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Gaeta AM, Quijada-López M, Barbé F, Vaca R, Pujol M, Minguez O, Sánchez-de-la-Torre M, Muñoz-Barrutia A, Piñol-Ripoll G. Predicting Alzheimer's disease CSF core biomarkers: a multimodal Machine Learning approach. Front Aging Neurosci 2024; 16:1369545. [PMID: 38988328 PMCID: PMC11233742 DOI: 10.3389/fnagi.2024.1369545] [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: 01/12/2024] [Accepted: 06/04/2024] [Indexed: 07/12/2024] Open
Abstract
Introduction Alzheimer's disease (AD) is a progressive neurodegenerative disorder. Current core cerebrospinal fluid (CSF) AD biomarkers, widely employed for diagnosis, require a lumbar puncture to be performed, making them impractical as screening tools. Considering the role of sleep disturbances in AD, recent research suggests quantitative sleep electroencephalography features as potential non-invasive biomarkers of AD pathology. However, quantitative analysis of comprehensive polysomnography (PSG) signals remains relatively understudied. PSG is a non-invasive test enabling qualitative and quantitative analysis of a wide range of parameters, offering additional insights alongside other biomarkers. Machine Learning (ML) gained interest for its ability to discern intricate patterns within complex datasets, offering promise in AD neuropathology detection. Therefore, this study aims to evaluate the effectiveness of a multimodal ML approach in predicting core AD CSF biomarkers. Methods Mild-moderate AD patients were prospectively recruited for PSG, followed by testing of CSF and blood samples for biomarkers. PSG signals underwent preprocessing to extract non-linear, time domain and frequency domain statistics quantitative features. Multiple ML algorithms were trained using four subsets of input features: clinical variables (CLINVAR), conventional PSG parameters (SLEEPVAR), quantitative PSG signal features (PSGVAR) and a combination of all subsets (ALL). Cross-validation techniques were employed to evaluate model performance and ensure generalizability. Regression models were developed to determine the most effective variable combinations for explaining variance in the biomarkers. Results On 49 subjects, Gradient Boosting Regressors achieved the best results in estimating biomarkers levels, using different loss functions for each biomarker: least absolute deviation (LAD) for the Aβ42, least squares (LS) for p-tau and Huber for t-tau. The ALL subset demonstrated the lowest training errors for all three biomarkers, albeit with varying test performance. Specifically, the SLEEPVAR subset yielded the best test performance in predicting Aβ42, while the ALL subset most accurately predicted p-tau and t-tau due to the lowest test errors. Conclusions Multimodal ML can help predict the outcome of CSF biomarkers in early AD by utilizing non-invasive and economically feasible variables. The integration of computational models into medical practice offers a promising tool for the screening of patients at risk of AD, potentially guiding clinical decisions.
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Affiliation(s)
- Anna Michela Gaeta
- Servicio de Neumología, Hospital Universitario Severo Ochoa, Leganés, Spain
| | - María Quijada-López
- Departamento de Bioingeniería, Universidad Carlos III de Madrid, Leganés, Spain
| | - Ferran Barbé
- Group of Translational Research in Respiratory Medicine, Hospital Universitari Arnau de Vilanova and Santa Maria, Institut de Recerca Biomedica de Lleida (IRBLleida), Lleida, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Madrid, Spain
| | - Rafaela Vaca
- Group of Translational Research in Respiratory Medicine, Hospital Universitari Arnau de Vilanova and Santa Maria, Institut de Recerca Biomedica de Lleida (IRBLleida), Lleida, Spain
| | - Montse Pujol
- Unitat Trastorns Cognitius, Clinical Neuroscience Research, Institut de Recerca Biomedica de Lleida (IRBLleida), Hospital Universitari Santa Maria, Lleida, Spain
| | - Olga Minguez
- Group of Translational Research in Respiratory Medicine, Hospital Universitari Arnau de Vilanova and Santa Maria, Institut de Recerca Biomedica de Lleida (IRBLleida), Lleida, Spain
| | - Manuel Sánchez-de-la-Torre
- Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Madrid, Spain
- Group of Precision Medicine in Chronic Diseases, Hospital Nacional de Parapléjicos, IDISCAM, Department of Nursing, Physiotherapy and Occupational Therapy, Faculty of Physiotherapy and Nursing, University of Castilla-La Mancha, Toledo, Spain
| | - Arrate Muñoz-Barrutia
- Departamento de Bioingeniería, Universidad Carlos III de Madrid, Leganés, Spain
- Departamento de Bioingegneria, Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain
| | - Gerard Piñol-Ripoll
- Unitat Trastorns Cognitius, Clinical Neuroscience Research, Institut de Recerca Biomedica de Lleida (IRBLleida), Hospital Universitari Santa Maria, Lleida, Spain
