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Serrurier A, Neuschaefer-Rube C, Röhrig R. Past and Trends in Cough Sound Acquisition, Automatic Detection and Automatic Classification: A Comparative Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:2896. [PMID: 35458885 PMCID: PMC9027375 DOI: 10.3390/s22082896] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 04/07/2022] [Accepted: 04/08/2022] [Indexed: 11/16/2022]
Abstract
Cough is a very common symptom and the most frequent reason for seeking medical advice. Optimized care goes inevitably through an adapted recording of this symptom and automatic processing. This study provides an updated exhaustive quantitative review of the field of cough sound acquisition, automatic detection in longer audio sequences and automatic classification of the nature or disease. Related studies were analyzed and metrics extracted and processed to create a quantitative characterization of the state-of-the-art and trends. A list of objective criteria was established to select a subset of the most complete detection studies in the perspective of deployment in clinical practice. One hundred and forty-four studies were short-listed, and a picture of the state-of-the-art technology is drawn. The trend shows an increasing number of classification studies, an increase of the dataset size, in part from crowdsourcing, a rapid increase of COVID-19 studies, the prevalence of smartphones and wearable sensors for the acquisition, and a rapid expansion of deep learning. Finally, a subset of 12 detection studies is identified as the most complete ones. An unequaled quantitative overview is presented. The field shows a remarkable dynamic, boosted by the research on COVID-19 diagnosis, and a perfect adaptation to mobile health.
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Affiliation(s)
- Antoine Serrurier
- Institute of Medical Informatics, University Hospital of the RWTH Aachen, 52057 Aachen, Germany;
- Clinic for Phoniatrics, Pedaudiology & Communication Disorders, University Hospital of the RWTH Aachen, 52057 Aachen, Germany;
| | - Christiane Neuschaefer-Rube
- Clinic for Phoniatrics, Pedaudiology & Communication Disorders, University Hospital of the RWTH Aachen, 52057 Aachen, Germany;
| | - Rainer Röhrig
- Institute of Medical Informatics, University Hospital of the RWTH Aachen, 52057 Aachen, Germany;
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2
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Kruizinga MD, Zhuparris A, Dessing E, Krol FJ, Sprij AJ, Doll RJ, Stuurman FE, Exadaktylos V, Driessen GJA, Cohen AF. Development and technical validation of a smartphone-based pediatric cough detection algorithm. Pediatr Pulmonol 2022; 57:761-767. [PMID: 34964557 PMCID: PMC9306830 DOI: 10.1002/ppul.25801] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Revised: 11/17/2021] [Accepted: 12/13/2021] [Indexed: 11/06/2022]
Abstract
INTRODUCTION Coughing is a common symptom in pediatric lung disease and cough frequency has been shown to be correlated to disease activity in several conditions. Automated cough detection could provide a noninvasive digital biomarker for pediatric clinical trials or care. The aim of this study was to develop a smartphone-based algorithm that objectively and automatically counts cough sounds of children. METHODS The training set was composed of 3228 pediatric cough sounds and 480,780 noncough sounds from various publicly available sources and continuous sound recordings of 7 patients admitted due to respiratory disease. A Gradient Boost Classifier was fitted on the training data, which was subsequently validated on recordings from 14 additional patients aged 0-14 admitted to the pediatric ward due to respiratory disease. The robustness of the algorithm was investigated by repeatedly classifying a recording with the smartphone-based algorithm during various conditions. RESULTS The final algorithm obtained an accuracy of 99.7%, sensitivity of 47.6%, specificity of 99.96%, positive predictive value of 82.2% and negative predictive value 99.8% in the validation dataset. The correlation coefficient between manual- and automated cough counts in the validation dataset was 0.97 (p < .001). The intra- and interdevice reliability of the algorithm was adequate, and the algorithm performed best at an unobstructed distance of 0.5-1 m from the audio source. CONCLUSION This novel smartphone-based pediatric cough detection application can be used for longitudinal follow-up in clinical care or as digital endpoint in clinical trials.
