics including accuracy, precision, recall, F1-score, Brier score, receiver operating characteristic curves, and area under the curve (AUC). RESULTS The fusion model exhibited outstanding performance with an AUC of 0.952, surpassing the standalone models based solely on deep learning features (AUC = 0.844) or radiomics features (AUC = 0.944). Notably, the comprehensive model, incorporating deep learning features, radiomics features, and questionnaire variables demonstrated the highest diagnostic performance among all models, yielding an AUC of 0.971. CONCLUSION We developed and implemented a data fusion strategy to construct a state-of-the-art COPD diagnostic model integrating deep learning features, radiomics features, and questionnaire variables. Our data fusion strategy proved effective, and the model can be easily deployed in clinical settings. TRIAL REGISTRATION Not applicable. This study is NOT a clinical trial, it does not report the results of a health care intervention on human participants.
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Affiliation(s)
- Zecheng Zhu
- Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Shunjin Zhao
- Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Lanxi Branch (Lanxi People's Hospital), Hangzhou, Zhejiang, China
| | - Jiahui Li
- Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yuting Wang
- Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Luopiao Xu
- Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yubing Jia
- Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Zihan Li
- Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Wenyuan Li
- Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Gang Chen
- College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang, China.
| | - Xifeng Wu
- Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
- National Institute for Data Science in Health and Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China.
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Sabry AH, I. Dallal Bashi O, Nik Ali N, Mahmood Al Kubaisi Y. Lung disease recognition methods using audio-based analysis with machine learning. Heliyon 2024; 10:e26218. [PMID: 38420389 PMCID: PMC10900411 DOI: 10.1016/j.heliyon.2024.e26218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 12/11/2023] [Accepted: 02/08/2024] [Indexed: 03/02/2024] Open
Abstract
The use of computer-based automated approaches and improvements in lung sound recording techniques have made lung sound-based diagnostics even better and devoid of subjectivity errors. Using a computer to evaluate lung sound features more thoroughly with the use of analyzing changes in lung sound behavior, recording measurements, suppressing the presence of noise contaminations, and graphical representations are all made possible by computer-based lung sound analysis. This paper starts with a discussion of the need for this research area, providing an overview of the field and the motivations behind it. Following that, it details the survey methodology used in this work. It presents a discussion on the elements of sound-based lung disease classification using machine learning algorithms. This includes commonly prior considered datasets, feature extraction techniques, pre-processing methods, artifact removal methods, lung-heart sound separation, deep learning algorithms, and wavelet transform of lung audio signals. The study introduces studies that review lung screening including a summary table of these references and discusses the literature gaps in the existing studies. It is concluded that the use of sound-based machine learning in the classification of respiratory diseases has promising results. While we believe this material will prove valuable to physicians and researchers exploring sound-signal-based machine learning, large-scale investigations remain essential to solidify the findings and foster wider adoption within the medical community.
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Affiliation(s)
- Ahmad H. Sabry
- Department of Medical Instrumentation Engineering Techniques, Shatt Al-Arab University College, Basra, Iraq
| | - Omar I. Dallal Bashi
- Medical Technical Institute, Northern Technical University, 95G2+P34, Mosul, 41002, Iraq
| | - N.H. Nik Ali
- School of Electrical Engineering, College of Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
| | - Yasir Mahmood Al Kubaisi
- Department of Sustainability Management, Dubai Academic Health Corporation, Dubai, 4545, United Arab Emirates
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Kumar S, Bhagat V, Sahu P, Chaube MK, Behera AK, Guizani M, Gravina R, Di Dio M, Fortino G, Curry E, Alsamhi SH. A novel multimodal framework for early diagnosis and classification of COPD based on CT scan images and multivariate pulmonary respiratory diseases. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 243:107911. [PMID: 37981453 DOI: 10.1016/j.cmpb.2023.107911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 10/23/2023] [Accepted: 11/01/2023] [Indexed: 11/21/2023]
Abstract
BACKGROUND AND OBJECTIVE Chronic Obstructive Pulmonary Disease (COPD) is one of the world's worst diseases; its early diagnosis using existing methods like statistical machine learning techniques, medical diagnostic tools, conventional medical procedures, and other methods is challenging due to misclassification results of COPD diagnosis and takes a long time to perform accurate prediction. Due to the severe consequences of COPD, detection and accurate diagnosis of COPD at an early stage is essential. This paper aims to design and develop a multimodal framework for early diagnosis and accurate prediction of COPD patients based on prepared Computerized Tomography (CT) scan images and lung sound/cough (audio) samples using machine learning techniques, which are presented in this study. METHOD The proposed multimodal framework extracts texture, histogram intensity, chroma, Mel-Frequency Cepstral Coefficients (MFCCs), and Gaussian scale space from the prepared CT images and lung sound/cough samples. Accurate data from All India Institute Medical Sciences (AIIMS), Raipur, India, and the open respiratory CT images and lung sound/cough (audio) sample dataset validate the proposed framework. The discriminatory features are selected from the extracted feature sets using unsupervised ML techniques, and customized ensemble learning techniques are applied to perform early classification and assess the severity levels of COPD patients. RESULTS The proposed framework provided 97.50%, 98%, and 95.30% accuracy for early diagnosis of COPD patients based on the fusion technique, CT diagnostic model, and cough sample model. CONCLUSION Finally, we compare the performance of the proposed framework with existing methods, current approaches, and conventional benchmark techniques for early diagnosis.
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Affiliation(s)
- Santosh Kumar
- Department of Computer Science and Engineering, IIIT-Naya Raipur, Chhattisgarh, India.
| | - Vijesh Bhagat
- Department of Computer Science and Engineering, IIIT-Naya Raipur, Chhattisgarh, India.
| | - Prakash Sahu
- Department of Computer Science and Engineering, IIIT-Naya Raipur, Chhattisgarh, India.
| | | | - Ajoy Kumar Behera
- Department of Pulmonary Medicine & TB, All India Institute of Medical Sciences (AIIMS), Raipur, Chhattisgarh, India.
| | - Mohsen Guizani
- Machine Learning Department, Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, United Arab Emirates.
| | - Raffaele Gravina
- Department of Informatics, Modeling, Electronic, and System Engineering, University of Calabria, 87036 Rende, Italy.
| | - Michele Di Dio
- Department of Informatics, Modeling, Electronic, and System Engineering, University of Calabria, 87036 Rende, Italy; Annunziata Hospital Cosenza, Italy.
| | - Giancarlo Fortino
- Department of Informatics, Modeling, Electronic, and System Engineering, University of Calabria, 87036 Rende, Italy.
| | - Edward Curry
- Insight Centre for Data Analytics, University of Galway, Galway, Ireland.
| | - Saeed Hamood Alsamhi
- Insight Centre for Data Analytics, University of Galway, Galway, Ireland; Faculty of Engineering, IBB University, Ibb, Yemen.
