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Niu J, Cai M, Shi Y, Ren S, Xu W, Gao W, Luo Z, Reinhardt JM. A Novel Method for Automatic Identification of Breathing State. Sci Rep 2019; 9:103. [PMID: 30643176 PMCID: PMC6331627 DOI: 10.1038/s41598-018-36454-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Accepted: 11/20/2018] [Indexed: 11/20/2022] Open
Abstract
Sputum deposition blocks the airways of patients and leads to blood oxygen desaturation. Medical staff must periodically check the breathing state of intubated patients. This process increases staff workload. In this paper, we describe a system designed to acquire respiratory sounds from intubated subjects, extract the audio features, and classify these sounds to detect the presence of sputum. Our method uses 13 features extracted from the time-frequency spectrum of the respiratory sounds. To test our system, 220 respiratory sound samples were collected. Half of the samples were collected from patients with sputum present, and the remainder were collected from patients with no sputum present. Testing was performed based on ten-fold cross-validation. In the ten-fold cross-validation experiment, the logistic classifier identified breath sounds with sputum present with a sensitivity of 93.36% and a specificity of 93.36%. The feature extraction and classification methods are useful and reliable for sputum detection. This approach differs from waveform research and can provide a better visualization of sputum conditions. The proposed system can be used in the ICU to inform medical staff when sputum is present in a patient's trachea.
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Affiliation(s)
- Jinglong Niu
- School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China
- Department of Biomedical Engineering, University of Iowa, Iowa City, IA, 52246, United States
| | - Maolin Cai
- School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China
| | - Yan Shi
- School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China.
| | - Shuai Ren
- School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China
| | - Weiqing Xu
- School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China
| | - Wei Gao
- Department of Respiration, Beijing Anzhen Hospital, Capital Medical University, Beijing, 100029, China.
| | - Zujin Luo
- Department of Respiratory and Critical Care Medicine, Beijing Engineering Research Center of Respiratory and Critical Care Medicine, Beijing Institute of Respiratory Medicine, Beijing Chao-Yang Hospital,Capital Medical University, Beijing, 100043, China
| | - Joseph M Reinhardt
- Department of Biomedical Engineering, University of Iowa, Iowa City, IA, 52246, United States
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Abstract
Recent developments in sensor technology and computational analysis methods enable new strategies to measure and interpret lung acoustic signals that originate internally, such as breathing or vocal sounds, or are externally introduced, such as in chest percussion or airway insonification. A better understanding of these sounds has resulted in a new instrumentation that allows for highly accurate as well as portable options for measurement in the hospital, in the clinic, and even at home. This review outlines the instrumentation for acoustic stimulation and measurement of the lungs. We first review the fundamentals of acoustic lung signals and the pathophysiology of the diseases that these signals are used to detect. Then, we focus on different methods of measuring and creating signals that have been used in recent research for pulmonary disease diagnosis. These new methods, combined with signal processing and modeling techniques, lead to a reduction in noise and allow improved feature extraction and signal classification. We conclude by presenting the results of human subject studies taking advantage of both the instrumentation and signal processing tools to accurately diagnose common lung diseases. This paper emphasizes the active areas of research within modern lung acoustics and encourages the standardization of future work in this field.
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Badnjevic A, Gurbeta L, Custovic E. An Expert Diagnostic System to Automatically Identify Asthma and Chronic Obstructive Pulmonary Disease in Clinical Settings. Sci Rep 2018; 8:11645. [PMID: 30076356 PMCID: PMC6076307 DOI: 10.1038/s41598-018-30116-2] [Citation(s) in RCA: 83] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2017] [Accepted: 07/24/2018] [Indexed: 12/30/2022] Open
Abstract
Respiratory diseases such as asthma and chronic obstructive pulmonary disease (COPD), are affecting a huge percentage of the world’s population with mortality rates exceeding those of lung cancer and breast cancer combined. The major challenge is the number of patients who are incorrectly diagnosed. To address this, we developed an expert diagnostic system that can differentiate among patients with asthma, COPD or a normal lung function based on measurements of lung function and information about patient’s symptoms. To develop accurate classification algorithms, data from 3657 patients were used and then independently verified using data from 1650 patients collected over a period of two years. Our results demonstrate that the expert diagnostic system can correctly identify patients with asthma and COPD with sensitivity of 96.45% and specificity of 98.71%. Additionally, 98.71% of the patients with a normal lung function were correctly classified, which contributed to a 49.23% decrease in demand for conducting additional tests, therefore decreasing financial cost.