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Holm B, Jouan G, Hardarson E, Sigurðardottir S, Hoelke K, Murphy C, Arnardóttir ES, Óskarsdóttir M, Islind AS. An optimized framework for processing multicentric polysomnographic data incorporating expert human oversight. Front Neuroinform 2024; 18:1379932. [PMID: 38803523 PMCID: PMC11128565 DOI: 10.3389/fninf.2024.1379932] [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: 01/31/2024] [Accepted: 04/29/2024] [Indexed: 05/29/2024] Open
Abstract
Introduction Polysomnographic recordings are essential for diagnosing many sleep disorders, yet their detailed analysis presents considerable challenges. With the rise of machine learning methodologies, researchers have created various algorithms to automatically score and extract clinically relevant features from polysomnography, but less research has been devoted to how exactly the algorithms should be incorporated into the workflow of sleep technologists. This paper presents a sophisticated data collection platform developed under the Sleep Revolution project, to harness polysomnographic data from multiple European centers. Methods A tripartite platform is presented: a user-friendly web platform for uploading three-night polysomnographic recordings, a dedicated splitter that segments these into individual one-night recordings, and an advanced processor that enhances the one-night polysomnography with contemporary automatic scoring algorithms. The platform is evaluated using real-life data and human scorers, whereby scoring time, accuracy, and trust are quantified. Additionally, the scorers were interviewed about their trust in the platform, along with the impact of its integration into their workflow. Results We found that incorporating AI into the workflow of sleep technologists both decreased the time to score by up to 65 min and increased the agreement between technologists by as much as 0.17 κ. Discussion We conclude that while the inclusion of AI into the workflow of sleep technologists can have a positive impact in terms of speed and agreement, there is a need for trust in the algorithms.
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Affiliation(s)
- Benedikt Holm
- Department of Computer Science, Reykjavik University, Reykjavik, Iceland
- School of Technology, Reykjavik University Sleep Institute, Reykjavik, Iceland
| | - Gabriel Jouan
- Department of Computer Science, Reykjavik University, Reykjavik, Iceland
- School of Technology, Reykjavik University Sleep Institute, Reykjavik, Iceland
| | - Emil Hardarson
- Department of Computer Science, Reykjavik University, Reykjavik, Iceland
- School of Technology, Reykjavik University Sleep Institute, Reykjavik, Iceland
| | | | - Kenan Hoelke
- School of Technology, Reykjavik University Sleep Institute, Reykjavik, Iceland
- Board of Registered Polysomnographic Technologists, Arlington, VA, United States
| | - Conor Murphy
- School of Technology, Reykjavik University Sleep Institute, Reykjavik, Iceland
- Physical Activity, Physical Education, Sport and Health Research Centre (PAPESH), Sports Science Department, School of Social Sciences, Reykjavik University, Reykjavik, Iceland
| | - Erna Sif Arnardóttir
- Department of Computer Science, Reykjavik University, Reykjavik, Iceland
- School of Technology, Reykjavik University Sleep Institute, Reykjavik, Iceland
| | - María Óskarsdóttir
- Department of Computer Science, Reykjavik University, Reykjavik, Iceland
- School of Technology, Reykjavik University Sleep Institute, Reykjavik, Iceland
| | - Anna Sigríður Islind
- Department of Computer Science, Reykjavik University, Reykjavik, Iceland
- School of Technology, Reykjavik University Sleep Institute, Reykjavik, Iceland
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Yun R, Rembado I, Perlmutter SI, Rao RPN, Fetz EE. Local field potentials and single unit dynamics in motor cortex of unconstrained macaques during different behavioral states. Front Neurosci 2023; 17:1273627. [PMID: 38075283 PMCID: PMC10702227 DOI: 10.3389/fnins.2023.1273627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Accepted: 11/09/2023] [Indexed: 02/12/2024] Open
Abstract
Different sleep stages have been shown to be vital for a variety of brain functions, including learning, memory, and skill consolidation. However, our understanding of neural dynamics during sleep and the role of prominent LFP frequency bands remain incomplete. To elucidate such dynamics and differences between behavioral states we collected multichannel LFP and spike data in primary motor cortex of unconstrained macaques for up to 24 h using a head-fixed brain-computer interface (Neurochip3). Each 8-s bin of time was classified into awake-moving (Move), awake-resting (Rest), REM sleep (REM), or non-REM sleep (NREM) by using dimensionality reduction and clustering on the average spectral density and the acceleration of the head. LFP power showed high delta during NREM, high theta during REM, and high beta when the animal was awake. Cross-frequency phase-amplitude coupling typically showed higher coupling during NREM between all pairs of frequency bands. Two notable exceptions were high delta-high gamma and theta-high gamma coupling during Move, and high theta-beta coupling during REM. Single units showed decreased firing rate during NREM, though with increased short ISIs compared to other states. Spike-LFP synchrony showed high delta synchrony during Move, and higher coupling with all other frequency bands during NREM. These results altogether reveal potential roles and functions of different LFP bands that have previously been unexplored.
