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Artificial intelligence for quality control of oscillometry measures. Comput Biol Med 2021; 138:104871. [PMID: 34560503 DOI: 10.1016/j.compbiomed.2021.104871] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 09/10/2021] [Accepted: 09/11/2021] [Indexed: 11/24/2022]
Abstract
BACKGROUND The forced oscillation technique (FOT) allows non-invasive lung function testing during quiet breathing even without expert guidance. However, it still relies on an operator for excluding breaths with artefacts such as swallowing, glottis closure and coughing. This manual selection is operator-dependent and time-consuming. We evaluated supervised machine learning methods to exclude breaths with artefacts from data analysis automatically. METHODS We collected 932 FOT measurements (Resmon Pro Full, Restech) from 155 patients (6-87 years) following the European Respiratory Society (ERS) technical standards. Patients were randomly assigned to either a training (70%) or test set. For each breath, we computed 71 features (including anthropometric, pressure stimulus, breathing pattern, and oscillometry data). Univariate filter, multivariate filter and wrapper methods for feature selection combined with several classification models were considered. RESULTS Trained operators identified 4333 breaths with- and 10244 without artefacts. Features selection performed by a wrapper method combined with an AdaBoost tree model provided the best performance metrics on the test set: Balanced Accuracy = 85%; Sensitivity = 79%; Specificity = 91%; AUC-ROC = 0.93. Differences in FOT parameters computed after manual or automatic breath selection was less than ∼0.25 cmH2O*s/L for 95% of cases. CONCLUSION Supervised machine-learning techniques allow reliable artefact detection in FOT diagnostic tests. Automating this process is fundamental for enabling FOT for home monitoring, telemedicine, and point-of-care diagnostic applications and opens new scenarios for respiratory and community medicine.
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2
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Radović N, Prelević V, Erceg M, Antunović T. Machine learning approach in mortality rate prediction for hemodialysis patients. Comput Methods Biomech Biomed Engin 2021; 25:111-122. [PMID: 34124977 DOI: 10.1080/10255842.2021.1937611] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Kernel support vector machine algorithm and K-means clustering algorithm are used to determine the expected mortality rate for hemodialysis patients. The national nephrology database of Montenegro has been used to conduct this research. Mortality rate prediction is realized with accuracy up to 94.12% and up to 96.77%, when a complete database is observed and when a reduced database (that contains data for the three most common basic diseases) is observed, respectively. Additionally, it is shown that just a few parameters, most of which are collected during the sole patient examination, are enough for satisfying results.
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Affiliation(s)
- Nevena Radović
- Electrical Engineering Department, University of Montenegro, Podgorica, Montenegro
| | - Vladimir Prelević
- Clinic for Nephrology, Clinical Center of Montenegro, Podgorica, Montenegro
| | - Milena Erceg
- Electrical Engineering Department, University of Montenegro, Podgorica, Montenegro
| | - Tanja Antunović
- Center for Laboratory Diagnostics, Clinical Center of Montenegro, Podgorica, Montenegro
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King GG, Bates J, Berger KI, Calverley P, de Melo PL, Dellacà RL, Farré R, Hall GL, Ioan I, Irvin CG, Kaczka DW, Kaminsky DA, Kurosawa H, Lombardi E, Maksym GN, Marchal F, Oppenheimer BW, Simpson SJ, Thamrin C, van den Berge M, Oostveen E. Technical standards for respiratory oscillometry. Eur Respir J 2020; 55:13993003.00753-2019. [PMID: 31772002 DOI: 10.1183/13993003.00753-2019] [Citation(s) in RCA: 284] [Impact Index Per Article: 71.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2019] [Accepted: 10/15/2019] [Indexed: 12/11/2022]
Abstract
Oscillometry (also known as the forced oscillation technique) measures the mechanical properties of the respiratory system (upper and intrathoracic airways, lung tissue and chest wall) during quiet tidal breathing, by the application of an oscillating pressure signal (input or forcing signal), most commonly at the mouth. With increased clinical and research use, it is critical that all technical details of the hardware design, signal processing and analyses, and testing protocols are transparent and clearly reported to allow standardisation, comparison and replication of clinical and research studies. Because of this need, an update of the 2003 European Respiratory Society (ERS) technical standards document was produced by an ERS task force of experts who are active in clinical oscillometry research.The aim of the task force was to provide technical recommendations regarding oscillometry measurement including hardware, software, testing protocols and quality control.The main changes in this update, compared with the 2003 ERS task force document are 1) new quality control procedures which reflect use of "within-breath" analysis, and methods of handling artefacts; 2) recommendation to disclose signal processing, quality control, artefact handling and breathing protocols (e.g. number and duration of acquisitions) in reports and publications to allow comparability and replication between devices and laboratories; 3) a summary review of new data to support threshold values for bronchodilator and bronchial challenge tests; and 4) updated list of predicted impedance values in adults and children.