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Kim Y, Hyon Y, Woo SD, Lee S, Lee SI, Ha T, Chung C. Evolution of the Stethoscope: Advances with the Adoption of Machine Learning and Development of Wearable Devices. Tuberc Respir Dis (Seoul) 2023; 86:251-263. [PMID: 37592751 PMCID: PMC10555525 DOI: 10.4046/trd.2023.0065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 08/04/2023] [Accepted: 08/15/2023] [Indexed: 08/19/2023] Open
Abstract
The stethoscope has long been used for the examination of patients, but the importance of auscultation has declined due to its several limitations and the development of other diagnostic tools. However, auscultation is still recognized as a primary diagnostic device because it is non-invasive and provides valuable information in real-time. To supplement the limitations of existing stethoscopes, digital stethoscopes with machine learning (ML) algorithms have been developed. Thus, now we can record and share respiratory sounds and artificial intelligence (AI)-assisted auscultation using ML algorithms distinguishes the type of sounds. Recently, the demands for remote care and non-face-to-face treatment diseases requiring isolation such as coronavirus disease 2019 (COVID-19) infection increased. To address these problems, wireless and wearable stethoscopes are being developed with the advances in battery technology and integrated sensors. This review provides the history of the stethoscope and classification of respiratory sounds, describes ML algorithms, and introduces new auscultation methods based on AI-assisted analysis and wireless or wearable stethoscopes.
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Affiliation(s)
- Yoonjoo Kim
- Division of Pulmonology, Department of Internal Medicine, Chungnam National University College of Medicine, Daejeon, Republic of Korea
| | - YunKyong Hyon
- Division of Industrial Mathematics, National Institute for Mathematical Sciences, Daejeon, Republic of Korea
| | - Seong-Dae Woo
- Division of Pulmonology, Department of Internal Medicine, Chungnam National University College of Medicine, Daejeon, Republic of Korea
| | - Sunju Lee
- Division of Industrial Mathematics, National Institute for Mathematical Sciences, Daejeon, Republic of Korea
| | - Song-I Lee
- Division of Pulmonology, Department of Internal Medicine, Chungnam National University College of Medicine, Daejeon, Republic of Korea
| | - Taeyoung Ha
- Division of Industrial Mathematics, National Institute for Mathematical Sciences, Daejeon, Republic of Korea
| | - Chaeuk Chung
- Division of Pulmonology, Department of Internal Medicine, Chungnam National University College of Medicine, Daejeon, Republic of Korea
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Huang DM, Huang J, Qiao K, Zhong NS, Lu HZ, Wang WJ. Deep learning-based lung sound analysis for intelligent stethoscope. Mil Med Res 2023; 10:44. [PMID: 37749643 PMCID: PMC10521503 DOI: 10.1186/s40779-023-00479-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 09/05/2023] [Indexed: 09/27/2023] Open
Abstract
Auscultation is crucial for the diagnosis of respiratory system diseases. However, traditional stethoscopes have inherent limitations, such as inter-listener variability and subjectivity, and they cannot record respiratory sounds for offline/retrospective diagnosis or remote prescriptions in telemedicine. The emergence of digital stethoscopes has overcome these limitations by allowing physicians to store and share respiratory sounds for consultation and education. On this basis, machine learning, particularly deep learning, enables the fully-automatic analysis of lung sounds that may pave the way for intelligent stethoscopes. This review thus aims to provide a comprehensive overview of deep learning algorithms used for lung sound analysis to emphasize the significance of artificial intelligence (AI) in this field. We focus on each component of deep learning-based lung sound analysis systems, including the task categories, public datasets, denoising methods, and, most importantly, existing deep learning methods, i.e., the state-of-the-art approaches to convert lung sounds into two-dimensional (2D) spectrograms and use convolutional neural networks for the end-to-end recognition of respiratory diseases or abnormal lung sounds. Additionally, this review highlights current challenges in this field, including the variety of devices, noise sensitivity, and poor interpretability of deep models. To address the poor reproducibility and variety of deep learning in this field, this review also provides a scalable and flexible open-source framework that aims to standardize the algorithmic workflow and provide a solid basis for replication and future extension: https://github.com/contactless-healthcare/Deep-Learning-for-Lung-Sound-Analysis .
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Affiliation(s)
- Dong-Min Huang
- Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, Guangdong, China
| | - Jia Huang
- The Third People's Hospital of Shenzhen, Shenzhen, 518112, Guangdong, China
| | - Kun Qiao
- The Third People's Hospital of Shenzhen, Shenzhen, 518112, Guangdong, China
| | - Nan-Shan Zhong
- Guangzhou Institute of Respiratory Health, China State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, China.
| | - Hong-Zhou Lu
- The Third People's Hospital of Shenzhen, Shenzhen, 518112, Guangdong, China.
| | - Wen-Jin Wang
- Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, Guangdong, China.
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6
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Dar JA, Srivastava KK, Mishra A. Lung anomaly detection from respiratory sound database (sound signals). Comput Biol Med 2023; 164:107311. [PMID: 37552916 DOI: 10.1016/j.compbiomed.2023.107311] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 07/01/2023] [Accepted: 07/28/2023] [Indexed: 08/10/2023]
Abstract
Chest or upper body auscultation has long been considered a useful part of the physical examination going back to the time of Hippocrates. However, it did not become a prevalent practice until the invention of the stethoscope by Rene Laennec in 1816, which made the practice suitable and hygienic. Pulmonary disease is a kind of sickness that affects the lungs and various parts of the respiratory system. Lung diseases are the third largest cause of death in the world. According to the World Health Organization (WHO), the five major respiratory diseases, namely chronic obstructive pulmonary disease (COPD), tuberculosis, acute lower respiratory tract infection (LRTI), asthma, and lung cancer, cause the death of more than 3 million people each year worldwide. Respiratory sounds disclose significant information regarding the lungs of patients. Numerous methods are developed for analyzing the lung sounds. However, clinical approaches require qualified pulmonologists to diagnose such kind of signals appropriately and are also time consuming. Hence, an efficient Fractional Water Cycle Swarm Optimizer-based Deep Residual Network (Fr-WCSO-based DRN) is developed in this research for detecting the pulmonary abnormalities using respiratory sounds signals. The proposed Fr-WCSO is newly designed by the incorporation of Fractional Calculus (FC) and Water Cycle Swarm Optimizer WCSO. Meanwhile, WCSO is the combination of Water Cycle Algorithm (WCA) with Competitive Swarm Optimizer (CSO). The respiratory input sound signals are pre-processed and the important features needed for the further processing are effectively extracted. With the extracted features, data augmentation is carried out for minimizing the over fitting issues for improving the overall detection performance. Once data augmentation is done, feature selection is performed using proposed Fr-WCSO algorithm. Finally, pulmonary abnormality detection is performed using DRN where the training procedure of DRN is performed using the developed Fr-WCSO algorithm. The developed method achieved superior performance by considering the evaluation measures, namely True Positive Rate (TPR), True Negative Rate (TNR) and testing accuracy with the values of 0.963(96.3%), 0.932,(93.2%) and 0.948(94.8%), respectively.