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Affiliation(s)
- Almir Badnjevic
- International Burch University, Faculty of Engineering and Natural Sciences, Genetics and Bioengineering Department, Sarajevo, Bosnia and Herzegovina. .,Medical Device Inspection Laboratory Verlab Ltd, Sarajevo, Bosnia and Herzegovina. .,Faculty of Electrical Engineering University of Sarajevo, Sarajevo, Bosnia and Herzegovina.
| | - Lejla Gurbeta
- International Burch University, Faculty of Engineering and Natural Sciences, Genetics and Bioengineering Department, Sarajevo, Bosnia and Herzegovina.,Technical faculty University of Bihac, Bihac, Bosnia and Herzegovina.,Medical Device Inspection Laboratory Verlab Ltd, Sarajevo, Bosnia and Herzegovina
| | - Eddie Custovic
- La Trobe Innovation & amp, Entrepreneurship Foundry at La Trobe University Melbourne, Melbourne, Australia
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Niu J, Shi Y, Cai M, Cao Z, Wang D, Zhang Z, Zhang XD. Detection of sputum by interpreting the time-frequency distribution of respiratory sound signal using image processing techniques. Bioinformatics 2018; 34:820-827. [PMID: 29040453 PMCID: PMC6192228 DOI: 10.1093/bioinformatics/btx652] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2017] [Revised: 07/25/2017] [Accepted: 10/12/2017] [Indexed: 11/14/2022] Open
Abstract
Motivation Sputum in the trachea is hard to expectorate and detect directly for the patients who are unconscious, especially those in Intensive Care Unit. Medical staff should always check the condition of sputum in the trachea. This is time-consuming and the necessary skills are difficult to acquire. Currently, there are few automatic approaches to serve as alternatives to this manual approach. Results We develop an automatic approach to diagnose the condition of the sputum. Our approach utilizes a system involving a medical device and quantitative analytic methods. In this approach, the time-frequency distribution of respiratory sound signals, determined from the spectrum, is treated as an image. The sputum detection is performed by interpreting the patterns in the image through the procedure of preprocessing and feature extraction. In this study, 272 respiratory sound samples (145 sputum sound and 127 non-sputum sound samples) are collected from 12 patients. We apply the method of leave-one out cross-validation to the 12 patients to assess the performance of our approach. That is, out of the 12 patients, 11 are randomly selected and their sound samples are used to predict the sound samples in the remaining one patient. The results show that our automatic approach can classify the sputum condition at an accuracy rate of 83.5%. Availability and implementation The matlab codes and examples of datasets explored in this work are available at Bioinformatics online. Contact yesoyou@gmail.com or douglaszhang@umac.mo. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jinglong Niu
- School of Automation Science and Electrical Engineering, Beihang
University, Beijing, China
- Beijing Engineering Research Center of Diagnosis and Treatment of
Respiratory and Critical Care Medicine, Beijing Chaoyang Hospital, Beijing, China
| | - Yan Shi
- School of Automation Science and Electrical Engineering, Beihang
University, Beijing, China
- Beijing Engineering Research Center of Diagnosis and Treatment of
Respiratory and Critical Care Medicine, Beijing Chaoyang Hospital, Beijing, China
- Faculty of Health Sciences, University of Macau, Taipa, Macau,
China
- The State Key Laboratory of Fluid Power Transmission and Control,
Zhejiang University, Hangzhou, China
| | - Maolin Cai
- School of Automation Science and Electrical Engineering, Beihang
University, Beijing, China
- Beijing Engineering Research Center of Diagnosis and Treatment of
Respiratory and Critical Care Medicine, Beijing Chaoyang Hospital, Beijing, China
| | - Zhixin Cao
- Beijing Engineering Research Center of Diagnosis and Treatment of
Respiratory and Critical Care Medicine, Beijing Chaoyang Hospital, Beijing, China
| | - Dandan Wang
- Faculty of Health Sciences, University of Macau, Taipa, Macau,
China
| | - Zhaozhi Zhang
- Department of Statistical Science, Duke University, Durham, NC,
USA
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Hassanzad M, Orooji A, Valinejadi A, Velayati A. A fuzzy rule-based expert system for diagnosing cystic fibrosis. Electron Physician 2017; 9:5974-5984. [PMID: 29560150 PMCID: PMC5843424 DOI: 10.19082/5974] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2017] [Accepted: 11/27/2017] [Indexed: 11/20/2022] Open
Abstract
Background Finding a valid diagnosis is mostly a prolonged process. Current advances in the sector of artificial intelligence have led to the appearance of expert systems that enrich the experiences and capabilities of doctors for making decisions for their patients. Objective The objective of this research was developing a fuzzy expert system for diagnosing Cystic Fibrosis (CF). Methods Defining the risk factors and then, designing the fuzzy expert system for diagnosis of CF were carried out in this cross-sectional study. To evaluate the performance of the proposed system, a dataset that corresponded to 70 patients with respiratory disease who were serially admitted to the CF Clinic in the Pediatric Respiratory Diseases Center, Masih Daneshvari Hospital in Tehran, Iran during August 2016 to January 2017 was considered. Whole procedures of system construction were implemented in a MATLAB environment. Results Results showed that the suggested system can be used as a strong diagnostic tool with 93.02% precision, 89.29% specificity, 95.24% sensitivity and 92.86% accuracy for diagnosing CF. There was also a good relationship between the user and the system through the appealing user interface. Conclusion The system is equipped with information, knowledge, and expertise from certified specialists; hence, as a training tool it can be useful for new physicians. It is worth mentioning that the accomplishment of this project depends on advocacy of decision making in CF diagnosis. Nevertheless, it is expected that the system will reduce the number of false positives and false negatives in unusual cases.