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Affiliation(s)
- Matthijs D Kruizinga
- Centre for Human Drug Research, Leiden, The Netherlands.,Juliana Children's Hospital, HAGA Teaching Hospital, The Hague, The Netherlands.,Leiden University Medical Centre, Leiden, The Netherlands
| | | | - Eva Dessing
- Centre for Human Drug Research, Leiden, The Netherlands.,Juliana Children's Hospital, HAGA Teaching Hospital, The Hague, The Netherlands
| | - Fas J Krol
- Centre for Human Drug Research, Leiden, The Netherlands.,Leiden University Medical Centre, Leiden, The Netherlands
| | - Arwen J Sprij
- Juliana Children's Hospital, HAGA Teaching Hospital, The Hague, The Netherlands
| | | | | | | | - Gertjan J A Driessen
- Juliana Children's Hospital, HAGA Teaching Hospital, The Hague, The Netherlands.,Department of pediatrics, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Adam F Cohen
- Centre for Human Drug Research, Leiden, The Netherlands.,Leiden University Medical Centre, Leiden, The Netherlands
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Ijaz A, Nabeel M, Masood U, Mahmood T, Hashmi MS, Posokhova I, Rizwan A, Imran A. Towards using cough for respiratory disease diagnosis by leveraging Artificial Intelligence: A survey. INFORMATICS IN MEDICINE UNLOCKED 2022. [DOI: 10.1016/j.imu.2021.100832] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
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Chung Y, Jin J, Jo HI, Lee H, Kim SH, Chung SJ, Yoon HJ, Park J, Jeon JY. Diagnosis of Pneumonia by Cough Sounds Analyzed with Statistical Features and AI. SENSORS 2021; 21:s21217036. [PMID: 34770341 PMCID: PMC8586978 DOI: 10.3390/s21217036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 10/21/2021] [Accepted: 10/21/2021] [Indexed: 11/28/2022]
Abstract
Pneumonia is a serious disease often accompanied by complications, sometimes leading to death. Unfortunately, diagnosis of pneumonia is frequently delayed until physical and radiologic examinations are performed. Diagnosing pneumonia with cough sounds would be advantageous as a non-invasive test that could be performed outside a hospital. We aimed to develop an artificial intelligence (AI)-based pneumonia diagnostic algorithm. We collected cough sounds from thirty adult patients with pneumonia or the other causative diseases of cough. To quantify the cough sounds, loudness and energy ratio were used to represent the level and its spectral variations. These two features were used for constructing the diagnostic algorithm. To estimate the performance of developed algorithm, we assessed the diagnostic accuracy by comparing with the diagnosis by pulmonologists based on cough sound alone. The algorithm showed 90.0% sensitivity, 78.6% specificity and 84.9% overall accuracy for the 70 cases of cough sound in pneumonia group and 56 cases in non-pneumonia group. For same cases, pulmonologists correctly diagnosed the cough sounds with 56.4% accuracy. These findings showed that the proposed AI algorithm has value as an effective assistant technology to diagnose adult pneumonia patients with significant reliability.
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Affiliation(s)
- Youngbeen Chung
- Department of Mechanical Engineering, Hanyang University, 222 Wangsimri-ro, Seongdong-gu, Seoul 04763, Korea;
| | - Jie Jin
- School of Electromechanical and Automotive Engineering, Yantai University, 30 Qingquan Road, Laishan District, Yantai 264005, China;
| | - Hyun In Jo
- Department of Architectural Engineering, Hanyang University, 222 Wangsimri-ro, Seongdong-gu, Seoul 04763, Korea;
| | - Hyun Lee
- Department of Internal Medicine, Hanyang University Hospital, Hanyang University College of Medicine, 222 Wangsimri-ro, Seongdong-gu, Seoul 04763, Korea; (H.L.); (S.J.C.); (H.J.Y.)
| | - Sang-Heon Kim
- Department of Internal Medicine, Hanyang University Hospital, Hanyang University College of Medicine, 222 Wangsimri-ro, Seongdong-gu, Seoul 04763, Korea; (H.L.); (S.J.C.); (H.J.Y.)
- Correspondence: (S.-H.K.); (J.P.); Tel.: +82-02-2220-8336 (S.-H.K.); +82-02-2220-0424 (J.P.)
| | - Sung Jun Chung
- Department of Internal Medicine, Hanyang University Hospital, Hanyang University College of Medicine, 222 Wangsimri-ro, Seongdong-gu, Seoul 04763, Korea; (H.L.); (S.J.C.); (H.J.Y.)
| | - Ho Joo Yoon
- Department of Internal Medicine, Hanyang University Hospital, Hanyang University College of Medicine, 222 Wangsimri-ro, Seongdong-gu, Seoul 04763, Korea; (H.L.); (S.J.C.); (H.J.Y.)
| | - Junhong Park
- Department of Mechanical Engineering, Hanyang University, 222 Wangsimri-ro, Seongdong-gu, Seoul 04763, Korea;
- Correspondence: (S.-H.K.); (J.P.); Tel.: +82-02-2220-8336 (S.-H.K.); +82-02-2220-0424 (J.P.)
