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Barata F, Shim J, Wu F, Langer P, Fleisch E. The Bitemporal Lens Model-toward a holistic approach to chronic disease prevention with digital biomarkers. JAMIA Open 2024; 7:ooae027. [PMID: 38596697 PMCID: PMC11000821 DOI: 10.1093/jamiaopen/ooae027] [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: 06/21/2023] [Revised: 01/22/2024] [Accepted: 03/14/2024] [Indexed: 04/11/2024] Open
Abstract
Objectives We introduce the Bitemporal Lens Model, a comprehensive methodology for chronic disease prevention using digital biomarkers. Materials and Methods The Bitemporal Lens Model integrates the change-point model, focusing on critical disease-specific parameters, and the recurrent-pattern model, emphasizing lifestyle and behavioral patterns, for early risk identification. Results By incorporating both the change-point and recurrent-pattern models, the Bitemporal Lens Model offers a comprehensive approach to preventive healthcare, enabling a more nuanced understanding of individual health trajectories, demonstrated through its application in cardiovascular disease prevention. Discussion We explore the benefits of the Bitemporal Lens Model, highlighting its capacity for personalized risk assessment through the integration of two distinct lenses. We also acknowledge challenges associated with handling intricate data across dual temporal dimensions, maintaining data integrity, and addressing ethical concerns pertaining to privacy and data protection. Conclusion The Bitemporal Lens Model presents a novel approach to enhancing preventive healthcare effectiveness.
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Affiliation(s)
- Filipe Barata
- Centre for Digital Health Interventions, ETH Zurich, Zürich, Zürich, 8092, Switzerland
| | - Jinjoo Shim
- Centre for Digital Health Interventions, ETH Zurich, Zürich, Zürich, 8092, Switzerland
| | - Fan Wu
- Centre for Digital Health Interventions, ETH Zurich, Zürich, Zürich, 8092, Switzerland
| | - Patrick Langer
- Centre for Digital Health Interventions, ETH Zurich, Zürich, Zürich, 8092, Switzerland
| | - Elgar Fleisch
- Centre for Digital Health Interventions, ETH Zurich, Zürich, Zürich, 8092, Switzerland
- Centre for Digital Health Interventions, University of St. Gallen, St. Gallen, St. Gallen, 9000, Switzerland
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Wieczorek K, Ananth S, Valazquez-Pimentel D. Acoustic biomarkers in asthma: a systematic review. J Asthma 2024:1-16. [PMID: 38634718 DOI: 10.1080/02770903.2024.2344156] [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: 01/18/2024] [Accepted: 04/13/2024] [Indexed: 04/19/2024]
Abstract
OBJECTIVE Current monitoring methods of asthma, such as peak expiratory flow testing, have important limitations. The emergence of automated acoustic sound analysis, capturing cough, wheeze, and inhaler use, offers a promising avenue for improving asthma diagnosis and monitoring. This systematic review evaluated the validity of acoustic biomarkers in supporting the diagnosis of asthma and its monitoring. DATA SOURCES A search was performed using two databases (PubMed and Embase) for all relevant studies published before November 2023. STUDY SELECTION 27 studies were included for analysis. Eligible studies focused on acoustic signals as digital biomarkers in asthma, utilizing recording devices to register or analyze sound. RESULTS Various respiratory acoustic signal types were analyzed, with cough and wheeze being predominant. Data collection methods included smartphones, custom sensors and digital stethoscopes. Across all studies, automated acoustic algorithms achieved average accuracy of cough and wheeze detection of 88.7% (range: 61.0 - 100.0%) with a median of 92.0%. The sensitivity of sound detection ranged from 54.0 to 100.0%, with a median of 90.3%; specificity ranged from 67.0 to 99.7%, with a median of 95.0%. Moreover, 70.4% (19/27) studies had a risk of bias identified. CONCLUSIONS This systematic review establishes the promising role of acoustic biomarkers, particularly cough and wheeze, in supporting the diagnosis of asthma and monitoring. The evidence suggests the potential for clinical integration of acoustic biomarkers, emphasizing the need for further validation in larger, clinically-diverse populations.
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Affiliation(s)
| | - Sachin Ananth
- London North West University Healthcare Trust, London, UK
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3
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Kapetanidis P, Kalioras F, Tsakonas C, Tzamalis P, Kontogiannis G, Karamanidou T, Stavropoulos TG, Nikoletseas S. Respiratory Diseases Diagnosis Using Audio Analysis and Artificial Intelligence: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2024; 24:1173. [PMID: 38400330 PMCID: PMC10893010 DOI: 10.3390/s24041173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 02/03/2024] [Accepted: 02/04/2024] [Indexed: 02/25/2024]
Abstract
Respiratory diseases represent a significant global burden, necessitating efficient diagnostic methods for timely intervention. Digital biomarkers based on audio, acoustics, and sound from the upper and lower respiratory system, as well as the voice, have emerged as valuable indicators of respiratory functionality. Recent advancements in machine learning (ML) algorithms offer promising avenues for the identification and diagnosis of respiratory diseases through the analysis and processing of such audio-based biomarkers. An ever-increasing number of studies employ ML techniques to extract meaningful information from audio biomarkers. Beyond disease identification, these studies explore diverse aspects such as the recognition of cough sounds amidst environmental noise, the analysis of respiratory sounds to detect respiratory symptoms like wheezes and crackles, as well as the analysis of the voice/speech for the evaluation of human voice abnormalities. To provide a more in-depth analysis, this review examines 75 relevant audio analysis studies across three distinct areas of concern based on respiratory diseases' symptoms: (a) cough detection, (b) lower respiratory symptoms identification, and (c) diagnostics from the voice and speech. Furthermore, publicly available datasets commonly utilized in this domain are presented. It is observed that research trends are influenced by the pandemic, with a surge in studies on COVID-19 diagnosis, mobile data acquisition, and remote diagnosis systems.