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Affiliation(s)
- Maryam Hassanzad
- M.D., Associate Professor, Pediatric Respiratory Disease Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Masih Daneshvari Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Azam Orooji
- Ph.D. Candidate of Medical Informatics, Department of Health Information Management and Technology, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Ali Valinejadi
- Ph.D. of Health Information Management, Assistant Professor, Social Determinants of Health Research Center, Department of Health Information Technology, Semnan University of Medical Sciences, Semnan, Iran
| | - Aliakbar Velayati
- M.D., Distinguished Professor, Mycobacteriology Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Masih Daneshvari Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Tartarisco G, Tonacci A, Minciullo PL, Billeci L, Pioggia G, Incorvaia C, Gangemi S. The soft computing-based approach to investigate allergic diseases: a systematic review. Clin Mol Allergy 2017; 15:10. [PMID: 28413358 PMCID: PMC5390370 DOI: 10.1186/s12948-017-0066-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Accepted: 03/29/2017] [Indexed: 01/23/2023] Open
Abstract
BACKGROUND Early recognition of inflammatory markers and their relation to asthma, adverse drug reactions, allergic rhinitis, atopic dermatitis and other allergic diseases is an important goal in allergy. The vast majority of studies in the literature are based on classic statistical methods; however, developments in computational techniques such as soft computing-based approaches hold new promise in this field. OBJECTIVE The aim of this manuscript is to systematically review the main soft computing-based techniques such as artificial neural networks, support vector machines, bayesian networks and fuzzy logic to investigate their performances in the field of allergic diseases. METHODS The review was conducted following PRISMA guidelines and the protocol was registered within PROSPERO database (CRD42016038894). The research was performed on PubMed and ScienceDirect, covering the period starting from September 1, 1990 through April 19, 2016. RESULTS The review included 27 studies related to allergic diseases and soft computing performances. We observed promising results with an overall accuracy of 86.5%, mainly focused on asthmatic disease. The review reveals that soft computing-based approaches are suitable for big data analysis and can be very powerful, especially when dealing with uncertainty and poorly characterized parameters. Furthermore, they can provide valuable support in case of lack of data and entangled cause-effect relationships, which make it difficult to assess the evolution of disease. CONCLUSIONS Although most works deal with asthma, we believe the soft computing approach could be a real breakthrough and foster new insights into other allergic diseases as well.
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Affiliation(s)
- Gennaro Tartarisco
- Messina Unit, National Research Council of Italy (CNR)-Institute of Applied Science and Intelligent System (ISASI), Messina, Italy
| | - Alessandro Tonacci
- Pisa Unit, National Research Council of Italy (CNR)-Institute of Clinical Physiology (IFC), Pisa, Italy
| | - Paola Lucia Minciullo
- School and Division of Allergy and Clinical Immunology, Department of Clinical and Experimental Medicine, University Hospital “G. Martino”, Messina, Italy
| | - Lucia Billeci
- Pisa Unit, National Research Council of Italy (CNR)-Institute of Clinical Physiology (IFC), Pisa, Italy
| | - Giovanni Pioggia
- Messina Unit, National Research Council of Italy (CNR)-Institute of Applied Science and Intelligent System (ISASI), Messina, Italy
| | | | - Sebastiano Gangemi
- School and Division of Allergy and Clinical Immunology, Department of Clinical and Experimental Medicine, University Hospital “G. Martino”, Messina, Italy
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Badnjevic A, Cifrek M, Koruga D, Osmankovic D. Neuro-fuzzy classification of asthma and chronic obstructive pulmonary disease. BMC Med Inform Decis Mak 2015; 15 Suppl 3:S1. [PMID: 26391218 PMCID: PMC4705495 DOI: 10.1186/1472-6947-15-s3-s1] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Background This paper presents a system for classification of asthma and chronic obstructive pulmonary disease (COPD) based on fuzzy rules and the trained neural network. Methods Fuzzy rules and neural network parameters are defined according to Global Initiative for Asthma (GINA) and Global Initiative for chronic Obstructive Lung Disease (GOLD) guidelines. For neural network training more than one thousand medical reports obtained from database of the company CareFusion were used. Afterwards the system was validated on 455 patients by physicians from the Clinical Centre University of Sarajevo. Results Out of 170 patients with asthma, 99.41% of patients were correctly classified. In addition, 99.19% of the 248 COPD patients were correctly classified. The system was 100% successful on 37 patients with normal lung function. Sensitivity of 99.28% and specificity of 100% in asthma and COPD classification were obtained. Conclusion Our neuro-fuzzy system for classification of asthma and COPD uses a combination of spirometry and Impulse Oscillometry System (IOS) test results, which in the very beginning enables more accurate classification. Additionally, using bronchodilatation and bronhoprovocation tests we get a complete patient's dynamic assessment, as opposed to the solution that provides a static assessment of the patient.