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Affiliation(s)
- Gregory G King
- Dept of Respiratory Medicine and Airway Physiology and Imaging Group, Royal North Shore Hospital and The Woolcock Institute of Medical Research, The University of Sydney, Sydney, Australia
| | - Jason Bates
- Dept of Medicine, Pulmonary/Critical Care Division, University of Vermont, Larner College of Medicine, Burlington, VT, USA
| | - Kenneth I Berger
- Division of Pulmonary, Critical Care, and Sleep Medicine, NYU School of Medicine and André Cournand Pulmonary Physiology Laboratory, Belleuve Hospital, New York, NY, USA
| | - Peter Calverley
- Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK
| | - Pedro L de Melo
- Institute of Biology and Faculty of Engineering, Department of Physiology, Biomedical Instrumentation Laboratory, State University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Raffaele L Dellacà
- Dipartimento di Elettronica, Informazione e Bioingegneria - DEIB, Politecnico di Milano University, Milano, Italy
| | - Ramon Farré
- Unitat de Biofísica i Bioenginyeria, Facultat de Medicina, Universitat de Barcelona-IDIBAPS, Barcelona, Spain.,CIBER de Enfermedades Respiratorias, Madrid, Spain
| | - Graham L Hall
- Children's Lung Health, Telethon Kids Institute, School of Physiotherapy and Exercise Science, Curtin University, Perth, Australia
| | - Iulia Ioan
- Dept of Pediatric Lung Function Testing, Children's Hospital, Vandoeuvre-lès-Nancy, France.,EA 3450 DevAH - Laboratory of Physiology, Faculty of Medicine, University of Lorraine, Vandoeuvre-lès-Nancy, France
| | - Charles G Irvin
- Dept of Medicine, Pulmonary/Critical Care Division, University of Vermont, Larner College of Medicine, Burlington, VT, USA
| | - David W Kaczka
- Depts of Anesthesia, Biomedical Engineering and Radiology, University of Iowa, Iowa City, IA, USA
| | - David A Kaminsky
- Dept of Medicine, Pulmonary/Critical Care Division, University of Vermont, Larner College of Medicine, Burlington, VT, USA
| | - Hajime Kurosawa
- Dept of Occupational Health, Tohoku University School of Medicine, Sendai, Japan
| | - Enrico Lombardi
- Pediatric Pulmonary Unit, Meyer Pediatric University Hospital, Florence, Italy
| | - Geoffrey N Maksym
- School of Biomedical Engineering, Dalhousie University, Halifax, NS, Canada
| | - François Marchal
- Dept of Pediatric Lung Function Testing, Children's Hospital, Vandoeuvre-lès-Nancy, France.,EA 3450 DevAH - Laboratory of Physiology, Faculty of Medicine, University of Lorraine, Vandoeuvre-lès-Nancy, France
| | - Beno W Oppenheimer
- Division of Pulmonary, Critical Care, and Sleep Medicine, NYU School of Medicine and André Cournand Pulmonary Physiology Laboratory, Belleuve Hospital, New York, NY, USA
| | - Shannon J Simpson
- Children's Lung Health, Telethon Kids Institute, School of Physiotherapy and Exercise Science, Curtin University, Perth, Australia
| | - Cindy Thamrin
- Dept of Respiratory Medicine and Airway Physiology and Imaging Group, Royal North Shore Hospital and The Woolcock Institute of Medical Research, The University of Sydney, Sydney, Australia
| | - Maarten van den Berge
- University of Groningen, University Medical Center Groningen, Dept of Pulmonary Diseases, Groningen, The Netherlands
| | - Ellie Oostveen
- Dept of Respiratory Medicine, Antwerp University Hospital and University of Antwerp, Antwerp, Belgium
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4
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Naik GR, Gargiulo GD, Serrador JM, Breen PP. Groundtruth: A Matlab GUI for Artifact and Feature Identification in Physiological Signals. Front Physiol 2019; 10:850. [PMID: 31481893 PMCID: PMC6710362 DOI: 10.3389/fphys.2019.00850] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Accepted: 06/20/2019] [Indexed: 12/03/2022] Open
Abstract
Groundtruth is a Matlab Graphical User Interface (GUI) developed for the identification of key features and artifacts within physiological signals. The ultimate aim of this GUI is to provide a simple means of assessing the performance of new sensors. Secondary, to this is providing a means of providing marked data, enabling assessment of automated artifact rejection and feature identification algorithms. With the emergence of new wearable sensor technologies, there is an unmet need for convenient assessment of device performance, and a faster means of assessing new algorithms. The proposed GUI allows interactive marking of artifact regions as well as simultaneous interactive identification of key features, e.g., respiration peaks in respiration signals, R-peaks in Electrocardiography signals, etc. In this paper, we present the base structure of the system, together with an example of its use for two simultaneously worn respiration sensors. The respiration rates are computed for both original as well as artifact removed data and validated using Bland–Altman plots. The respiration rates computed based on the proposed GUI (after artifact removal process) demonstrated consistent results for two respiration sensors after artifact removal process. Groundtruth is customizable, and alternative processing modules are easy to add/remove. Groundtruth is intended for open-source use.