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Affiliation(s)
- Panagiotis Kapetanidis
- Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece (C.T.); (G.K.); (S.N.)
| | - Fotios Kalioras
- Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece (C.T.); (G.K.); (S.N.)
| | - Constantinos Tsakonas
- Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece (C.T.); (G.K.); (S.N.)
| | - Pantelis Tzamalis
- Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece (C.T.); (G.K.); (S.N.)
| | - George Kontogiannis
- Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece (C.T.); (G.K.); (S.N.)
| | - Theodora Karamanidou
- Pfizer Center for Digital Innovation, 55535 Thessaloniki, Greece; (T.K.); (T.G.S.)
| | | | - Sotiris Nikoletseas
- Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece (C.T.); (G.K.); (S.N.)
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4
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Vitazkova D, Foltan E, Kosnacova H, Micjan M, Donoval M, Kuzma A, Kopani M, Vavrinsky E. Advances in Respiratory Monitoring: A Comprehensive Review of Wearable and Remote Technologies. BIOSENSORS 2024; 14:90. [PMID: 38392009 PMCID: PMC10886711 DOI: 10.3390/bios14020090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 01/28/2024] [Accepted: 02/03/2024] [Indexed: 02/24/2024]
Abstract
This article explores the importance of wearable and remote technologies in healthcare. The focus highlights its potential in continuous monitoring, examines the specificity of the issue, and offers a view of proactive healthcare. Our research describes a wide range of device types and scientific methodologies, starting from traditional chest belts to their modern alternatives and cutting-edge bioamplifiers that distinguish breathing from chest impedance variations. We also investigated innovative technologies such as the monitoring of thorax micromovements based on the principles of seismocardiography, ballistocardiography, remote camera recordings, deployment of integrated optical fibers, or extraction of respiration from cardiovascular variables. Our review is extended to include acoustic methods and breath and blood gas analysis, providing a comprehensive overview of different approaches to respiratory monitoring. The topic of monitoring respiration with wearable and remote electronics is currently the center of attention of researchers, which is also reflected by the growing number of publications. In our manuscript, we offer an overview of the most interesting ones.
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Affiliation(s)
- Diana Vitazkova
- Institute of Electronics and Photonics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Ilkovicova 3, 81219 Bratislava, Slovakia; (E.F.); (H.K.); (M.M.); (M.D.); (A.K.)
| | - Erik Foltan
- Institute of Electronics and Photonics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Ilkovicova 3, 81219 Bratislava, Slovakia; (E.F.); (H.K.); (M.M.); (M.D.); (A.K.)
| | - Helena Kosnacova
- Institute of Electronics and Photonics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Ilkovicova 3, 81219 Bratislava, Slovakia; (E.F.); (H.K.); (M.M.); (M.D.); (A.K.)
- Department of Simulation and Virtual Medical Education, Faculty of Medicine, Comenius University, Sasinkova 4, 81272 Bratislava, Slovakia
| | - Michal Micjan
- Institute of Electronics and Photonics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Ilkovicova 3, 81219 Bratislava, Slovakia; (E.F.); (H.K.); (M.M.); (M.D.); (A.K.)
| | - Martin Donoval
- Institute of Electronics and Photonics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Ilkovicova 3, 81219 Bratislava, Slovakia; (E.F.); (H.K.); (M.M.); (M.D.); (A.K.)
| | - Anton Kuzma
- Institute of Electronics and Photonics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Ilkovicova 3, 81219 Bratislava, Slovakia; (E.F.); (H.K.); (M.M.); (M.D.); (A.K.)
| | - Martin Kopani
- Institute of Medical Physics, Biophysics, Informatics and Telemedicine, Faculty of Medicine, Comenius University, Sasinkova 2, 81272 Bratislava, Slovakia;
| | - Erik Vavrinsky
- Institute of Electronics and Photonics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Ilkovicova 3, 81219 Bratislava, Slovakia; (E.F.); (H.K.); (M.M.); (M.D.); (A.K.)
- Institute of Medical Physics, Biophysics, Informatics and Telemedicine, Faculty of Medicine, Comenius University, Sasinkova 2, 81272 Bratislava, Slovakia;
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Castro O, Mair JL, Salamanca-Sanabria A, Alattas A, Keller R, Zheng S, Jabir A, Lin X, Frese BF, Lim CS, Santhanam P, van Dam RM, Car J, Lee J, Tai ES, Fleisch E, von Wangenheim F, Tudor Car L, Müller-Riemenschneider F, Kowatsch T. Development of "LvL UP 1.0": a smartphone-based, conversational agent-delivered holistic lifestyle intervention for the prevention of non-communicable diseases and common mental disorders. Front Digit Health 2023; 5:1039171. [PMID: 37234382 PMCID: PMC10207359 DOI: 10.3389/fdgth.2023.1039171] [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: 09/07/2022] [Accepted: 04/06/2023] [Indexed: 05/28/2023] Open
Abstract
Background Non-communicable diseases (NCDs) and common mental disorders (CMDs) are the leading causes of death and disability worldwide. Lifestyle interventions via mobile apps and conversational agents present themselves as low-cost, scalable solutions to prevent these conditions. This paper describes the rationale for, and development of, "LvL UP 1.0″, a smartphone-based lifestyle intervention aimed at preventing NCDs and CMDs. Materials and Methods A multidisciplinary team led the intervention design process of LvL UP 1.0, involving four phases: (i) preliminary research (stakeholder consultations, systematic market reviews), (ii) selecting intervention components and developing the conceptual model, (iii) whiteboarding and prototype design, and (iv) testing and refinement. The Multiphase Optimization Strategy and the UK Medical Research Council framework for developing and evaluating complex interventions were used to guide the intervention development. Results Preliminary research highlighted the importance of targeting holistic wellbeing (i.e., both physical and mental health). Accordingly, the first version of LvL UP features a scalable, smartphone-based, and conversational agent-delivered holistic lifestyle intervention built around three pillars: Move More (physical activity), Eat Well (nutrition and healthy eating), and Stress Less (emotional regulation and wellbeing). Intervention components include health literacy and psychoeducational coaching sessions, daily "Life Hacks" (healthy activity suggestions), breathing exercises, and journaling. In addition to the intervention components, formative research also stressed the need to introduce engagement-specific components to maximise uptake and long-term use. LvL UP includes a motivational interviewing and storytelling approach to deliver the coaching sessions, as well as progress feedback and gamification. Offline materials are also offered to allow users access to essential intervention content without needing a mobile device. Conclusions The development process of LvL UP 1.0 led to an evidence-based and user-informed smartphone-based intervention aimed at preventing NCDs and CMDs. LvL UP is designed to be a scalable, engaging, prevention-oriented, holistic intervention for adults at risk of NCDs and CMDs. A feasibility study, and subsequent optimisation and randomised-controlled trials are planned to further refine the intervention and establish effectiveness. The development process described here may prove helpful to other intervention developers.