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Affiliation(s)
- Jawad Ahmad Dar
- Department of Computer Science and Engineering, Mansarovar Global University, Madhya Pradesh, India.
| | - Kamal Kr Srivastava
- Department of Information Technology at Babu Banarasi Das Northern India Institute of Technology, Lucknow, India.
| | - Alok Mishra
- Department of Physics, Gaya College of Engineering, Gaya, India.
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Prabhakar SK, Won DO. HISET: Hybrid interpretable strategies with ensemble techniques for respiratory sound classification. Heliyon 2023; 9:e18466. [PMID: 37554776 PMCID: PMC10404967 DOI: 10.1016/j.heliyon.2023.e18466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 07/18/2023] [Accepted: 07/18/2023] [Indexed: 08/10/2023] Open
Abstract
The human respiratory systems can be affected by several diseases and it is associated with distinctive sounds. For advanced biomedical signal processing, one of the most complex issues is automated respiratory sound classification. In this research, five Hybrid Interpretable Strategies with Ensemble Techniques (HISET) which are quite interesting and robust are proposed for the purpose of respiratory sounds classification. The first approach is termed as an Ensemble GSSR technique which utilizes L 2 Granger Analysis and the proposed Supportive Ensemble Empirical Mode Decomposition (SEEMD) technique and then Support Vector Machine based Recursive Feature Elimination (SVM-RFE) is used for feature selection and followed by classification with Machine Learning (ML) classifiers. The second approach proposed is the implementation of a novel Realm Revamping Sparse Representation Classification (RR-SRC) technique and third approach proposed is a Distance Metric dependent Variational Mode Decomposition (DM-VMD) with Extreme Learning Machine (ELM) classification process. The fourth approach proposed is with the usage of Harris Hawks Optimization (HHO) with a Scaling Factor based Pliable Differential Evolution (SFPDE) algorithm termed as HHO-SFPDE and it is classified with ML classifiers. The fifth or the final approach proposed analyzes the application of dimensionality reduction techniques with the proposed Gray Wolf Optimization based Support Vector Classification (GWO-SVC) and another parallel approach utilizes a similar kind of analysis with the Grasshopper Optimization Algorithm (GOA) based Sparse Autoencoder. The results are examined for ICBHI dataset and the best results are shown for the 2-class classification when the analysis is carried out with Manhattan distance-based VMD-ELM reporting an accuracy of 95.39%, and for 3-class classification Euclidean distance-based VMD-ELM reported an accuracy of 90.61% and for 4-class classification, Manhattan distance-based VMD-ELM reported an accuracy of 89.27%.
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Affiliation(s)
- Sunil Kumar Prabhakar
- Department of Artificial Intelligence Convergence, Hallym University, Chuncheon, Gangwon-do, South Korea
| | - Dong-Ok Won
- Department of Artificial Intelligence Convergence, Hallym University, Chuncheon, Gangwon-do, South Korea
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Alotaibi N, Borg BM, Abramson MJ, Paul E, Zwar N, Russell G, Wilson S, Holland AE, Bonevski B, Mahal A, George J. Different Case Finding Approaches to Optimise COPD Diagnosis: Evidence from the RADICALS Trial. Int J Chron Obstruct Pulmon Dis 2023; 18:1543-1554. [PMID: 37492489 PMCID: PMC10364814 DOI: 10.2147/copd.s371371] [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: 04/30/2022] [Accepted: 05/28/2023] [Indexed: 07/27/2023] Open
Abstract
Aim Diagnosis of COPD in primary care is hindered by underuse of spirometry. Case finding using validated symptom and health status questionnaires, and simple handheld devices in high-risk populations may improve diagnosis. This study aimed to determine the best combination of measures to optimise COPD diagnosis in the primary care setting. Methods We recruited 335 current or ex-smokers, including those with an established diagnosis of COPD from general practices. Participants' FEV1 and FEV6 were measured using a handheld spirometry device (COPD-6®). Each completed the COPD assessment test (CAT), a modified Medical Research Council (mMRC) dyspnoea scale, St George's Respiratory Questionnaire (SGRQ) and smoking history questionnaire. From these data we calculated the predictive validity for spirometry-confirmed diagnosis of COPD. Area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, positive and negative predictive values (PPV, NPV) were calculated for each. Kappa coefficient was used to measure the agreement between the Fixed-Ratio (FR) and Lower Limit of Normal (LLN) spirometric criteria in diagnosing COPD. Results FEV1/FEV6 <0.70 alone showed significant association (p<0.0001) with COPD diagnosis and good predictive accuracy (AUROC=0.725). However, no further improvement was found after combining SGRQ, CAT and mMRC with FEV1/FEV6. FEV1/FEV6 <0.70 using the COPD-6® handheld device had moderate sensitivity (65.7%) and high PPV (90.1%), high specificity (79.3%) and NPV (44.8%). There was good agreement between FR and LLN definitions (κ=0.70). Conclusion Handheld micro-spirometers can facilitate case finding of COPD in smokers and ex-smokers attending general practice. The fixed ratio criterion currently recommended by COPD-X guidelines offers the simplest method for diagnosing COPD in Australian primary care.