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Samad-Soltani T, Ghanei M, Langarizadeh M. Development of a Fuzzy Decision Support System to Determine the Severity of Obstructive Pulmonary in Chemical Injured Victims. Acta Inform Med 2015; 23:138-41. [PMID: 26236078 PMCID: PMC4499289 DOI: 10.5455/aim.2015.23.138-141] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2015] [Accepted: 04/15/2015] [Indexed: 11/03/2022] Open
Abstract
BACKGROUND Chronic Obstructive Pulmonary Disease (COPD) is the most common known complication of exposure to mustard gas. Thus, all clinical guidelines have provided some recommendation for diagnosis, clinical management and treatment of this disease. Decision support systems are used to increase the acceptance of clinical guidelines. The purpose of this research is to develop a CDSS to determine the severity of COPD in chemical injured victims. OBJECTIVES Development of a decision support system to determine the severity of COPD. PATIENTS AND METHODS First, the variables influencing to determining the severity of the disease was classified through studying the clinical guidelines. Then, the fuzzy model was implemented. To testing the system, the data from 50 patients were used. RESULTS the overall accuracy in determining the severity of the injury is equal to 92%, these indicators reflect the proper functioning of the system to assist the physician regarding the diagnosis of chronic obstructive pulmonary disease and determining its severity. CONCLUSIONS The CDSS has efficient results and satisfactory performance. Although, the medical expert systems cannot be expected to provide 100 percent correct responses, however, they can be useful in the areas of patient management, diagnosis and treatment planning.
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Affiliation(s)
- Taha Samad-Soltani
- Health Information Management Department, School of Health Management & Information Sciences, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Mostafa Ghanei
- Chemical Injuries Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Mostafa Langarizadeh
- Health Information Management Department, School of Health Management & Information Sciences, Iran University of Medical Sciences, Tehran, Iran
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Kunhimangalam R, Ovallath S, Joseph PK. A clinical decision support system with an integrated EMR for diagnosis of peripheral neuropathy. J Med Syst 2014; 38:38. [PMID: 24692180 DOI: 10.1007/s10916-014-0038-9] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2013] [Accepted: 03/13/2014] [Indexed: 11/26/2022]
Abstract
The prevalence of peripheral neuropathy in general population is ever increasing. The diagnosis and classification of peripheral neuropathies is often difficult as it involves careful clinical and electro-diagnostic examination by an expert neurologist. In developing countries a large percentage of the disease remains undiagnosed due to lack of adequate number of experts. In this study a novel clinical decision support system has been developed using a fuzzy expert system. The study was done to provide a solution to the demand of systems that can improve health care by accurate diagnosis in limited time, in the absence of specialists. It employs a graphical user interface and a fuzzy logic controller with rule viewer for identification of the type of peripheral neuropathy. An integrated medical records database is also developed for the storage and retrieval of the data. The system consists of 24 input fields, which includes the clinical values of the diagnostic test and the clinical symptoms. The output field is the disease diagnosis, whether it is Motor (Demyelinating/Axonopathy) neuropathy, sensory (Demyelinating/Axonopathy) neuropathy, mixed type or a normal case. The results obtained were compared with the expert's opinion and the system showed 93.27 % accuracy. The study aims at showing that Fuzzy Expert Systems may prove useful in providing diagnostic and predictive medical opinions. It enables the clinicians to arrive at a better diagnosis as it keeps the expert knowledge in an intelligent system to be used efficiently and effectively.
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Affiliation(s)
- Reeda Kunhimangalam
- National Institute of Technology, NIT Calicut (PO), Kozhikode, Kerala, India, 673601,
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Current World Literature. Curr Opin Allergy Clin Immunol 2013. [DOI: 10.1097/aci.0b013e3283619e49] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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