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Affiliation(s)
- Oscar Castro
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
| | - Jacqueline Louise Mair
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Alicia Salamanca-Sanabria
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
| | - Aishah Alattas
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
| | - Roman Keller
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Shenglin Zheng
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Ahmad Jabir
- Neuroscience and Mental Health, Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore, Singapore
| | - Xiaowen Lin
- Neuroscience and Mental Health, Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore, Singapore
| | - Bea Franziska Frese
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
- Centre for Digital Health Interventions,Institute of Technology Management, University of St. Gallen, St. Gallen, Switzerland
| | - Chang Siang Lim
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
| | - Prabhakaran Santhanam
- Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
| | - Rob M. van Dam
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
- Department of Exercise and Nutrition Sciences, Milken Institute School of Public Health, George Washington University, Washington DC, DC, United States
| | - Josip Car
- Centre for Population Health Sciences, LKCMedicine, Nanyang Technological University, Singapore, Singapore
- Department of Primary Care and Public Health, School of Public Health, Imperial College London, London, United Kingdom
| | - Jimmy Lee
- Neuroscience and Mental Health, Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore, Singapore
- Research Division, Institute of Mental Health, Singapore, Singapore
- North Region & Department of Psychosis, Institute of Mental Health, Singapore, Singapore
| | - E Shyong Tai
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Elgar Fleisch
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
- Centre for Digital Health Interventions,Institute of Technology Management, University of St. Gallen, St. Gallen, Switzerland
| | - Florian von Wangenheim
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
- Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
| | - Lorainne Tudor Car
- Neuroscience and Mental Health, Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore, Singapore
- Department of Primary Care and Public Health, School of Public Health, Imperial College London, London, United Kingdom
| | - Falk Müller-Riemenschneider
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
- Digital Health Center, Berlin Institute of Health, Charite University Medical Centre Berlin, Berlin, Germany
| | - Tobias Kowatsch
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
- Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
- School of Medicine, University of St. Gallen, St. Gallen, Switzerland
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6
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Barata F, Cleres D, Tinschert P, Iris Shih CH, Rassouli F, Boesch M, Brutsche M, Fleisch E. Nighttime Continuous Contactless Smartphone-Based Cough Monitoring for the Ward: Validation Study. JMIR Form Res 2023; 7:e38439. [PMID: 36655551 PMCID: PMC9989914 DOI: 10.2196/38439] [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: 04/14/2022] [Revised: 09/17/2022] [Accepted: 01/17/2023] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND Clinical deterioration can go unnoticed in hospital wards for hours. Mobile technologies such as wearables and smartphones enable automated, continuous, noninvasive ward monitoring and allow the detection of subtle changes in vital signs. Cough can be effectively monitored through mobile technologies in the ward, as it is not only a symptom of prevalent respiratory diseases such as asthma, lung cancer, and COVID-19 but also a predictor of acute health deterioration. In past decades, many efforts have been made to develop an automatic cough counting tool. To date, however, there is neither a standardized, sufficiently validated method nor a scalable cough monitor that can be deployed on a consumer-centric device that reports cough counts continuously. These shortcomings limit the tracking of coughing and, consequently, hinder the monitoring of disease progression in prevalent respiratory diseases such as asthma, chronic obstructive pulmonary disease, and COVID-19 in the ward. OBJECTIVE This exploratory study involved the validation of an automated smartphone-based monitoring system for continuous cough counting in 2 different modes in the ward. Unlike previous studies that focused on evaluating cough detection models on unseen data, the focus of this work is to validate a holistic smartphone-based cough detection system operating in near real time. METHODS Automated cough counts were measured consistently on devices and on computers and compared with cough and noncough sounds counted manually over 8-hour long nocturnal recordings in 9 patients with pneumonia in the ward. The proposed cough detection system consists primarily of an Android app running on a smartphone that detects coughs and records sounds and secondarily of a backend that continuously receives the cough detection information and displays the hourly cough counts. Cough detection is based on an ensemble convolutional neural network developed and trained on asthmatic cough data. RESULTS In this validation study, a total of 72 hours of recordings from 9 participants with pneumonia, 4 of whom were infected with SARS-CoV-2, were analyzed. All the recordings were subjected to manual analysis by 2 blinded raters. The proposed system yielded a sensitivity and specificity of 72% and 99% on the device and 82% and 99% on the computer, respectively, for detecting coughs. The mean differences between the automated and human rater cough counts were -1.0 (95% CI -12.3 to 10.2) and -0.9 (95% CI -6.5 to 4.8) coughs per hour within subject for the on-device and on-computer modes, respectively. CONCLUSIONS The proposed system thus represents a smartphone cough counter that can be used for continuous hourly assessment of cough frequency in the ward.