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Affiliation(s)
- Nawar Alotaibi
- Centre for Medicine Use and Safety, Monash University, Melbourne, VIC, Australia
| | - Brigitte M Borg
- Respiratory Medicine, Alfred Health, Melbourne, VIC, Australia
- School of Public Health & Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Michael J Abramson
- School of Public Health & Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Eldho Paul
- School of Public Health & Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Nicholas Zwar
- Faculty of Health Sciences and Medicine, Bond University, Robina, QLD, Australia
| | - Grant Russell
- Department of General Practice, Monash University, Melbourne, VIC, Australia
| | - Sally Wilson
- Centre for Medicine Use and Safety, Monash University, Melbourne, VIC, Australia
- Department of Infrastructure Engineering, The University of Melbourne, Melbourne, VIC, Australia
| | - Anne E Holland
- Respiratory Medicine, Alfred Health, Melbourne, VIC, Australia
- Central Clinical School, Monash University, Melbourne, VIC, Australia
| | - Billie Bonevski
- College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia
| | - Ajay Mahal
- The Nossal Institute for Global Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Johnson George
- Centre for Medicine Use and Safety, Monash University, Melbourne, VIC, Australia
- School of Public Health & Preventive Medicine, Monash University, Melbourne, VIC, Australia
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Choi Y, Lee H. Interpretation of lung disease classification with light attention connected module. Biomed Signal Process Control 2023; 84:104695. [PMID: 36879856 PMCID: PMC9978539 DOI: 10.1016/j.bspc.2023.104695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 12/21/2022] [Accepted: 02/11/2023] [Indexed: 03/06/2023]
Abstract
Lung diseases lead to complications from obstructive diseases, and the COVID-19 pandemic has increased lung disease-related deaths. Medical practitioners use stethoscopes to diagnose lung disease. However, an artificial intelligence model capable of objective judgment is required since the experience and diagnosis of respiratory sounds differ. Therefore, in this study, we propose a lung disease classification model that uses an attention module and deep learning. Respiratory sounds were extracted using log-Mel spectrogram MFCC. Normal and five types of adventitious sounds were effectively classified by improving VGGish and adding a light attention connected module to which the efficient channel attention module (ECA-Net) was applied. The performance of the model was evaluated for accuracy, precision, sensitivity, specificity, f1-score, and balanced accuracy, which were 92.56%, 92.81%, 92.22%, 98.50%, 92.29%, and 95.4%, respectively. We confirmed high performance according to the attention effect. The classification causes of lung diseases were analyzed using gradient-weighted class activation mapping (Grad-CAM), and the performances of their models were compared using open lung sounds measured using a Littmann 3200 stethoscope. The experts' opinions were also included. Our results will contribute to the early diagnosis and interpretation of diseases in patients with lung disease by utilizing algorithms in smart medical stethoscopes.
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Affiliation(s)
- Youngjin Choi
- School of Industrial Management Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
| | - Hongchul Lee
- School of Industrial Management Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
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Sfayyih AH, Sulaiman N, Sabry AH. A review on lung disease recognition by acoustic signal analysis with deep learning networks. JOURNAL OF BIG DATA 2023; 10:101. [PMID: 37333945 PMCID: PMC10259357 DOI: 10.1186/s40537-023-00762-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 05/08/2023] [Indexed: 06/20/2023]
Abstract
Recently, assistive explanations for difficulties in the health check area have been made viable thanks in considerable portion to technologies like deep learning and machine learning. Using auditory analysis and medical imaging, they also increase the predictive accuracy for prompt and early disease detection. Medical professionals are thankful for such technological support since it helps them manage further patients because of the shortage of skilled human resources. In addition to serious illnesses like lung cancer and respiratory diseases, the plurality of breathing difficulties is gradually rising and endangering society. Because early prediction and immediate treatment are crucial for respiratory disorders, chest X-rays and respiratory sound audio are proving to be quite helpful together. Compared to related review studies on lung disease classification/detection using deep learning algorithms, only two review studies based on signal analysis for lung disease diagnosis have been conducted in 2011 and 2018. This work provides a review of lung disease recognition with acoustic signal analysis with deep learning networks. We anticipate that physicians and researchers working with sound-signal-based machine learning will find this material beneficial.
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Affiliation(s)
- Alyaa Hamel Sfayyih
- Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 Serdang, Malaysia
| | - Nasri Sulaiman
- Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 Serdang, Malaysia
| | - Ahmad H. Sabry
- Department of Computer Engineering, Al-Nahrain University, Al Jadriyah Bridge, 64074 Baghdad, Iraq
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Siebert JN, Hartley MA, Courvoisier DS, Salamin M, Robotham L, Doenz J, Barazzone-Argiroffo C, Gervaix A, Bridevaux PO. Deep learning diagnostic and severity-stratification for interstitial lung diseases and chronic obstructive pulmonary disease in digital lung auscultations and ultrasonography: clinical protocol for an observational case-control study. BMC Pulm Med 2023; 23:191. [PMID: 37264374 DOI: 10.1186/s12890-022-02255-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Accepted: 11/20/2022] [Indexed: 06/03/2023] Open
Abstract
BACKGROUND Interstitial lung diseases (ILD), such as idiopathic pulmonary fibrosis (IPF) and non-specific interstitial pneumonia (NSIP), and chronic obstructive pulmonary disease (COPD) are severe, progressive pulmonary disorders with a poor prognosis. Prompt and accurate diagnosis is important to enable patients to receive appropriate care at the earliest possible stage to delay disease progression and prolong survival. Artificial intelligence-assisted lung auscultation and ultrasound (LUS) could constitute an alternative to conventional, subjective, operator-related methods for the accurate and earlier diagnosis of these diseases. This protocol describes the standardised collection of digitally-acquired lung sounds and LUS images of adult outpatients with IPF, NSIP or COPD and a deep learning diagnostic and severity-stratification approach. METHODS A total of 120 consecutive patients (≥ 18 years) meeting international criteria for IPF, NSIP or COPD and 40 age-matched controls will be recruited in a Swiss pulmonology outpatient clinic, starting from August 2022. At inclusion, demographic and clinical data will be collected. Lung auscultation will be recorded with a digital stethoscope at 10 thoracic sites in each patient and LUS images using a standard point-of-care device will be acquired at the same sites. A deep learning algorithm (DeepBreath) using convolutional neural networks, long short-term memory models, and transformer architectures will be trained on these audio recordings and LUS images to derive an automated diagnostic tool. The primary outcome is the diagnosis of ILD versus control subjects or COPD. Secondary outcomes are the clinical, functional and radiological characteristics of IPF, NSIP and COPD diagnosis. Quality of life will be measured with dedicated questionnaires. Based on previous work to distinguish normal and pathological lung sounds, we estimate to achieve convergence with an area under the receiver operating characteristic curve of > 80% using 40 patients in each category, yielding a sample size calculation of 80 ILD (40 IPF, 40 NSIP), 40 COPD, and 40 controls. DISCUSSION This approach has a broad potential to better guide care management by exploring the synergistic value of several point-of-care-tests for the automated detection and differential diagnosis of ILD and COPD and to estimate severity. Trial registration Registration: August 8, 2022. CLINICALTRIALS gov Identifier: NCT05318599.
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Affiliation(s)
- Johan N Siebert
- Division of Paediatric Emergency Medicine, Department of Women, Child and Adolescent, Geneva University Hospitals, 47 Avenue de la Roseraie, 1211, Geneva 14, Switzerland.