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Affiliation(s)
- Richy Yun
- Department of Bioengineering, University of Washington, Seattle, WA, United States
- Center for Neurotechnology, University of Washington, Seattle, WA, United States
- Washington National Primate Research Center, University of Washington, Seattle, WA, United States
| | - Irene Rembado
- Washington National Primate Research Center, University of Washington, Seattle, WA, United States
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, United States
| | - Steve I. Perlmutter
- Center for Neurotechnology, University of Washington, Seattle, WA, United States
- Washington National Primate Research Center, University of Washington, Seattle, WA, United States
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, United States
| | - Rajesh P. N. Rao
- Center for Neurotechnology, University of Washington, Seattle, WA, United States
- Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, United States
| | - Eberhard E. Fetz
- Department of Bioengineering, University of Washington, Seattle, WA, United States
- Center for Neurotechnology, University of Washington, Seattle, WA, United States
- Washington National Primate Research Center, University of Washington, Seattle, WA, United States
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, United States
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Han K, Rezapour R, Nakamura K, Devkota D, Miller DC, Diesner J. An
expert‐in‐the‐loop
method for
domain‐specific
document categorization based on small training data. J Assoc Inf Sci Technol 2022. [DOI: 10.1002/asi.24714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Kanyao Han
- School of Information Sciences University of Illinois at Urbana‐Champaign Champaign Illinois USA
| | - Rezvaneh Rezapour
- College of Computing and Informatics Drexel University Philadelphia Pennsylvania USA
| | - Katia Nakamura
- Department of Natural Resources and Environmental Science University of Illinois at Urbana‐Champaign Champaign Illinois USA
- Keough School of Global Affairs University of Notre Dame South Bend Indiana USA
| | - Dikshya Devkota
- Department of Natural Resources and Environmental Science University of Illinois at Urbana‐Champaign Champaign Illinois USA
| | - Daniel C. Miller
- Department of Natural Resources and Environmental Science University of Illinois at Urbana‐Champaign Champaign Illinois USA
- Keough School of Global Affairs University of Notre Dame South Bend Indiana USA
| | - Jana Diesner
- School of Information Sciences University of Illinois at Urbana‐Champaign Champaign Illinois USA
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Adversarial learning for semi-supervised pediatric sleep staging with single-EEG channel. Methods 2022; 204:84-91. [DOI: 10.1016/j.ymeth.2022.03.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 03/12/2022] [Accepted: 03/21/2022] [Indexed: 11/18/2022] Open
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Artificial Intelligence and Women Researchers in the Czech Republic. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12031465] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Artificial intelligence as a research area has been continuously growing for several decades. Many applications were developed in various domains. Medicine and health care have attracted more intensive attention thanks to rapid technological development that has accelerated generation of large volumes of data requiring intelligent analysis and evaluation. This article illustrates, through examples of women researchers and selected AI projects in medicine, the wide spectrum of applications developed during the last fifteen years in the Czech Republic, and in particular at the Czech Technical University in Prague. Women researchers played an important and irreplaceable role since the advent of AI research in the Czech Republic. By their example, they motivated many young female students to join the community and start their research career in the AI area. They frequently participated in research projects led by the senior women researchers. The presented overview of projects illustrates the diversity of the medical area and the potential of AI methods that can be used for solving data- and knowledge-intensive problems. We briefly touch on the AI study programs. In conclusion, we point out the future challenges in AI and its applications in medicine and health care.