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Affiliation(s)
- Filipe Barata
- Center for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
| | - David Cleres
- Center for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
| | - Peter Tinschert
- Center for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland.,Resmonics AG, Zurich, Switzerland
| | - Chen-Hsuan Iris Shih
- Center for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland.,Resmonics AG, Zurich, Switzerland
| | - Frank Rassouli
- Lung Center, Cantonal Hospital St. Gallen, St. Gallen, Switzerland
| | | | - Martin Brutsche
- Lung Center, Cantonal Hospital St. Gallen, St. Gallen, Switzerland
| | - Elgar Fleisch
- Center for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland.,Center for Digital Health Interventions, Institute of Technology Management, University of St. Gallen, St. Gallen, Switzerland
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7
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Kuhn M, Nalbant E, Kohlbrenner D, Alge M, Kuett L, Arvaji A, Sievi NA, Russi EW, Clarenbach CF. Validation of a small cough detector. ERJ Open Res 2023; 9:00279-2022. [PMID: 36699651 PMCID: PMC9868968 DOI: 10.1183/23120541.00279-2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 09/23/2022] [Indexed: 01/28/2023] Open
Abstract
Research question The assessment of cough frequency in clinical practice relies predominantly on the patient's history. Currently, objective evaluation of cough is feasible with bulky equipment during a brief time (i.e. hours up to 1 day). Thus, monitoring of cough has been rarely performed outside clinical studies. We developed a small wearable cough detector (SIVA-P3) that uses deep neural networks for the automatic counting of coughs. This study examined the performance of the SIVA-P3 in an outpatient setting. Methods We recorded cough epochs with SIVA-P3 over eight consecutive days in patients suffering from chronic cough. During the first 24 h, the detector was validated against cough events counted by trained human listeners. The wearing comfort and the device usage were assessed using a questionnaire. Results In total, 27 participants (mean±sd age 50±14 years) with either chronic unexplained cough (n=12), COPD (n=4), asthma (n=5) or interstitial lung disease (n=6) were studied. During the daytime, the sensitivity of SIVA-P3 cough detection was 88.5±2.49% and the specificity was 99.97±0.01%. During the night-time, the sensitivity was 84.15±5.04% and the specificity was 99.97±0.02%. The wearing comfort and usage of the device was rated as very high by most participants. Conclusion SIVA-P3 enables automatic continuous cough monitoring in an outpatient setting for objective assessment of cough over days and weeks. It shows comparable sensitivity or higher sensitivity than other devices with fully automatic cough counting. Thanks to its wearing comfort and the high performance for cough detection, it has the potential for being used in routine clinical practice.
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Affiliation(s)
- Manuel Kuhn
- Faculty of Medicine, University of Zurich, Zurich, Switzerland,Department of Pulmonology, University Hospital Zurich, Zurich, Switzerland,Corresponding author: Manuel Kuhn ()
| | | | - Dario Kohlbrenner
- Department of Pulmonology, University Hospital Zurich, Zurich, Switzerland
| | | | | | - Alexandra Arvaji
- Department of Pulmonology, University Hospital Zurich, Zurich, Switzerland
| | - Noriane A. Sievi
- Department of Pulmonology, University Hospital Zurich, Zurich, Switzerland
| | - Erich W. Russi
- Faculty of Medicine, University of Zurich, Zurich, Switzerland
| | - Christian F. Clarenbach
- Faculty of Medicine, University of Zurich, Zurich, Switzerland,Department of Pulmonology, University Hospital Zurich, Zurich, Switzerland
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8
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Langholm C, Kowatsch T, Bucci S, Cipriani A, Torous J. Exploring the Potential of Apple SensorKit and Digital Phenotyping Data as New Digital Biomarkers for Mental Health Research. Digit Biomark 2023; 7:104-114. [PMID: 37901364 PMCID: PMC10601905 DOI: 10.1159/000530698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 03/27/2023] [Indexed: 10/31/2023] Open
Abstract
The use of digital phenotyping continues to expand across all fields of health. By collecting quantitative data in real-time using devices such as smartphones or smartwatches, researchers and clinicians can develop a profile of a wide range of conditions. Smartphones contain sensors that collect data, such as GPS or accelerometer data, which can inform secondary metrics such as time spent at home, location entropy, or even sleep duration. These metrics, when used as digital biomarkers, are not only used to investigate the relationship between behavior and health symptoms but can also be used to support personalized and preventative care. Successful phenotyping requires consistent long-term collection of relevant and high-quality data. In this paper, we present the potential of newly available, for approved research, opt-in SensorKit sensors on iOS devices in improving the accuracy of digital phenotyping. We collected opt-in sensor data over 1 week from a single person with depression using the open-source mindLAMP app developed by the Division of Digital Psychiatry at Beth Israel Deaconess Medical Center. Five sensors from SensorKit were included. The names of the sensors, as listed in official documentation, include the following: phone usage, messages usage, visits, device usage, and ambient light. We compared data from these five new sensors from SensorKit to our current digital phenotyping data collection sensors to assess similarity and differences in both raw and processed data. We present sample data from all five of these new sensors. We also present sample data from current digital phenotyping sources and compare these data to SensorKit sensors when applicable. SensorKit offers great potential for health research. Many SensorKit sensors improve upon previously accessible features and produce data that appears clinically relevant. SensorKit sensors will likely play a substantial role in digital phenotyping. However, using these data requires advanced health app infrastructure and the ability to securely store high-frequency data.