- Faculty of Medicine, University of Geneva, Geneva, Switzerland.
| | - Mary-Anne Hartley
- Machine Learning and Optimization (MLO) Laboratory, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
| | - Delphine S Courvoisier
- Quality of Care Unit, Geneva University Hospitals, Geneva, Switzerland
- Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Marlène Salamin
- Division of Pulmonology, Hospital of Valais, Sion, Switzerland
| | - Laura Robotham
- Division of Pulmonology, Hospital of Valais, Sion, Switzerland
| | - Jonathan Doenz
- Machine Learning and Optimization (MLO) Laboratory, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
| | - Constance Barazzone-Argiroffo
- Division of Paediatric Pulmonology, Department of Women, Child and Adolescent, Geneva University Hospitals, Geneva, Switzerland
- Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Alain Gervaix
- Division of Paediatric Emergency Medicine, Department of Women, Child and Adolescent, Geneva University Hospitals, 47 Avenue de la Roseraie, 1211, Geneva 14, Switzerland
- Faculty of Medicine, University of Geneva, Geneva, Switzerland
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12
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Sfayyih AH, Sabry AH, Jameel SM, Sulaiman N, Raafat SM, Humaidi AJ, Kubaiaisi YMA. Acoustic-Based Deep Learning Architectures for Lung Disease Diagnosis: A Comprehensive Overview. Diagnostics (Basel) 2023; 13:diagnostics13101748. [PMID: 37238233 DOI: 10.3390/diagnostics13101748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 05/04/2023] [Accepted: 05/11/2023] [Indexed: 05/28/2023] Open
Abstract
Lung auscultation has long been used as a valuable medical tool to assess respiratory health and has gotten a lot of attention in recent years, notably following the coronavirus epidemic. Lung auscultation is used to assess a patient's respiratory role. Modern technological progress has guided the growth of computer-based respiratory speech investigation, a valuable tool for detecting lung abnormalities and diseases. Several recent studies have reviewed this important area, but none are specific to lung sound-based analysis with deep-learning architectures from one side and the provided information was not sufficient for a good understanding of these techniques. This paper gives a complete review of prior deep-learning-based architecture lung sound analysis. Deep-learning-based respiratory sound analysis articles are found in different databases including the Plos, ACM Digital Libraries, Elsevier, PubMed, MDPI, Springer, and IEEE. More than 160 publications were extracted and submitted for assessment. This paper discusses different trends in pathology/lung sound, the common features for classifying lung sounds, several considered datasets, classification methods, signal processing techniques, and some statistical information based on previous study findings. Finally, the assessment concludes with a discussion of potential future improvements and recommendations.
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Affiliation(s)
- Alyaa Hamel Sfayyih
- Department of Electrical and Electronic Engineering, Faculty of Engineering, University Putra Malaysia, Serdang 43400, Malaysia
| | - Ahmad H Sabry
- Department of Computer Engineering, Al-Nahrain University Al Jadriyah Bridge, Baghdad 64074, Iraq
| | | | - Nasri Sulaiman
- Department of Electrical and Electronic Engineering, Faculty of Engineering, University Putra Malaysia, Serdang 43400, Malaysia
| | - Safanah Mudheher Raafat
- Department of Control and Systems Engineering, University of Technology, Baghdad 10011, Iraq
| | - Amjad J Humaidi
- Department of Control and Systems Engineering, University of Technology, Baghdad 10011, Iraq
| | - Yasir Mahmood Al Kubaiaisi
- Department of Sustainability Management, Dubai Academic Health Corporation, Dubai 4545, United Arab Emirates
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Yang Y, Cao Y, Han X, Ma X, Li R, Wang R, Xiao L, Xie L. Revealing EXPH5 as a potential diagnostic gene biomarker of the late stage of COPD based on machine learning analysis. Comput Biol Med 2023; 154:106621. [PMID: 36746116 DOI: 10.1016/j.compbiomed.2023.106621] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 01/19/2023] [Accepted: 01/28/2023] [Indexed: 02/01/2023]
Abstract
Chronic obstructive pulmonary disease is a kind of chronic lung disease characterized by persistent air flow obstruction, which was the third leading cause of death in China. The incidence of COPD is steadily and increasing and has been a globally sever disease. Accordingly, it is urgently needed to explore how to diagnose and treat COPD timely. This study aims to find key genes to diagnose COPD as soon as possible to avoid COPD processing and analyze immune cell infiltration between COPD early stage and late stage. Two GEO datasets were merged as the merge data for analyses. 157 DEGs were used for GSEA analysis to find the pathway between COPD early stage and late stage. Above all, gene EXPH5 stood out from the screen as the most likely candidate diagnosis biomarker of COPD indicating the late-stage by least LASSO and SVM-RFE. ROC curves of EXPH5 were applied to represent the discriminatory ability through the area under the curve which is the gold standard to evaluate the accuracy of diagnosis and survival rate. The CIBERSORT algorithm was used to assess the distribution of tissue-infiltrating immune cells between two COPD stages. The diagnosis biomarker, gene EXPH5 had a positive correlation with NK cells resting; mast cell resting, eosinophils, and negative correlation with T cell gamma delta, macrophages M1, which underscore the role of gene and immune cell infiltration. To make results more reliable, we further analyzed the gene EXPH5 expression in single-cell transcriptome data and showed again that EXPH5 genes significantly downregulated in the late stage of COPD especially in the main lung cell types AT1 and AT2. In a word, our study identified genes EXPH5 as a marker gene, which adds to the knowledge for clinical diagnosis and pharmaceutical design of COPD.
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Affiliation(s)
- Yuwei Yang
- College of Pulmonary & Critical Care Medicine, Chinese PLA General Hospital, Beijing, 100091, China; Beijing Key Laboratory of OTIR, Beijing, 100091, China.
| | - Yan Cao
- College of Pulmonary & Critical Care Medicine, Chinese PLA General Hospital, Beijing, 100091, China; Beijing Key Laboratory of OTIR, Beijing, 100091, China.
| | - Xiaobo Han
- College of Pulmonary & Critical Care Medicine, Chinese PLA General Hospital, Beijing, 100091, China; Beijing Key Laboratory of OTIR, Beijing, 100091, China.
| | - Xihui Ma
- College of Pulmonary & Critical Care Medicine, Chinese PLA General Hospital, Beijing, 100091, China; Beijing Key Laboratory of OTIR, Beijing, 100091, China.
| | - Rui Li
- College of Pulmonary & Critical Care Medicine, Chinese PLA General Hospital, Beijing, 100091, China; Hebei North Universit, Zhangjiakou, 075000, China.
| | - Rentao Wang
- College of Pulmonary & Critical Care Medicine, Chinese PLA General Hospital, Beijing, 100091, China; Beijing Key Laboratory of OTIR, Beijing, 100091, China.
| | - Li Xiao
- College of Pulmonary & Critical Care Medicine, Chinese PLA General Hospital, Beijing, 100091, China; Beijing Key Laboratory of OTIR, Beijing, 100091, China.
| | - Lixin Xie
- College of Pulmonary & Critical Care Medicine, Chinese PLA General Hospital, Beijing, 100091, China; Beijing Key Laboratory of OTIR, Beijing, 100091, China.