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Dell KL, Payne DE, Kremen V, Maturana MI, Gerla V, Nejedly P, Worrell GA, Lenka L, Mivalt F, Boston RC, Brinkmann BH, D'Souza W, Burkitt AN, Grayden DB, Kuhlmann L, Freestone DR, Cook MJ. Seizure likelihood varies with day-to-day variations in sleep duration in patients with refractory focal epilepsy: A longitudinal electroencephalography investigation. EClinicalMedicine 2021; 37:100934. [PMID: 34386736 PMCID: PMC8343264 DOI: 10.1016/j.eclinm.2021.100934] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 05/03/2021] [Accepted: 05/13/2021] [Indexed: 01/16/2023] Open
Abstract
BACKGROUND While the effects of prolonged sleep deprivation (≥24 h) on seizure occurrence has been thoroughly explored, little is known about the effects of day-to-day variations in the duration and quality of sleep on seizure probability. A better understanding of the interaction between sleep and seizures may help to improve seizure management. METHODS To explore how sleep and epileptic seizures are associated, we analysed continuous intracranial electroencephalography (EEG) recordings collected from 10 patients with refractory focal epilepsy undergoing ordinary life activities between 2010 and 2012 from three clinical centres (Austin Health, The Royal Melbourne Hospital, and St Vincent's Hospital of the Melbourne University Epilepsy Group). A total of 4340 days of sleep-wake data were analysed (average 434 days per patient). EEG data were sleep scored using a semi-automated machine learning approach into wake, stages one, two, and three non-rapid eye movement sleep, and rapid eye movement sleep categories. FINDINGS Seizure probability changes with day-to-day variations in sleep duration. Logistic regression models revealed that an increase in sleep duration, by 1·66 ± 0·52 h, lowered the odds of seizure by 27% in the following 48 h. Following a seizure, patients slept for longer durations and if a seizure occurred during sleep, then sleep quality was also reduced with increased time spent aroused from sleep and reduced rapid eye movement sleep. INTERPRETATION Our results suggest that day-to-day deviations from regular sleep duration correlates with changes in seizure probability. Sleeping longer, by 1·66 ± 0·52 h, may offer protective effects for patients with refractory focal epilepsy, reducing seizure risk. Furthermore, the occurrence of a seizure may disrupt sleep patterns by elongating sleep and, if the seizure occurs during sleep, reducing its quality.
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Affiliation(s)
- Katrina L. Dell
- Department of Medicine, St. Vincent's Hospital, University of Melbourne, Level 4, 29 Regent Street, Fitzroy, Victoria 3065, Australia
- Corresponding author.