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Affiliation(s)
- Carsten Langholm
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Tobias Kowatsch
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
- School of Medicine, University of St. Gallen, St. Gallen, Switzerland
- Center for Digital Health Interventions, Department of Management, Technology, and Economics at ETH Zurich, Zurich, Switzerland
| | - Sandra Bucci
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Andrea Cipriani
- Department of Psychiatry, University of Manchester, Manchester, UK
- Oxford Precision Psychiatry Lab, NIHR Oxford Health Biomedical Research Centre, Oxford, UK
- Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, UK
| | - John Torous
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
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9
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Zhang M, Sykes DL, Brindle K, Sadofsky LR, Morice AH. Chronic cough-the limitation and advances in assessment techniques. J Thorac Dis 2022; 14:5097-5119. [PMID: 36647459 PMCID: PMC9840016 DOI: 10.21037/jtd-22-874] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 11/04/2022] [Indexed: 11/23/2022]
Abstract
Accurate and consistent assessments of cough are essential to advance the understanding of the mechanisms of cough and individualised the management of patients. Considerable progress has been made in this work. Here we reviewed the currently available tools for subjectively and objectively measuring both cough sensitivity and severity. We also provided some opinions on the new techniques and future directions. The simple and practical Visual Analogue Scale (VAS), the Leicester Cough Questionnaire (LCQ), and the Cough Specific Quality of Life Questionnaire (CQLQ) are the most widely used self-reported questionnaires for evaluating and quantifying cough severity. The Hull Airway Reflux Questionnaire (HARQ) is a tool to elucidate the constellation of symptoms underlying the diagnosis of chronic cough. Chemical excitation tests are widely used to explore the pathophysiological mechanisms of the cough reflex, such as capsaicin, citric acid and adenosine triphosphate (ATP) challenge test. Cough frequency is an ideal primary endpoint for clinical research, but the application of cough counters has been limited in clinical practice by the high cost and reliance on aural validation. The ongoing development of cough detection technology for smartphone apps and wearable devices will hopefully simplify cough counting, thus transitioning it from niche research to a widely available clinical application.
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Affiliation(s)
- Mengru Zhang
- Centre for Clinical Science, Respiratory Medicine, Hull York Medical School, University of Hull, Castle Hill Hospital, Cottingham, East Yorkshire, UK;,Department of Pulmonary and Critical Care Medicine, Tongji Hospital, Tongji University School of Medicine, Shanghai, China
| | - Dominic L. Sykes
- Centre for Clinical Science, Respiratory Medicine, Hull York Medical School, University of Hull, Castle Hill Hospital, Cottingham, East Yorkshire, UK
| | - Kayleigh Brindle
- Centre for Clinical Science, Respiratory Medicine, Hull York Medical School, University of Hull, Castle Hill Hospital, Cottingham, East Yorkshire, UK
| | - Laura R. Sadofsky
- Centre for Clinical Science, Respiratory Medicine, Hull York Medical School, University of Hull, Castle Hill Hospital, Cottingham, East Yorkshire, UK
| | - Alyn H. Morice
- Centre for Clinical Science, Respiratory Medicine, Hull York Medical School, University of Hull, Castle Hill Hospital, Cottingham, East Yorkshire, UK
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10
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Lee GT, Nam H, Kim SH, Choi SM, Kim Y, Park YH. Deep learning based cough detection camera using enhanced features. EXPERT SYSTEMS WITH APPLICATIONS 2022; 206:117811. [PMID: 35712056 PMCID: PMC9181707 DOI: 10.1016/j.eswa.2022.117811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 05/24/2022] [Accepted: 06/06/2022] [Indexed: 06/15/2023]
Abstract
Coughing is a typical symptom of COVID-19. To detect and localize coughing sounds remotely, a convolutional neural network (CNN) based deep learning model was developed in this work and integrated with a sound camera for the visualization of the cough sounds. The cough detection model is a binary classifier of which the input is a two second acoustic feature and the output is one of two inferences (Cough or Others). Data augmentation was performed on the collected audio files to alleviate class imbalance and reflect various background noises in practical environments. For effective featuring of the cough sound, conventional features such as spectrograms, mel-scaled spectrograms, and mel-frequency cepstral coefficients (MFCC) were reinforced by utilizing their velocity (V) and acceleration (A) maps in this work. VGGNet, GoogLeNet, and ResNet were simplified to binary classifiers, and were named V-net, G-net, and R-net, respectively. To find the best combination of features and networks, training was performed for a total of 39 cases and the performance was confirmed using the test F1 score. Finally, a test F1 score of 91.9% (test accuracy of 97.2%) was achieved from G-net with the MFCC-V-A feature (named Spectroflow), an acoustic feature effective for use in cough detection. The trained cough detection model was integrated with a sound camera (i.e., one that visualizes sound sources using a beamforming microphone array). In a pilot test, the cough detection camera detected coughing sounds with an F1 score of 90.0% (accuracy of 96.0%), and the cough location in the camera image was tracked in real time.
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Affiliation(s)
- Gyeong-Tae Lee
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, South Korea
| | - Hyeonuk Nam
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, South Korea
| | - Seong-Hu Kim
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, South Korea
| | - Sang-Min Choi
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, South Korea
| | | | - Yong-Hwa Park
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, South Korea
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11
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Holmes J, Heaney LG, McGarvey LPA. Objective and Subjective Measurement of Cough in Asthma: A Systematic Review of the Literature. Lung 2022; 200:169-178. [PMID: 35416544 PMCID: PMC9038879 DOI: 10.1007/s00408-022-00527-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 03/09/2022] [Indexed: 12/04/2022]
Abstract
Background The extent to which objective and subjective tools has been used to measure the characteristics and burden of cough in patients with asthma has not been reported. Objective To review the large and extensive body of literature in asthma with the specific hypothesis that the characteristics of cough and clinical impact in this disease has only occasionally been studied. Methods For this systematic review, we searched EMBASE and MEDLINE databases using a combination of MeSH terms for “cough” and “asthma” for studies published up to and including end of August 2021. Studies included for analysis were confined to those undertaken in adult patients (≥ 18 years) with asthma of any severity where any tool or method to specifically measure cough was employed. Results Of 12,090 citations identified after our initial search, 112 full-text articles met criteria for inclusion in our analysis. We found that a broad range of objective and subjective measures have been used albeit with a lack of consistency between studies. Clinically important levels of cough associated with impaired health status were identified in patients with asthma. Conclusion Although cough is a common symptom in asthma, the clinical features and accompanying healthcare burden have been studied infrequently. In studies where cough was measured, the methods employed varied considerably. A more consistent use of cough-specific measurement tools is required to better determine the nature and burden of cough in asthma.
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Affiliation(s)
- Joshua Holmes
- Wellcome-Wolfson Institute for Experimental Medicine, Belfast, UK
| | - Liam G Heaney
- Wellcome-Wolfson Institute for Experimental Medicine, Belfast, UK
| | - Lorcan P A McGarvey
- Wellcome-Wolfson Institute for Experimental Medicine, Belfast, UK. .,Wellcome-Wolfson Institute for Experimental Medicine, School of Medicine, Dentistry and Biomedical Sciences, Queen's University, Belfast, UK.