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An efficient lung disease classification from X-ray images using hybrid Mask-RCNN and BiDLSTM. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.104340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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15
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Spectral features and optimal Hierarchical attention networks for pulmonary abnormality detection from the respiratory sound signals. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103905] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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16
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Neili Z, Sundaraj K. A comparative study of the spectrogram, scalogram, melspectrogram and gammatonegram time-frequency representations for the classification of lung sounds using the ICBHI database based on CNNs. BIOMED ENG-BIOMED TE 2022; 67:367-390. [PMID: 35926850 DOI: 10.1515/bmt-2022-0180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 06/21/2022] [Indexed: 11/15/2022]
Abstract
In lung sound classification using deep learning, many studies have considered the use of short-time Fourier transform (STFT) as the most commonly used 2D representation of the input data. Consequently, STFT has been widely used as an analytical tool, but other versions of the representation have also been developed. This study aims to evaluate and compare the performance of the spectrogram, scalogram, melspectrogram and gammatonegram representations, and provide comparative information to users regarding the suitability of these time-frequency (TF) techniques in lung sound classification. Lung sound signals used in this study were obtained from the ICBHI 2017 respiratory sound database. These lung sound recordings were converted into images of spectrogram, scalogram, melspectrogram and gammatonegram TF representations respectively. The four types of images were fed separately into the VGG16, ResNet-50 and AlexNet deep-learning architectures. Network performances were analyzed and compared based on accuracy, precision, recall and F1-score. The results of the analysis on the performance of the four representations using these three commonly used CNN deep-learning networks indicate that the generated gammatonegram and scalogram TF images coupled with ResNet-50 achieved maximum classification accuracies.
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Affiliation(s)
- Zakaria Neili
- Electronics Department, University of Badji Mokhtar Annaba, Annaba, Algeria
| | - Kenneth Sundaraj
- Faculty of Electronics and Computer Engineering, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia
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Cao Z, Wang Y, Zheng W, Yin L, Tang Y, Miao W, Liu S, Yang B. The algorithm of stereo vision and shape from shading based on endoscope imaging. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103658] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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18
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Kim Y, Hyon Y, Lee S, Woo SD, Ha T, Chung C. The coming era of a new auscultation system for analyzing respiratory sounds. BMC Pulm Med 2022; 22:119. [PMID: 35361176 PMCID: PMC8969404 DOI: 10.1186/s12890-022-01896-1] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 03/20/2022] [Indexed: 01/28/2023] Open
Abstract
Auscultation with stethoscope has been an essential tool for diagnosing the patients with respiratory disease. Although auscultation is non-invasive, rapid, and inexpensive, it has intrinsic limitations such as inter-listener variability and subjectivity, and the examination must be performed face-to-face. Conventional stethoscope could not record the respiratory sounds, so it was impossible to share the sounds. Recent innovative digital stethoscopes have overcome the limitations and enabled clinicians to store and share the sounds for education and discussion. In particular, the recordable stethoscope made it possible to analyze breathing sounds using artificial intelligence, especially based on neural network. Deep learning-based analysis with an automatic feature extractor and convoluted neural network classifier has been applied for the accurate analysis of respiratory sounds. In addition, the current advances in battery technology, embedded processors with low power consumption, and integrated sensors make possible the development of wearable and wireless stethoscopes, which can help to examine patients living in areas of a shortage of doctors or those who need isolation. There are still challenges to overcome, such as the analysis of complex and mixed respiratory sounds and noise filtering, but continuous research and technological development will facilitate the transition to a new era of a wearable and smart stethoscope.
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Affiliation(s)
- Yoonjoo Kim
- Division of Pulmonology and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Chungnam National University, Daejeon, 34134, Korea
| | - YunKyong Hyon
- Division of Industrial Mathematics, National Institute for Mathematical Sciences, 70, Yuseong-daero 1689 beon-gil, Yuseong-gu, Daejeon, 34047, Republic of Korea
| | - Sunju Lee
- Division of Industrial Mathematics, National Institute for Mathematical Sciences, 70, Yuseong-daero 1689 beon-gil, Yuseong-gu, Daejeon, 34047, Republic of Korea
| | - Seong-Dae Woo
- Division of Pulmonology and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Chungnam National University, Daejeon, 34134, Korea
| | - Taeyoung Ha
- Division of Industrial Mathematics, National Institute for Mathematical Sciences, 70, Yuseong-daero 1689 beon-gil, Yuseong-gu, Daejeon, 34047, Republic of Korea.
| | - Chaeuk Chung
- Division of Pulmonology and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Chungnam National University, Daejeon, 34134, Korea. .,Infection Control Convergence Research Center, Chungnam National University School of Medicine, Daejeon, 35015, Republic of Korea.
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19
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Arrhythmia classification of LSTM autoencoder based on time series anomaly detection. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103228] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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20
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Zulfiqar R, Majeed F, Irfan R, Rauf HT, Benkhelifa E, Belkacem AN. Abnormal Respiratory Sounds Classification Using Deep CNN Through Artificial Noise Addition. Front Med (Lausanne) 2021; 8:714811. [PMID: 34869413 PMCID: PMC8635523 DOI: 10.3389/fmed.2021.714811] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 10/07/2021] [Indexed: 11/29/2022] Open
Abstract
Respiratory sound (RS) attributes and their analyses structure a fundamental piece of pneumonic pathology, and it gives symptomatic data regarding a patient's lung. A couple of decades back, doctors depended on their hearing to distinguish symptomatic signs in lung audios by utilizing the typical stethoscope, which is usually considered a cheap and secure method for examining the patients. Lung disease is the third most ordinary cause of death worldwide, so; it is essential to classify the RS abnormality accurately to overcome the death rate. In this research, we have applied Fourier analysis for the visual inspection of abnormal respiratory sounds. Spectrum analysis was done through Artificial Noise Addition (ANA) in conjunction with different deep convolutional neural networks (CNN) to classify the seven abnormal respiratory sounds—both continuous (CAS) and discontinuous (DAS). The proposed framework contains an adaptive mechanism of adding a similar type of noise to unhealthy respiratory sounds. ANA makes sound features enough reach to be identified more accurately than the respiratory sounds without ANA. The obtained results using the proposed framework are superior to previous techniques since we simultaneously considered the seven different abnormal respiratory sound classes.