| | - Jin Yong Jeon
- Department of Medical and Digital Engineering, Hanyang University, 222 Wangsimri-ro, Seongdong-gu, Seoul 04763, Korea;
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Alqudaihi KS, Aslam N, Khan IU, Almuhaideb AM, Alsunaidi SJ, Ibrahim NMAR, Alhaidari FA, Shaikh FS, Alsenbel YM, Alalharith DM, Alharthi HM, Alghamdi WM, Alshahrani MS. Cough Sound Detection and Diagnosis Using Artificial Intelligence Techniques: Challenges and Opportunities. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:102327-102344. [PMID: 34786317 PMCID: PMC8545201 DOI: 10.1109/access.2021.3097559] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 07/09/2021] [Indexed: 06/02/2023]
Abstract
Coughing is a common symptom of several respiratory diseases. The sound and type of cough are useful features to consider when diagnosing a disease. Respiratory infections pose a significant risk to human lives worldwide as well as a significant economic downturn, particularly in countries with limited therapeutic resources. In this study we reviewed the latest proposed technologies that were used to control the impact of respiratory diseases. Artificial Intelligence (AI) is a promising technology that aids in data analysis and prediction of results, thereby ensuring people's well-being. We conveyed that the cough symptom can be reliably used by AI algorithms to detect and diagnose different types of known diseases including pneumonia, pulmonary edema, asthma, tuberculosis (TB), COVID19, pertussis, and other respiratory diseases. We also identified different techniques that produced the best results for diagnosing respiratory disease using cough samples. This study presents the most recent challenges, solutions, and opportunities in respiratory disease detection and diagnosis, allowing practitioners and researchers to develop better techniques.
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Affiliation(s)
- Kawther S. Alqudaihi
- Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Nida Aslam
- Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Irfan Ullah Khan
- Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Abdullah M. Almuhaideb
- Department of Networks and CommunicationsCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Shikah J. Alsunaidi
- Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Nehad M. Abdel Rahman Ibrahim
- Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Fahd A. Alhaidari
- Department of Networks and CommunicationsCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Fatema S. Shaikh
- Department of Computer Information SystemsCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Yasmine M. Alsenbel
- Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Dima M. Alalharith
- Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Hajar M. Alharthi
- Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Wejdan M. Alghamdi
- Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Mohammed S. Alshahrani
- Department of Emergency MedicineCollege of MedicineImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
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Miller E, Banerjee N, Zhu T. Smart homes that detect sneeze, cough, and face touching. SMART HEALTH (AMSTERDAM, NETHERLANDS) 2021; 19:100170. [PMID: 33521224 PMCID: PMC7836979 DOI: 10.1016/j.smhl.2020.100170] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Coughing, sneezing, and face touching activities are three primary ways of spreading disease. With the onset of COVID-19 it is paramount to monitor these activities at home and practice good hygiene. To help stop the spread of disease, we have developed a wireless sensing system capable of detecting voluntary coughs, sneezes, and face touching with alert based notifications sent to a mobile application. Our system uses radio frequency technology to capture motion, speed, direction, and range information from human activities. It does this by using a combination of a continuous wave Doppler and frequency modulated continuous wave radar. By observing a set of features related to the sensed motion, we designed a set of fuzzy logic IF-THEN rules that can differentiate each activity from each other with an overall accuracy of 96%. In addition, our system enables smart homes to detect and localize these activities at different distances up to 2.74 m, through walls, and with multiple people. We envision our system helping not only with prevention of COVID-19, but supporting contact tracing efforts by monitoring people's activities at home.
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Affiliation(s)
- Elishiah Miller
- Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, United States
| | - Nilanjan Banerjee
- Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, United States
| | - Ting Zhu
- Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, United States
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Monitoring Health Parameters of Elders to Support Independent Living and Improve Their Quality of Life. SENSORS 2021; 21:s21020517. [PMID: 33450904 PMCID: PMC7828366 DOI: 10.3390/s21020517] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 12/20/2020] [Accepted: 01/07/2021] [Indexed: 11/17/2022]
Abstract
Improving the well-being and quality of life of the elderly population is closely related to assisting them to effectively manage age-related conditions such as chronic illnesses and anxiety, and to maintain their independence and self-sufficiency as much as possible. This paper presents the design, architecture and implementation structure of an adaptive system for monitoring the health and well-being of the elderly. The system was designed following best practices of the Human-Centred Design approach involving representative end-users from the early stages.
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Soliński M, Łepek M, Kołtowski Ł. Automatic cough detection based on airflow signals for portable spirometry system. INFORMATICS IN MEDICINE UNLOCKED 2020. [DOI: 10.1016/j.imu.2020.100313] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
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