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12
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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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13
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Schlieter H, Marsch LA, Whitehouse D, Otto L, Londral AR, Teepe GW, Benedict M, Ollier J, Ulmer T, Gasser N, Ultsch S, Wollschlaeger B, Kowatsch T. Scale-up of Digital Innovations in Health Care: Expert Commentary on Enablers and Barriers. J Med Internet Res 2022; 24:e24582. [PMID: 35275065 PMCID: PMC8956989 DOI: 10.2196/24582] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 02/17/2021] [Accepted: 12/28/2021] [Indexed: 12/13/2022] Open
Abstract
Health care delivery is undergoing a rapid change from traditional processes toward the use of digital health interventions and personalized medicine. This movement has been accelerated by the COVID-19 crisis as a response to the need to guarantee access to health care services while reducing the risk of contagion. Digital health scale-up is now also vital to achieve population-wide impact: it will only accomplish sustainable effects if and when deployed into regular health care delivery services. The question of how sustainable digital health scale-up can be successfully achieved has, however, not yet been sufficiently resolved. This paper identifies and discusses enablers and barriers for scaling up digital health innovations. The results discussed in this paper were gathered by scientists and representatives of public bodies as well as patient organizations at an international workshop on scaling up digital health innovations. Results are explored in the context of prior research and implications for future work in achieving large-scale implementations that will benefit the population as a whole.
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Affiliation(s)
- Hannes Schlieter
- Research Group Digital Health, Faculty of Business and Economics, Technische Universität Dresden, Dresden, Germany
| | - Lisa A Marsch
- Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States
| | | | - Lena Otto
- Research Group Digital Health, Faculty of Business and Economics, Technische Universität Dresden, Dresden, Germany
| | - Ana Rita Londral
- Value for Health CoLAB, NOVA Medical School, Nova University of Lisbon, Lisbon, Portugal.,Comprehensive Health Research Center, NOVA Medical School, Nova University of Lisbon, Lisbon, Portugal
| | - Gisbert Wilhelm Teepe
- Centre for Digital Health Interventions, Department of Management, Technology and Economics, ETH Zurich, Zurich, Switzerland
| | - Martin Benedict
- Research Group Digital Health, Faculty of Business and Economics, Technische Universität Dresden, Dresden, Germany
| | - Joseph Ollier
- Centre for Digital Health Interventions, Department of Management, Technology and Economics, ETH Zurich, Zurich, Switzerland
| | - Tom Ulmer
- Institute of Information and Process Management, Eastern Switzerland University of Applied Sciences, St. Gallen, Switzerland
| | | | - Sabine Ultsch
- Centre for Digital Health Interventions, Institute of Technology Management, University of St. Gallen, St. Gallen, Switzerland
| | - Bastian Wollschlaeger
- Research Group Digital Health, Faculty of Business and Economics, Technische Universität Dresden, Dresden, Germany
| | - Tobias Kowatsch
- Centre for Digital Health Interventions, Department of Management, Technology and Economics, ETH Zurich, Zurich, Switzerland.,Centre for Digital Health Interventions, Institute of Technology Management, University of St. Gallen, St. Gallen, Switzerland
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14
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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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15
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Jokic S, Cleres D, Rassouli F, Steurer-Stey C, Puhan MA, Brutsche M, Fleisch E, Barata F. TripletCough: Cougher Identification and Verification from Contact-Free Smartphone-Based Audio Recordings Using Metric Learning. IEEE J Biomed Health Inform 2022; 26:2746-2757. [PMID: 35196248 DOI: 10.1109/jbhi.2022.3152944] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Cough, a symptom associated with many prevalent respiratory diseases, can serve as a potential biomarker for diagnosis and disease progression. Consequently, the development of cough monitoring systems and, in particular, automatic cough detection algorithms have been studied since the early 2000s. Recently, there has been an increased focus on the efficiency of such algorithms, as implementation on consumer-centric devices such as smartphones would provide a scalable and affordable solution for monitoring cough with contact-free sensors. Current algorithms, however, are incapable of discerning between coughs of different individuals and, thus, cannot function reliably in situations where potentially multiple individuals have to be monitored in shared environments. Therefore, we propose a weakly supervised metric learning approach for cougher recognition based on smartphone audio recordings of coughs. Our approach involves a triplet network architecture, which employs convolutional neural networks (CNNs). The CNNs of the triplet network learn an embedding function, which maps Mel spectrograms of cough recordings to an embedding space where they are more easily distinguishable. Using audio recordings of nocturnal coughs from asthmatic patients captured with a smartphone, our approach achieved a mean accuracy of 88% (10% SD) on two-way identification tests with 12 enrollment samples and accuracy of 80% and an equal error rate (EER) of 20% on verification tests. Furthermore, our approach outperformed human raters with regard to verification tests on average by 8% in accuracy, 4% in false acceptance rate (FAR), and 12% in false rejection rate (FRR). Our code and models are publicly available.
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16
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Zhang X, Pettinati M, Jalali A, Rajput KS, Selvaraj N. Novel COVID-19 Screening Using Cough Recordings of A Mobile Patient Monitoring System. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:2353-2357. [PMID: 34891755 DOI: 10.1109/embc46164.2021.9630722] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Since the COVID-19 pandemic began, research has shown promises in building COVID-19 screening tools using cough recordings as a convenient and inexpensive alternative to current testing techniques. In this paper, we present a novel and fully automated algorithm framework for cough extraction and COVID-19 detection using a combination of signal processing and machine learning techniques. It involves extracting cough episodes from audios of a diverse real-world noisy conditions and then screening for the COVID-19 infection based on the cough characteristics. The proposed algorithm was developed and evaluated using self-recorded cough audios collected from COVID-19 patients monitored by Biovitals® Sentinel remote patient management platform and publicly available datasets of various sound recordings. The proposed algorithm achieves a duration Area Under Receiver Operating Characteristic curve (AUROC) of 98.6% in the cough extraction task and a mean cross-validation AUROC of 98.1% in the COVID-19 classification task. These results demonstrate high accuracy and robustness of the proposed algorithm as a fast and easily accessible COVID-19 screening tool and its potential to be used for other cough analysis applications.