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Affiliation(s)
- Rizwana Zulfiqar
- Faculty of Computing and Information Technology, University of Gujrat, Gujrat, Pakistan
| | - Fiaz Majeed
- Faculty of Computing and Information Technology, University of Gujrat, Gujrat, Pakistan
| | - Rizwana Irfan
- Department of Information Technology, College of Computing and Information Technology at Khulais, University of Jeddah, Jeddah, Saudi Arabia
| | - Hafiz Tayyab Rauf
- Centre for Smart Systems, AI and Cybersecurity, Staffordshire University, Stoke-on-Trent, United Kingdom
| | - Elhadj Benkhelifa
- Cloud Computing and Applications Reseach Lab, Staffordshire University, Stoke-on-Trent, United Kingdom
| | - Abdelkader Nasreddine Belkacem
- Department of Computer and Network Engineering, College of Information Technology, UAE University, Al Ain, United Arab Emirates
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Surucu M, Isler Y, Perc M, Kara R. Convolutional neural networks predict the onset of paroxysmal atrial fibrillation: Theory and applications. CHAOS (WOODBURY, N.Y.) 2021; 31:113119. [PMID: 34881615 DOI: 10.1063/5.0069272] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 10/04/2021] [Indexed: 06/13/2023]
Abstract
In this study, we aimed to detect paroxysmal atrial fibrillation episodes before they occur so that patients can take precautions before putting their and others' lives in potentially life-threatening danger. We used the atrial fibrillation prediction database, open data from PhysioNet, and assembled our process based on convolutional neural networks. Conventional heart rate variability features are calculated from time-domain measures, frequency-domain measures using power spectral density estimations, time-frequency-domain measures using wavelet transform, and nonlinear Poincaré plot measures. In addition, we also applied an alternative heart rate normalization, which gave promising results only in a few studies, before calculating these heart rate variability features. We used these features directly and their normalized versions using min-max normalization and z-score normalization methods. Thus, heart rate variability features extracted from six different combinations of these normalizations, in addition to no normalization cases, were applied to the convolutional neural network classifier. We tuned the classifiers' hyperparameters using 90% of feature sets and tested the classifiers' performances using 10% of feature sets. The proposed approach resulted in 87.76% accuracy, 91.30% precision, 80.04% recall, and 87.50% f1-score in heart rate variability with z-score feature normalization. When the heart rate normalization was also utilized, the suggested method gave 100% accuracy, 100% precision, 100% recall, and 100% f1-score in heart rate variability with z-score feature normalization. The proposed method with heart rate normalization and z-score normalization methods resulted in better classification performance than similar studies in the literature. By comparing the existing studies, we conclude that our approach provides a much better tool to determine a near-future paroxysmal atrial fibrillation episode. However, although the achieved benchmarks are impressive, we note that the approach needs to be supported by other studies and on other datasets before clinical trials.
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Affiliation(s)
- M Surucu
- Department of Computer Engineering, Duzce University, 81620 Duzce, Turkey
| | - Y Isler
- Department of Biomedical Engineering, Izmir Katip Celebi University, Cigli, 35620 Izmir, Turkey
| | - M Perc
- Faculty of Natural Sciences and Mathematics, University of Maribor, Koroška cesta 160, 2000 Maribor, Slovenia
| | - R Kara
- Department of Computer Engineering, Duzce University, 81620 Duzce, Turkey
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22
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Yu H, Zhao J, Liu D, Chen Z, Sun J, Zhao X. Multi-channel lung sounds intelligent diagnosis of chronic obstructive pulmonary disease. BMC Pulm Med 2021; 21:321. [PMID: 34654400 PMCID: PMC8518292 DOI: 10.1186/s12890-021-01682-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 09/29/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Chronic obstructive pulmonary disease (COPD) is a chronic respiratory disease that seriously threatens people's health, with high morbidity and mortality worldwide. At present, the clinical diagnosis methods of COPD are time-consuming, invasive, and radioactive. Therefore, it is urgent to develop a non-invasive and rapid COPD severity diagnosis technique suitable for daily screening in clinical practice. RESULTS This study established an effective model for the preliminary diagnosis of COPD severity using lung sounds with few channels. Firstly, the time-frequency-energy features of 12 channels lung sounds were extracted by Hilbert-Huang transform. And then, channels and features were screened by the reliefF algorithm. Finally, the feature sets were input into a support vector machine to diagnose COPD severity, and the performance with Bayes, decision tree, and deep belief network was compared. Experimental results show that high classification performance using only 4-channel lung sounds of L1, L2, L3, and L4 channels can be achieved by the proposed model. The accuracy, sensitivity, and specificity of mild COPD and moderate + severe COPD were 89.13%, 87.72%, and 91.01%, respectively. The classification performance rates of moderate COPD and severe COPD were 94.26%, 97.32%, and 89.93% for accuracy, sensitivity, and specificity, respectively. CONCLUSION This model provides a standardized evaluation with high classification performance rates, which can assist doctors to complete the preliminary diagnosis of COPD severity immediately, and has important clinical significance.
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Affiliation(s)
- Hui Yu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, 300072 China
- Department of Biomedical Engineering, Tianjin Key Laboratory of Biomedical Detecting Techniques and Instruments, Tianjin University, Tianjin, 300072 China
| | - Jing Zhao
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, 300072 China
- Department of Biomedical Engineering, Tianjin Key Laboratory of Biomedical Detecting Techniques and Instruments, Tianjin University, Tianjin, 300072 China
| | - Dongyi Liu
- Department of Biomedical Engineering, Tianjin Key Laboratory of Biomedical Detecting Techniques and Instruments, Tianjin University, Tianjin, 300072 China
| | - Zhen Chen
- Department of Biomedical Engineering, Tianjin Key Laboratory of Biomedical Detecting Techniques and Instruments, Tianjin University, Tianjin, 300072 China
| | - Jinglai Sun
- Department of Biomedical Engineering, Tianjin Key Laboratory of Biomedical Detecting Techniques and Instruments, Tianjin University, Tianjin, 300072 China
| | - Xiaoyun Zhao
- Chest Hospital of Tianjin University, Tianjin, 300051 China
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Kim Y, Hyon Y, Jung SS, Lee S, Yoo G, Chung C, Ha T. Respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning. Sci Rep 2021; 11:17186. [PMID: 34433880 PMCID: PMC8387488 DOI: 10.1038/s41598-021-96724-7] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Accepted: 08/12/2021] [Indexed: 11/09/2022] Open
Abstract
Auscultation has been essential part of the physical examination; this is non-invasive, real-time, and very informative. Detection of abnormal respiratory sounds with a stethoscope is important in diagnosing respiratory diseases and providing first aid. However, accurate interpretation of respiratory sounds requires clinician's considerable expertise, so trainees such as interns and residents sometimes misidentify respiratory sounds. To overcome such limitations, we tried to develop an automated classification of breath sounds. We utilized deep learning convolutional neural network (CNN) to categorize 1918 respiratory sounds (normal, crackles, wheezes, rhonchi) recorded in the clinical setting. We developed the predictive model for respiratory sound classification combining pretrained image feature extractor of series, respiratory sound, and CNN classifier. It detected abnormal sounds with an accuracy of 86.5% and the area under the ROC curve (AUC) of 0.93. It further classified abnormal lung sounds into crackles, wheezes, or rhonchi with an overall accuracy of 85.7% and a mean AUC of 0.92. On the other hand, as a result of respiratory sound classification by different groups showed varying degree in terms of accuracy; the overall accuracies were 60.3% for medical students, 53.4% for interns, 68.8% for residents, and 80.1% for fellows. Our deep learning-based classification would be able to complement the inaccuracies of clinicians' auscultation, and it may aid in the rapid diagnosis and appropriate treatment of respiratory diseases.