| | - Daniel E. Payne
- Department of Medicine, St. Vincent's Hospital, University of Melbourne, Level 4, 29 Regent Street, Fitzroy, Victoria 3065, Australia
- Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia
| | - Vaclav Kremen
- Department of Neurology, Mayo Clinic, Rochester, United States
- Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University in Prague, Prague, Czech Republic
| | - Matias I. Maturana
- Department of Medicine, St. Vincent's Hospital, University of Melbourne, Level 4, 29 Regent Street, Fitzroy, Victoria 3065, Australia
- Seer Medical, Melbourne, Victoria, Australia
| | - Vaclav Gerla
- Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University in Prague, Prague, Czech Republic
| | - Petr Nejedly
- Department of Neurology, Mayo Clinic, Rochester, United States
| | | | - Lhotska Lenka
- Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University in Prague, Prague, Czech Republic
| | - Filip Mivalt
- Department of Neurology, Mayo Clinic, Rochester, United States
| | - Raymond C. Boston
- Department of Medicine, St. Vincent's Hospital, University of Melbourne, Level 4, 29 Regent Street, Fitzroy, Victoria 3065, Australia
- Department of Clinical Studies - NBC, University of Pennsylvania, School of Veterinary Medicine, Kennett Square, PA, United States
| | | | - Wendyl D'Souza
- Department of Medicine, St. Vincent's Hospital, University of Melbourne, Level 4, 29 Regent Street, Fitzroy, Victoria 3065, Australia
| | - Anthony N. Burkitt
- Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia
| | - David B. Grayden
- Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia
| | - Levin Kuhlmann
- Department of Medicine, St. Vincent's Hospital, University of Melbourne, Level 4, 29 Regent Street, Fitzroy, Victoria 3065, Australia
- Department of Data Science and AI, Faculty of Information and Technology, Monash University, Clayton, Victoria, Australia
| | | | - Mark J. Cook
- Department of Medicine, St. Vincent's Hospital, University of Melbourne, Level 4, 29 Regent Street, Fitzroy, Victoria 3065, Australia
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Payne DE, Dell KL, Karoly PJ, Kremen V, Gerla V, Kuhlmann L, Worrell GA, Cook MJ, Grayden DB, Freestone DR. Identifying seizure risk factors: A comparison of sleep, weather, and temporal features using a Bayesian forecast. Epilepsia 2020; 62:371-382. [PMID: 33377501 DOI: 10.1111/epi.16785] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Revised: 11/15/2020] [Accepted: 11/16/2020] [Indexed: 11/30/2022]
Abstract
OBJECTIVE Most seizure forecasting algorithms have relied on features specific to electroencephalographic recordings. Environmental and physiological factors, such as weather and sleep, have long been suspected to affect brain activity and seizure occurrence but have not been fully explored as prior information for seizure forecasts in a patient-specific analysis. The study aimed to quantify whether sleep, weather, and temporal factors (time of day, day of week, and lunar phase) can provide predictive prior probabilities that may be used to improve seizure forecasts. METHODS This study performed post hoc analysis on data from eight patients with a total of 12.2 years of continuous intracranial electroencephalographic recordings (average = 1.5 years, range = 1.0-2.1 years), originally collected in a prospective trial. Patients also had sleep scoring and location-specific weather data. Histograms of future seizure likelihood were generated for each feature. The predictive utility of individual features was measured using a Bayesian approach to combine different features into an overall forecast of seizure likelihood. Performance of different feature combinations was compared using the area under the receiver operating curve. Performance evaluation was pseudoprospective. RESULTS For the eight patients studied, seizures could be predicted above chance accuracy using sleep (five patients), weather (two patients), and temporal features (six patients). Forecasts using combined features performed significantly better than chance in six patients. For four of these patients, combined forecasts outperformed any individual feature. SIGNIFICANCE Environmental and physiological data, including sleep, weather, and temporal features, provide significant predictive information on upcoming seizures. Although forecasts did not perform as well as algorithms that use invasive intracranial electroencephalography, the results were significantly above chance. Complementary signal features derived from an individual's historic seizure records may provide useful prior information to augment traditional seizure detection or forecasting algorithms. Importantly, many predictive features used in this study can be measured noninvasively.
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Affiliation(s)
- Daniel E Payne
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia.,Department of Medicine, St Vincent's Hospital, The University of Melbourne, Melbourne, Victoria, Australia
| | - Katrina L Dell
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Melbourne, Victoria, Australia
| | - Phillipa J Karoly
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia.,Graeme Clark Institute, The University of Melbourne, Melbourne, Victoria, Australia
| | - Vaclav Kremen
- Department of Neurology, Mayo Clinic, Rochester, MN, USA.,Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University in Prague, Prague, Czech Republic
| | - Vaclav Gerla
- Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University in Prague, Prague, Czech Republic
| | - Levin Kuhlmann
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Melbourne, Victoria, Australia.,Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Victoria, Australia
| | | | - Mark J Cook
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Melbourne, Victoria, Australia.,Graeme Clark Institute, The University of Melbourne, Melbourne, Victoria, Australia
| | - David B Grayden
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia.,Department of Medicine, St Vincent's Hospital, The University of Melbourne, Melbourne, Victoria, Australia
| | - Dean R Freestone
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Melbourne, Victoria, Australia
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