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17
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Abstract
Respiratory diseases are leading causes of death and disability in the world. The recent COVID-19 pandemic is also affecting the respiratory system. Detecting and diagnosing respiratory diseases requires both medical professionals and the clinical environment. Most of the techniques used up to date were also invasive or expensive. Some research groups are developing hardware devices and techniques to make possible a non-invasive or even remote respiratory sound acquisition. These sounds are then processed and analysed for clinical, scientific, or educational purposes. We present the literature review of non-invasive sound acquisition devices and techniques. The results are about a huge number of digital tools, like microphones, wearables, or Internet of Thing devices, that can be used in this scope. Some interesting applications have been found. Some devices make easier the sound acquisition in a clinic environment, but others make possible daily monitoring outside that ambient. We aim to use some of these devices and include the non-invasive recorded respiratory sounds in a Digital Twin system for personalized health.
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18
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Balamurali BT, Hee HI, Kapoor S, Teoh OH, Teng SS, Lee KP, Herremans D, Chen JM. Deep Neural Network-Based Respiratory Pathology Classification Using Cough Sounds. SENSORS (BASEL, SWITZERLAND) 2021; 21:5555. [PMID: 34450996 PMCID: PMC8402243 DOI: 10.3390/s21165555] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 08/05/2021] [Accepted: 08/09/2021] [Indexed: 11/24/2022]
Abstract
Intelligent systems are transforming the world, as well as our healthcare system. We propose a deep learning-based cough sound classification model that can distinguish between children with healthy versus pathological coughs such as asthma, upper respiratory tract infection (URTI), and lower respiratory tract infection (LRTI). To train a deep neural network model, we collected a new dataset of cough sounds, labelled with a clinician's diagnosis. The chosen model is a bidirectional long-short-term memory network (BiLSTM) based on Mel-Frequency Cepstral Coefficients (MFCCs) features. The resulting trained model when trained for classifying two classes of coughs-healthy or pathology (in general or belonging to a specific respiratory pathology)-reaches accuracy exceeding 84% when classifying the cough to the label provided by the physicians' diagnosis. To classify the subject's respiratory pathology condition, results of multiple cough epochs per subject were combined. The resulting prediction accuracy exceeds 91% for all three respiratory pathologies. However, when the model is trained to classify and discriminate among four classes of coughs, overall accuracy dropped: one class of pathological coughs is often misclassified as the other. However, if one considers the healthy cough classified as healthy and pathological cough classified to have some kind of pathology, then the overall accuracy of the four-class model is above 84%. A longitudinal study of MFCC feature space when comparing pathological and recovered coughs collected from the same subjects revealed the fact that pathological coughs, irrespective of the underlying conditions, occupy the same feature space making it harder to differentiate only using MFCC features.
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Affiliation(s)
- B T Balamurali
- Science, Mathematics and Technology, Singapore University of Technology and Design, Singapore 487372, Singapore; (S.K.); (J.M.C.)
| | - Hwan Ing Hee
- Department of Paediatric Anaesthesia, KK Women’s and Children’s Hospital, Singapore 229899, Singapore;
- Anaesthesiology and Perioperative Sciences, Duke-NUS Medical School, 8 College Road, Singapore 169857, Singapore
| | - Saumitra Kapoor
- Science, Mathematics and Technology, Singapore University of Technology and Design, Singapore 487372, Singapore; (S.K.); (J.M.C.)
| | - Oon Hoe Teoh
- Respiratory Medicine Service, Department of Paediatrics, KK Women’s and Children’s Hospital, Singapore 229899, Singapore;
| | - Sung Shin Teng
- Department of Emergency Medicine, KK Women’s and Children’s Hospital, Singapore 229899, Singapore; (S.S.T.); (K.P.L.)
| | - Khai Pin Lee
- Department of Emergency Medicine, KK Women’s and Children’s Hospital, Singapore 229899, Singapore; (S.S.T.); (K.P.L.)
| | - Dorien Herremans
- Information Systems, Technology, and Design, Singapore University of Technology and Design, Singapore 487372, Singapore;
| | - Jer Ming Chen
- Science, Mathematics and Technology, Singapore University of Technology and Design, Singapore 487372, Singapore; (S.K.); (J.M.C.)