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Affiliation(s)
- Yoonjoo Kim
- Division of Pulmonology and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Chungnam National University, Daejeon, 34134, Republic of Korea
| | - YunKyong Hyon
- Division of Medical Mathematics, National Institute for Mathematical Sciences, Daejeon, 34047, Republic of Korea
| | - Sung Soo Jung
- Division of Pulmonology and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Chungnam National University, Daejeon, 34134, Republic of Korea
| | - Sunju Lee
- Division of Medical Mathematics, National Institute for Mathematical Sciences, Daejeon, 34047, Republic of Korea
| | - Geon Yoo
- Clinical Research Division, National Institute of Food and Drug Safety Evaluation, Cheongju-si, Chungcheongbuk-do, Republic of Korea
| | - Chaeuk Chung
- Division of Pulmonology and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Chungnam National University, Daejeon, 34134, Republic of Korea. .,Infection Control Convergence Research Center, Chungnam National University School of Medicine, Daejeon, 35015, Republic of Korea.
| | - Taeyoung Ha
- Division of Medical Mathematics, National Institute for Mathematical Sciences, Daejeon, 34047, Republic of Korea.
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Gender classification on digital dental x-ray images using deep convolutional neural network. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102939] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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25
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Hasan MK, Roy S, Mondal C, Alam MA, E Elahi MT, Dutta A, Uddin Raju ST, Jawad MT, Ahmad M. Dermo-DOCTOR: A framework for concurrent skin lesion detection and recognition using a deep convolutional neural network with end-to-end dual encoders. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102661] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
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26
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Prediction of atrial fibrillation inducibility using spatiotemporal activation analysis combined with network mapping. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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27
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28
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Lin CH, Wu JX, Kan CD, Chen PY, Chen WL. Arteriovenous shunt stenosis assessment based on empirical mode decomposition and 1D-convolutional neural network: Clinical trial stage. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102461] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Zaki AM, El-Nagar AM, El-Bardini M, Soliman FAS. Deep learning controller for nonlinear system based on Lyapunov stability criterion. Neural Comput Appl 2021. [DOI: 10.1007/s00521-020-05077-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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Srivastava A, Jain S, Miranda R, Patil S, Pandya S, Kotecha K. Deep learning based respiratory sound analysis for detection of chronic obstructive pulmonary disease. PeerJ Comput Sci 2021; 7:e369. [PMID: 33817019 PMCID: PMC7959628 DOI: 10.7717/peerj-cs.369] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 01/03/2021] [Indexed: 05/27/2023]
Abstract
In recent times, technologies such as machine learning and deep learning have played a vital role in providing assistive solutions to a medical domain's challenges. They also improve predictive accuracy for early and timely disease detection using medical imaging and audio analysis. Due to the scarcity of trained human resources, medical practitioners are welcoming such technology assistance as it provides a helping hand to them in coping with more patients. Apart from critical health diseases such as cancer and diabetes, the impact of respiratory diseases is also gradually on the rise and is becoming life-threatening for society. The early diagnosis and immediate treatment are crucial in respiratory diseases, and hence the audio of the respiratory sounds is proving very beneficial along with chest X-rays. The presented research work aims to apply Convolutional Neural Network based deep learning methodologies to assist medical experts by providing a detailed and rigorous analysis of the medical respiratory audio data for Chronic Obstructive Pulmonary detection. In the conducted experiments, we have used a Librosa machine learning library features such as MFCC, Mel-Spectrogram, Chroma, Chroma (Constant-Q) and Chroma CENS. The presented system could also interpret the severity of the disease identified, such as mild, moderate, or acute. The investigation results validate the success of the proposed deep learning approach. The system classification accuracy has been enhanced to an ICBHI score of 93%. Furthermore, in the conducted experiments, we have applied K-fold Cross-Validation with ten splits to optimize the performance of the presented deep learning approach.
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Affiliation(s)
- Arpan Srivastava
- CS&IT Dept, Symbiosis Insitute of Technology, Symbiosis International (Deemed University), Pune, Maharastra, India
| | - Sonakshi Jain
- CS&IT Dept, Symbiosis Insitute of Technology, Symbiosis International (Deemed University), Pune, Maharastra, India
| | - Ryan Miranda
- CS&IT Dept, Symbiosis Insitute of Technology, Symbiosis International (Deemed University), Pune, Maharastra, India
| | - Shruti Patil
- Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International (Deemed University), Pune, Maharastra, India
| | - Sharnil Pandya
- Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International (Deemed University), Pune, Maharastra, India
| | - Ketan Kotecha
- Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International (Deemed University), Pune, Maharastra, India
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Fajardo JM, Gomez O, Prieto F. EMG hand gesture classification using handcrafted and deep features. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102210] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Chakraborty J, Nandy A. Discrete wavelet transform based data representation in deep neural network for gait abnormality detection. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.102076] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Altan G, Kutlu Y, Allahverdi N. Deep Learning on Computerized Analysis of Chronic Obstructive Pulmonary Disease. IEEE J Biomed Health Inform 2019; 24:1344-1350. [PMID: 31369388 DOI: 10.1109/jbhi.2019.2931395] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
GOAL Chronic obstructive pulmonary disease (COPD) is one of the deadliest diseases in the world. Because COPD is an incurable disease and requires considerable time to be diagnosed even by an experienced specialist, it becomes important to provide analysis abnormalities in simple ways. The aim of the study is comparing multiple machine learning algorithms for the early diagnosis of COPD using multi-channel lung sounds. METHODS Deep learning is an efficient machine-learning algorithm, which comprises unsupervised training to reduce optimization and supervised training by a feature-based distribution of classification parameters. This study focuses on analyzing multichannel lung sounds using statistical features of frequency modulations that are extracted using the Hilbert-Huang transform. RESULTS Deep learning algorithm was used in the classification stage of the proposed model to separate the patients with COPD and healthy subjects. The proposed DL model with the Hilbert-Huang transform based statistical features was successful in achieving high classification performance rates of 93.67%, 91%, and 96.33% for accuracy, sensitivity, and specificity, respectively. CONCLUSION The proposed computerized analysis of the multi-channel lung sounds using DL algorithms provides a standardized assessment with high classification performance. SIGNIFICANCE Our study is a pioneer study that directly focuses on the lung sounds to separate COPD and non-COPD patients. Analyzing 12-channel lung sounds gives the advantages of assessing the entire lung obstructions.
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