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Affiliation(s)
- Ganesh R Naik
- Biomedical Engineering and Neuromorphic Systems, The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Penrith, NSW, Australia
| | - Gaetano D Gargiulo
- Biomedical Engineering and Neuromorphic Systems, The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Penrith, NSW, Australia
| | - Jorge M Serrador
- Rutgers Biomedical and Health Sciences, Newark, NJ, United States.,Department of Pharmacology, Physiology & Neuroscience, New Jersey Medical School, Rutgers University, Newark, NJ, United States
| | - Paul P Breen
- Biomedical Engineering and Neuromorphic Systems, The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Penrith, NSW, Australia.,Translational Health Research Institute, Western Sydney University, Penrith, NSW, Australia
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Lonini L, Dai A, Shawen N, Simuni T, Poon C, Shimanovich L, Daeschler M, Ghaffari R, Rogers JA, Jayaraman A. Wearable sensors for Parkinson's disease: which data are worth collecting for training symptom detection models. NPJ Digit Med 2018; 1:64. [PMID: 31304341 PMCID: PMC6550186 DOI: 10.1038/s41746-018-0071-z] [Citation(s) in RCA: 75] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Accepted: 11/02/2018] [Indexed: 11/24/2022] Open
Abstract
Machine learning algorithms that use data streams captured from soft wearable sensors have the potential to automatically detect PD symptoms and inform clinicians about the progression of disease. However, these algorithms must be trained with annotated data from clinical experts who can recognize symptoms, and collecting such data are costly. Understanding how many sensors and how much labeled data are required is key to successfully deploying these models outside of the clinic. Here we recorded movement data using 6 flexible wearable sensors in 20 individuals with PD over the course of multiple clinical assessments conducted on 1 day and repeated 2 weeks later. Participants performed 13 common tasks, such as walking or typing, and a clinician rated the severity of symptoms (bradykinesia and tremor). We then trained convolutional neural networks and statistical ensembles to detect whether a segment of movement showed signs of bradykinesia or tremor based on data from tasks performed by other individuals. Our results show that a single wearable sensor on the back of the hand is sufficient for detecting bradykinesia and tremor in the upper extremities, whereas using sensors on both sides does not improve performance. Increasing the amount of training data by adding other individuals can lead to improved performance, but repeating assessments with the same individuals—even at different medication states—does not substantially improve detection across days. Our results suggest that PD symptoms can be detected during a variety of activities and are best modeled by a dataset incorporating many individuals.
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Affiliation(s)
- Luca Lonini
- Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL 60611 USA.,2Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL 60611 USA
| | - Andrew Dai
- Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL 60611 USA.,3Department of Biomedical Engineering, Northwestern University, Evanston, IL 60208 USA
| | - Nicholas Shawen
- Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL 60611 USA.,4Medical Scientist Training Program, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611 USA
| | - Tanya Simuni
- 5Department of Neurology, Northwestern University, Chicago, IL 60611 USA
| | - Cynthia Poon
- 5Department of Neurology, Northwestern University, Chicago, IL 60611 USA
| | - Leo Shimanovich
- 5Department of Neurology, Northwestern University, Chicago, IL 60611 USA
| | - Margaret Daeschler
- 6The Michael J. Fox Foundation for Parkinson's Research, New York, NY 10163 USA
| | - Roozbeh Ghaffari
- 7Center for Bio-Integrated Electronics, Departments of Materials Science and Engineering, Biomedical Engineering, Chemistry, Mechanical Engineering, Electrical Engineering and Computer Science, Neurological Surgery, Simpson Querrey Institute for Nano/Biotechnology, McCormick School of Engineering, Feinberg School of Medicine, Northwestern University, Evanston, IL 60208 USA
| | - John A Rogers
- 7Center for Bio-Integrated Electronics, Departments of Materials Science and Engineering, Biomedical Engineering, Chemistry, Mechanical Engineering, Electrical Engineering and Computer Science, Neurological Surgery, Simpson Querrey Institute for Nano/Biotechnology, McCormick School of Engineering, Feinberg School of Medicine, Northwestern University, Evanston, IL 60208 USA.,8Frederick Seitz Materials Research Laboratory, Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801 USA
| | - Arun Jayaraman
- Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL 60611 USA.,2Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL 60611 USA.,9Department of Physical Therapy and Human Movement Sciences, Northwestern University, Chicago, IL 60611 USA
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