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19
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Kowatsch T, Schachner T, Harperink S, Barata F, Dittler U, Xiao G, Stanger C, V Wangenheim F, Fleisch E, Oswald H, Möller A. Conversational Agents as Mediating Social Actors in Chronic Disease Management Involving Health Care Professionals, Patients, and Family Members: Multisite Single-Arm Feasibility Study. J Med Internet Res 2021; 23:e25060. [PMID: 33484114 PMCID: PMC7929753 DOI: 10.2196/25060] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 11/19/2020] [Accepted: 01/22/2021] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND Successful management of chronic diseases requires a trustful collaboration between health care professionals, patients, and family members. Scalable conversational agents, designed to assist health care professionals, may play a significant role in supporting this collaboration in a scalable way by reaching out to the everyday lives of patients and their family members. However, to date, it remains unclear whether conversational agents, in such a role, would be accepted and whether they can support this multistakeholder collaboration. OBJECTIVE With asthma in children representing a relevant target of chronic disease management, this study had the following objectives: (1) to describe the design of MAX, a conversational agent-delivered asthma intervention that supports health care professionals targeting child-parent teams in their everyday lives; and (2) to assess the (a) reach of MAX, (b) conversational agent-patient working alliance, (c) acceptance of MAX, (d) intervention completion rate, (e) cognitive and behavioral outcomes, and (f) human effort and responsiveness of health care professionals in primary and secondary care settings. METHODS MAX was designed to increase cognitive skills (ie, knowledge about asthma) and behavioral skills (ie, inhalation technique) in 10-15-year-olds with asthma, and enables support by a health professional and a family member. To this end, three design goals guided the development: (1) to build a conversational agent-patient working alliance; (2) to offer hybrid (human- and conversational agent-supported) ubiquitous coaching; and (3) to provide an intervention with high experiential value. An interdisciplinary team of computer scientists, asthma experts, and young patients with their parents developed the intervention collaboratively. The conversational agent communicates with health care professionals via email, with patients via a mobile chat app, and with a family member via SMS text messaging. A single-arm feasibility study in primary and secondary care settings was performed to assess MAX. RESULTS Results indicated an overall positive evaluation of MAX with respect to its reach (49.5%, 49/99 of recruited and eligible patient-family member teams participated), a strong patient-conversational agent working alliance, and high acceptance by all relevant stakeholders. Moreover, MAX led to improved cognitive and behavioral skills and an intervention completion rate of 75.5%. Family members supported the patients in 269 out of 275 (97.8%) coaching sessions. Most of the conversational turns (99.5%) were conducted between patients and the conversational agent as opposed to between patients and health care professionals, thus indicating the scalability of MAX. In addition, it took health care professionals less than 4 minutes to assess the inhalation technique and 3 days to deliver related feedback to the patients. Several suggestions for improvement were made. CONCLUSIONS This study provides the first evidence that conversational agents, designed as mediating social actors involving health care professionals, patients, and family members, are not only accepted in such a "team player" role but also show potential to improve health-relevant outcomes in chronic disease management.
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Affiliation(s)
- Tobias Kowatsch
- Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
- Future Health Technologies Programme, Campus for Research Excellence and Technological Enterprise, Singapore-ETH Centre, Singapore, Singapore
- Centre for Digital Health Interventions, Institute of Technology Management, University of St Gallen, St Gallen, Switzerland
| | - Theresa Schachner
- Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
| | - Samira Harperink
- Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
| | - Filipe Barata
- Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
| | - Ullrich Dittler
- Fakultät Digitale Medien, Campus Furtwangen, Hochschule Furtwangen University, Furtwangen, Germany
| | - Grace Xiao
- Johns Hopkins University School of Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Catherine Stanger
- Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Hanover, NH, United States
| | - Florian V Wangenheim
- Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
- Future Health Technologies Programme, Campus for Research Excellence and Technological Enterprise, Singapore-ETH Centre, Singapore, Singapore
| | - Elgar Fleisch
- Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
- Future Health Technologies Programme, Campus for Research Excellence and Technological Enterprise, Singapore-ETH Centre, Singapore, Singapore
- Centre for Digital Health Interventions, Institute of Technology Management, University of St Gallen, St Gallen, Switzerland
| | - Helmut Oswald
- Department of Child and Adolescent Health, Cantonal Hospital Winterthur, Winterthur, Switzerland
| | - Alexander Möller
- Division of Respiratory Medicine and Childhood Research Center, University Children's Hospital Zurich, Zurich, Switzerland
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20
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Tinschert P, Rassouli F, Barata F, Steurer-Stey C, Fleisch E, Puhan MA, Kowatsch T, Brutsche MH. Nocturnal Cough and Sleep Quality to Assess Asthma Control and Predict Attacks. J Asthma Allergy 2020; 13:669-678. [PMID: 33363391 PMCID: PMC7754262 DOI: 10.2147/jaa.s278155] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Accepted: 11/04/2020] [Indexed: 01/11/2023] Open
Abstract
Introduction Objective markers for asthma, that can be measured without extra patient effort, could mitigate current shortcomings in asthma monitoring. We investigated whether smartphone-recorded nocturnal cough and sleep quality can be utilized for the detection of periods with uncontrolled asthma or meaningful changes in asthma control and for the prediction of asthma attacks. Methods We analyzed questionnaire and sensor data of 79 adults with asthma. Data were collected in situ for 29 days by means of a smartphone. Sleep quality and nocturnal cough frequencies were measured every night with the Pittsburgh Sleep Quality Index and by manually annotating coughs from smartphone audio recordings. Primary endpoint was asthma control assessed with a weekly version of the Asthma Control Test. Secondary endpoint was self-reported asthma attacks. Results Mixed-effects regression analyses showed that nocturnal cough and sleep quality were statistically significantly associated with asthma control on a between- and within-patient level (p < 0.05). Decision trees indicated that sleep quality was more useful for detecting weeks with uncontrolled asthma (balanced accuracy (BAC) 68% vs 61%; Δ sensitivity −12%; Δ specificity −2%), while nocturnal cough better detected weeks with asthma control deteriorations (BAC 71% vs 56%; Δ sensitivity 3%; Δ specificity −34%). Cut-offs using both markers predicted asthma attacks up to five days ahead with BACs between 70% and 75% (sensitivities 75 - 88% and specificities 57 - 72%). Conclusion Nocturnal cough and sleep quality have useful properties as markers for asthma control and seem to have prognostic value for the early detection of asthma attacks. Due to the limited study duration per patient and the pragmatic nature of the study, future research is needed to comprehensively evaluate and externally validate the performance of both biomarkers and their utility for asthma self-management.
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Affiliation(s)
- Peter Tinschert
- Center for Digital Health Interventions, Institute of Technology Management, University of St. Gallen, St. Gallen, Switzerland
| | - Frank Rassouli
- Lung Center, Cantonal Hospital St. Gallen, St. Gallen, Switzerland
| | - Filipe Barata
- Center for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
| | - Claudia Steurer-Stey
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.,mediX Group Practice Zurich, Zurich, Switzerland
| | - Elgar Fleisch
- Center for Digital Health Interventions, Institute of Technology Management, University of St. Gallen, St. Gallen, Switzerland.,Center for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
| | - Milo Alan Puhan
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Tobias Kowatsch
- Center for Digital Health Interventions, Institute of Technology Management, University of St. Gallen, St. Gallen, Switzerland.,Center for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
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